Introduction

Parametric survival models are often the preferred method of extrapolating survival data for use in economic models. The National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) technical support document (TSD) 14 recommends that the Exponential, Weibull, Gompertz, log-logistic, log normal and Generalized Gamma parametric models should all be considered.[1] More recently, NICE also discusses more flexible models in NICE DSU TSD 21, however, more these models are not in the scope of this package.[2] The Canadian Agency for Drugs and Technologies in Health (CADTH) additionally specifies that the Gamma distribution must also be considered. This document therefore details the characteristics of each of these distributions and demonstrates how the parameters from each distribution, outputted using the flexsurvPlus package, can be implemented within an economic model.[3] The Generalized F distribution is not commonly used, however it has been included in this package in case it is required.

The flexsurvPlus package allows the inclusion of a treatment effect in the following four ways:

  • Separate models - Models fitted to each treatment arm separately

  • Independent shape models - Models fitted to both/all treatment arms including a treatment covariate to model the effect of treatment on both the scale and shape parameter(s) of the distribution

  • Common shape models - Models fitted to both/all treatment arms including a treatment covariate to model the effect of treatment on the scale parameter of the distribution. The shape parameter(s) of the distribution is common across treatments which reflects an assumption of proportional hazards or accelerated failure time between treatments depending on the distribution

  • One arm models - Models fitted to the entire dataset (no treatment strata)

This document details how to use the flexsurvPlus package to perform these models. A separate vignette; “Parametric survival analysis using the flexsurvPlus package: understanding the theory” details the theory behind the models.

Set up packages and data

Install packages

The following packages are required to run this example:

# Libraries
library(flexsurvPlus)
library(tibble)
library(dplyr)
library(boot)
library(ggplot2)

# Non-scientific notation
options(scipen=999) 

# Colours for plots
blue = rgb(69, 185, 209, max=255)
pink = rgb(211,78,147,max=255)
Dyellow = rgb(214, 200, 16, max=255)
orange<-rgb(247,139,21,max=255)

Read in the data

To perform survival analyses, patient level data is required for the survival endpoints.

This example uses a standard simulated data set (adtte). There is no standard naming that is needed for this package however, there are some set variables that are needed:

  • Time - a numeric variable
  • Event - a binary variable (event=1, censor=0)
  • Treatment - a character variable with the name of the intervention treatment

The data must be in “wide” format such that there is one row per patient and columns for each endpoint separately. In this example, we analyze progression-free survival (PFS).

adtte <- sim_adtte(seed = 2020, rho = 0.6)
head(adtte)
#>   USUBJID ARMCD             ARM PARAMCD                     PARAM AVAL AVALU
#> 1       1     A Reference Arm A     PFS Progression Free Survival  141  DAYS
#> 2       2     A Reference Arm A     PFS Progression Free Survival  173  DAYS
#> 3       3     A Reference Arm A     PFS Progression Free Survival  197  DAYS
#> 4       4     A Reference Arm A     PFS Progression Free Survival  133  DAYS
#> 5       5     A Reference Arm A     PFS Progression Free Survival  100  DAYS
#> 6       6     A Reference Arm A     PFS Progression Free Survival  525  DAYS
#>   CNSR
#> 1    0
#> 2    0
#> 3    0
#> 4    0
#> 5    0
#> 6    0

# subset PFS data and rename
PFS_data <- adtte %>%
  filter(PARAMCD=="PFS") %>%
  transmute(USUBJID,
            ARMCD,
            PFS_days = AVAL,
            PFS_event = 1- CNSR
  )
head(PFS_data)
#>   USUBJID ARMCD PFS_days PFS_event
#> 1       1     A      141         1
#> 2       2     A      173         1
#> 3       3     A      197         1
#> 4       4     A      133         1
#> 5       5     A      100         1
#> 6       6     A      525         1

Exploratory analysis

Before performing any statistical analysis, it is important to explore the data. Most importantly is a Kaplan-Meier plot. A simple Kaplan-Meier plot has been produced here:


# Create survfit object
km.est.PFS <- survfit(Surv(PFS_days, PFS_event) ~ ARMCD, 
                      data = PFS_data, 
                      conf.type = 'plain')

# Plot Kaplan-Meier
plot(km.est.PFS, 
     col = c(blue, pink), # plot colours
     lty = c(1:2), # line type
     xlab = "Time (Days)", # x-axis label
     ylab = "Progression-free survival", # y-axis label
     xlim = c(0, 800)) 

legend(x = 500, 
       y = .9, 
       legend = c("Arm A", "Arm B"), 
       lty = c(1:2), 
       col = c(blue, pink))

Fitting the models

The runPSM function fits parametric survival models for multiple distributions using the flexsurv package, manipulates the flexsurv objects to get the parameter estimates and AIC and BIC value (using the flexsurvPlus function get_params) and rearranges the parameter estimates such that they can easily be output to excel to calculate survival for both the intervention and reference treatment in an economic model.

These functions can be used to estimate 3 types of model:

  • Separate models - Models fitted to each treatment arm separately
    • These models are fitted using the runPSM function using the model.type=“Separate” argument.
  • Independent shape models - Models fitted to both/all treatment arms including a treatment covariate to model the effect of treatment on both the scale and shape parameter(s) of the distribution.
    • They have been fit using the runPSM function using the model.type=“Independent shape” argument.
  • Common shape models - Models fitted to both/all treatment arms including a treatment covariate to model the effect of treatment on the scale parameter of the distribution. The shape parameter(s) of the distribution is common across treatments which reflects an assumption of proportional hazards or accelerated failure time between treatments depending on the distribution
    • These models have been fit using the runPSM function using the model.type=“Common shape” argument.
  • One arm models - Models fitted to the entire dataset (no treatment strata)
    • These models are fitted using the runPSM function using the model.type=“One arm” argument.

The inputs to the runPSM function are:

  • data - A data frame containing individual patient data for the relevant time to event outcomes

  • time_var - Name of time variable in ‘data’. Variable must be numerical and >0.

  • event_var - Name of event variable in ‘data’. Variable must be numerical and contain 1’s to indicate an event and 0 to indicate a censor.

  • model.type - Character vector indicating the types of model formula provided. Permitted values are ‘Common shape’, ‘Independent shape’ or ‘Separate’ as per the models explained above.

  • distr - Each type of model can be fitted with multiple distributions. The distributions available for this package are:

    • Exponential (‘exp’)

    • Weibull (‘weibull’)

    • Gompertz (‘gompertz’)

    • Log-normal (‘lnorm’)

    • Log-logistic (‘llogis’)

    • Generalized gamma (‘gengamma’)

    • Gamma (‘gamma’)

    • Generalized F (‘genf’)

  • strata_var - Name of stratification variable in “data”. This is usually the treatment variable and must be categorical. Not required when model.type=‘One arm’.

  • int_name - Character to indicate the name of the treatment of interest, must be a level of the “strata_var” column in “data”, used for labelling the parameters.

  • ref_name - Character to indicate the name of the reference treatment, must be a level of the “strata_var” column in “data”, used for labelling the parameters. Not required when model.type=‘One arm’.

More information about each function can be used by running the code ?runPSM.

Example code to fit all two arm models is presented below.

psm_PFS_all <- runPSM(data=PFS_data,
                     time_var="PFS_days",
                     event_var="PFS_event",
                     model.type= c("Common shape", 
                                   "Independent shape", 
                                   "Separate"),
                     distr = c('exp',
                               'weibull',
                               'gompertz',
                               'lnorm',
                               'llogis',
                               'gengamma',
                               'gamma',
                               'genf'),
                     strata_var = "ARMCD",
                     int_name="A",
                     ref_name = "B")
psm_PFS_all
#> $models
#> $models$comshp.exp
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est       L95%      U95%      se        exp(est)  L95%    
#> rate          NA   0.002210  0.001911  0.002557  0.000164        NA        NA
#> ARMInt  0.500000   0.767591  0.574293  0.960889  0.098623  2.154570  1.775875
#>         U95%    
#> rate          NA
#> ARMInt  2.614019
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2798.33, df = 2
#> AIC = 5600.66
#> 
#> 
#> $models$comshp.weibull
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est       L95%      U95%      se        exp(est)  L95%    
#> shape         NA     1.3881    1.2830    1.5017    0.0557        NA        NA
#> scale         NA   437.0600  393.4819  485.4643   23.4223        NA        NA
#> ARMInt    0.5000    -0.6623   -0.8035   -0.5211    0.0721    0.5157    0.4477
#>         U95%    
#> shape         NA
#> scale         NA
#> ARMInt    0.5939
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2769.611, df = 3
#> AIC = 5545.222
#> 
#> 
#> $models$comshp.gompertz
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est       L95%      U95%      se        exp(est)  L95%    
#> shape         NA   0.002072  0.001455  0.002688  0.000315        NA        NA
#> rate          NA   0.001358  0.001091  0.001690  0.000152        NA        NA
#> ARMInt  0.500000   0.911755  0.712472  1.111039  0.101677  2.488687  2.039025
#>         U95%    
#> shape         NA
#> rate          NA
#> ARMInt  3.037512
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2779.001, df = 3
#> AIC = 5564.003
#> 
#> 
#> $models$comshp.lnorm
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>          data mean  est      L95%     U95%     se       exp(est)  L95%   
#> meanlog       NA     5.7350   5.6068   5.8632   0.0654       NA        NA
#> sdlog         NA     0.9863   0.9199   1.0575   0.0351       NA        NA
#> ARMInt    0.5000    -0.7128  -0.8901  -0.5355   0.0905   0.4903    0.4106
#>          U95%   
#> meanlog       NA
#> sdlog         NA
#> ARMInt    0.5854
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2800.225, df = 3
#> AIC = 5606.451
#> 
#> 
#> $models$comshp.llogis
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est       L95%      U95%      se        exp(est)  L95%    
#> shape         NA     1.9062    1.7573    2.0676    0.0791        NA        NA
#> scale         NA   323.1928  288.2447  362.3782   18.8707        NA        NA
#> ARMInt    0.5000    -0.6975   -0.8565   -0.5384    0.0812    0.4978    0.4246
#>         U95%    
#> shape         NA
#> scale         NA
#> ARMInt    0.5837
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2780.256, df = 3
#> AIC = 5566.512
#> 
#> 
#> $models$comshp.gengamma
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est      L95%     U95%     se       exp(est)  L95%   
#> mu           NA     6.0574   5.9291   6.1857   0.0654       NA        NA
#> sigma        NA     0.7377   0.6624   0.8215   0.0405       NA        NA
#> Q            NA     0.9195   0.6682   1.1708   0.1282       NA        NA
#> ARMInt   0.5000    -0.6699  -0.8149  -0.5249   0.0740   0.5118    0.4427
#>         U95%   
#> mu           NA
#> sigma        NA
#> Q            NA
#> ARMInt   0.5916
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2769.421, df = 4
#> AIC = 5546.841
#> 
#> 
#> $models$comshp.gamma
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est       L95%      U95%      se        exp(est)  L95%    
#> shape         NA   1.658113  1.465091  1.876566  0.104702        NA        NA
#> rate          NA   0.004046  0.003377  0.004846  0.000373        NA        NA
#> ARMInt  0.500000   0.684825  0.537124  0.832527  0.075359  1.983426  1.711078
#>         U95%    
#> shape         NA
#> rate          NA
#> ARMInt  2.299122
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2770.102, df = 3
#> AIC = 5546.203
#> 
#> 
#> $models$comshp.genf
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est      L95%     U95%     se       exp(est)  L95%   
#> mu           NA     6.0061   5.8600   6.1523   0.0746       NA        NA
#> sigma        NA     0.6487   0.5128   0.8206   0.0778       NA        NA
#> Q            NA     0.7342   0.3803   1.0881   0.1806       NA        NA
#> P            NA     0.9507   0.1978   4.5692   0.7615       NA        NA
#> ARMInt   0.5000    -0.6866  -0.8313  -0.5420   0.0738   0.5033    0.4355
#>         U95%   
#> mu           NA
#> sigma        NA
#> Q            NA
#> P            NA
#> ARMInt   0.5816
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2768.32, df = 5
#> AIC = 5546.64
#> 
#> 
#> $models$sep.exp.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>       est       L95%      U95%      se      
#> rate  0.004762  0.004194  0.005408  0.000309
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1510.588, df = 1
#> AIC = 3023.176
#> 
#> 
#> $models$sep.weibull.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est       L95%      U95%      se      
#> shape    1.3841    1.2516    1.5306    0.0711
#> scale  225.2128  204.7892  247.6732   10.9235
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1493.471, df = 2
#> AIC = 2990.943
#> 
#> 
#> $models$sep.gompertz.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est       L95%      U95%      se      
#> shape  0.002126  0.001252  0.003000  0.000446
#> rate   0.003345  0.002719  0.004116  0.000354
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1500.61, df = 2
#> AIC = 3005.22
#> 
#> 
#> $models$sep.lnorm.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>          est     L95%    U95%    se    
#> meanlog  5.0201  4.9049  5.1352  0.0587
#> sdlog    0.9245  0.8442  1.0124  0.0428
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1508.722, df = 2
#> AIC = 3021.443
#> 
#> 
#> $models$sep.llogis.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est     L95%    U95%    se    
#> shape    2.02    1.81    2.24    0.11
#> scale  161.22  145.05  179.19    8.69
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1499.366, df = 2
#> AIC = 3002.733
#> 
#> 
#> $models$sep.gengamma.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est     L95%    U95%    se    
#> mu     5.3572  5.2058  5.5086  0.0772
#> sigma  0.7496  0.6635  0.8469  0.0467
#> Q      0.8349  0.5197  1.1501  0.1608
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1492.975, df = 3
#> AIC = 2991.95
#> 
#> 
#> $models$sep.gamma.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est       L95%      U95%      se      
#> shape  1.708525  1.447974  2.015960  0.144238
#> rate   0.008273  0.006806  0.010057  0.000824
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1493.076, df = 2
#> AIC = 2990.152
#> 
#> 
#> $models$sep.genf.int
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est     L95%    U95%    se    
#> mu     5.3121  5.1465  5.4777  0.0845
#> sigma  0.6579  0.4870  0.8888  0.1010
#> Q      0.7012  0.3001  1.1022  0.2046
#> P      0.8038  0.0983  6.5742  0.8619
#> 
#> N = 250,  Events: 238,  Censored: 12
#> Total time at risk: 49975
#> Log-likelihood = -1492.383, df = 4
#> AIC = 2992.766
#> 
#> 
#> $models$sep.exp.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>       est       L95%      U95%      se      
#> rate  0.002210  0.001911  0.002557  0.000164
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1287.742, df = 1
#> AIC = 2577.485
#> 
#> 
#> $models$sep.weibull.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est       L95%      U95%      se      
#> shape    1.3944    1.2290    1.5821    0.0898
#> scale  436.9567  393.5292  485.1766   23.3372
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1276.135, df = 2
#> AIC = 2556.271
#> 
#> 
#> $models$sep.gompertz.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est       L95%      U95%      se      
#> shape  0.002018  0.001149  0.002887  0.000443
#> rate   0.001377  0.001050  0.001807  0.000191
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1278.377, df = 2
#> AIC = 2560.755
#> 
#> 
#> $models$sep.lnorm.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>          est     L95%    U95%    se    
#> meanlog  5.7541  5.6135  5.8946  0.0717
#> sdlog    1.0684  0.9587  1.1907  0.0591
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1289.469, df = 2
#> AIC = 2582.939
#> 
#> 
#> $models$sep.llogis.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est      L95%     U95%     se     
#> shape    1.775    1.566    2.012    0.113
#> scale  324.711  287.502  366.736   20.163
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1279.735, df = 2
#> AIC = 2563.469
#> 
#> 
#> $models$sep.gengamma.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est     L95%    U95%    se    
#> mu     6.1015  5.9352  6.2679  0.0849
#> sigma  0.6938  0.5462  0.8814  0.0847
#> Q      1.0810  0.5839  1.5782  0.2537
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1276.081, df = 3
#> AIC = 2558.162
#> 
#> 
#> $models$sep.gamma.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est       L95%      U95%      se      
#> shape  1.596692  1.325325  1.923622  0.151750
#> rate   0.003872  0.003033  0.004943  0.000482
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1276.884, df = 2
#> AIC = 2557.768
#> 
#> 
#> $models$sep.genf.ref
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>        est     L95%    U95%    se    
#> mu      5.965   5.654   6.275   0.158
#> sigma   0.611   0.379   0.985   0.149
#> Q       0.601  -0.364   1.566   0.492
#> P       1.767   0.136  22.976   2.313
#> 
#> N = 250,  Events: 181,  Censored: 69
#> Total time at risk: 81887
#> Log-likelihood = -1275.713, df = 4
#> AIC = 2559.427
#> 
#> 
#> $models$indshp.exp
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>         data mean  est       L95%      U95%      se        exp(est)  L95%    
#> rate          NA   0.002210  0.001911  0.002557  0.000164        NA        NA
#> ARMInt  0.500000   0.767591  0.574293  0.960889  0.098623  2.154570  1.775875
#>         U95%    
#> rate          NA
#> ARMInt  2.614019
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2798.33, df = 2
#> AIC = 5600.66
#> 
#> 
#> $models$indshp.weibull
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est        L95%       U95%       se         exp(est) 
#> shape                 NA    1.39439    1.22897    1.58208    0.08984         NA
#> scale                 NA  436.95667  393.52916  485.17658   23.33723         NA
#> ARMInt           0.50000   -0.66279   -0.80419   -0.52138    0.07215    0.51541
#> shape(ARMInt)    0.50000   -0.00742   -0.16889    0.15406    0.08239    0.99261
#>                L95%       U95%     
#> shape                 NA         NA
#> scale                 NA         NA
#> ARMInt           0.44745    0.59370
#> shape(ARMInt)    0.84460    1.16656
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2769.607, df = 4
#> AIC = 5547.214
#> 
#> 
#> $models$indshp.gompertz
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est        L95%       U95%       se         exp(est) 
#> shape                 NA   0.002018   0.001194   0.002841   0.000420         NA
#> rate                  NA   0.001377   0.001050   0.001807   0.000191         NA
#> ARMInt          0.500000   0.887593   0.545992   1.229194   0.174289   2.429276
#> shape(ARMInt)   0.500000   0.000108  -0.001115   0.001332   0.000624   1.000108
#>                L95%       U95%     
#> shape                 NA         NA
#> rate                  NA         NA
#> ARMInt          1.726320   3.418474
#> shape(ARMInt)   0.998886   1.001332
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2778.987, df = 4
#> AIC = 5565.974
#> 
#> 
#> $models$indshp.llogis
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est       L95%      U95%      se        exp(est)
#> shape                NA     1.7752    1.5662    2.0121    0.1134        NA
#> scale                NA   324.7128  287.5038  366.7375   20.1633        NA
#> ARMInt           0.5000    -0.7002   -0.8614   -0.5390    0.0822    0.4965
#> shape(ARMInt)    0.5000     0.1272   -0.0375    0.2919    0.0840    1.1357
#>                L95%      U95%    
#> shape                NA        NA
#> scale                NA        NA
#> ARMInt           0.4226    0.5833
#> shape(ARMInt)    0.9632    1.3390
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2779.101, df = 4
#> AIC = 5566.202
#> 
#> 
#> $models$indshp.gamma
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est        L95%       U95%       se         exp(est) 
#> shape                 NA   1.596555   1.325203   1.923470   0.151743         NA
#> rate                  NA   0.003871   0.003032   0.004942   0.000482         NA
#> ARMInt          0.500000   0.759334   0.446665   1.072003   0.159528   2.136853
#> shape(ARMInt)   0.500000   0.067700  -0.181462   0.316863   0.127126   1.070045
#>                L95%       U95%     
#> shape                 NA         NA
#> rate                  NA         NA
#> ARMInt          1.563091   2.921226
#> shape(ARMInt)   0.834050   1.372814
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2769.96, df = 4
#> AIC = 5547.919
#> 
#> 
#> $models$indshp.lnorm
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est       L95%      U95%      se        exp(est)
#> meanlog              NA    5.75408   5.61352   5.89464   0.07172        NA
#> sdlog                NA    1.06842   0.95866   1.19074   0.05909        NA
#> ARMInt          0.50000   -0.73401  -0.91570  -0.55232   0.09270   0.47998
#> sdlog(ARMInt)   0.50000   -0.14466  -0.28608  -0.00323   0.07216   0.86532
#>                L95%      U95%    
#> meanlog              NA        NA
#> sdlog                NA        NA
#> ARMInt          0.40024   0.57561
#> sdlog(ARMInt)   0.75120   0.99678
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2798.191, df = 4
#> AIC = 5604.382
#> 
#> 
#> $models$indshp.gengamma
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est      L95%     U95%     se       exp(est)  L95%   
#> mu                  NA     6.1016   5.9352   6.2680   0.0849       NA        NA
#> sigma               NA     0.6938   0.5461   0.8814   0.0847       NA        NA
#> Q                   NA     1.0814   0.5840   1.5788   0.2538       NA        NA
#> ARMInt          0.5000    -0.7444  -0.9693  -0.5194   0.1148   0.4750    0.3794
#> sigma(ARMInt)   0.5000     0.0773  -0.1913   0.3460   0.1371   1.0804    0.8258
#> Q(ARMInt)       0.5000    -0.2465  -0.8353   0.3423   0.3004   0.7816    0.4338
#>                U95%   
#> mu                  NA
#> sigma               NA
#> Q                   NA
#> ARMInt          0.5949
#> sigma(ARMInt)   1.4134
#> Q(ARMInt)       1.4082
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2769.056, df = 6
#> AIC = 5550.112
#> 
#> 
#> $models$indshp.genf
#> Call:
#> flexsurv::flexsurvreg(formula = model.formula, data = data, dist = dist)
#> 
#> Estimates: 
#>                data mean  est      L95%     U95%     se       exp(est)  L95%   
#> mu                  NA     5.9638   5.6539   6.2738   0.1582       NA        NA
#> sigma               NA     0.6104   0.3781   0.9853   0.1491       NA        NA
#> Q                   NA     0.5991  -0.3641   1.5623   0.4914       NA        NA
#> P                   NA     1.7770   0.1382  22.8488   2.3156       NA        NA
#> ARMInt          0.5000    -0.6517  -1.0031  -0.3002   0.1793   0.5212    0.3667
#> sigma(ARMInt)   0.5000     0.0753  -0.4902   0.6409   0.2886   1.0782    0.6125
#> Q(ARMInt)       0.5000     0.1022  -0.9411   1.1456   0.5323   1.1076    0.3902
#> P(ARMInt)       0.5000    -0.7957  -4.1067   2.5153   1.6893   0.4513    0.0165
#>                U95%   
#> mu                  NA
#> sigma               NA
#> Q                   NA
#> P                   NA
#> ARMInt          0.7407
#> sigma(ARMInt)   1.8982
#> Q(ARMInt)       3.1443
#> P(ARMInt)      12.3708
#> 
#> N = 500,  Events: 419,  Censored: 81
#> Total time at risk: 131862
#> Log-likelihood = -2768.096, df = 8
#> AIC = 5552.193
#> 
#> 
#> 
#> $model_summary
#>         flexsurvfit                   Model                  ModelF
#> 1        comshp.exp            Common shape            Common shape
#> 2    comshp.weibull            Common shape            Common shape
#> 3   comshp.gompertz            Common shape            Common shape
#> 4      comshp.lnorm            Common shape            Common shape
#> 5     comshp.llogis            Common shape            Common shape
#> 6   comshp.gengamma            Common shape            Common shape
#> 7      comshp.gamma            Common shape            Common shape
#> 8       comshp.genf            Common shape            Common shape
#> 9       sep.exp.int Separate - Intervention Separate - Intervention
#> 10  sep.weibull.int Separate - Intervention Separate - Intervention
#> 11 sep.gompertz.int Separate - Intervention Separate - Intervention
#> 12    sep.lnorm.int Separate - Intervention Separate - Intervention
#> 13   sep.llogis.int Separate - Intervention Separate - Intervention
#> 14 sep.gengamma.int Separate - Intervention Separate - Intervention
#> 15    sep.gamma.int Separate - Intervention Separate - Intervention
#> 16     sep.genf.int Separate - Intervention Separate - Intervention
#> 17      sep.exp.ref    Separate - Reference    Separate - Reference
#> 18  sep.weibull.ref    Separate - Reference    Separate - Reference
#> 19 sep.gompertz.ref    Separate - Reference    Separate - Reference
#> 20    sep.lnorm.ref    Separate - Reference    Separate - Reference
#> 21   sep.llogis.ref    Separate - Reference    Separate - Reference
#> 22 sep.gengamma.ref    Separate - Reference    Separate - Reference
#> 23    sep.gamma.ref    Separate - Reference    Separate - Reference
#> 24     sep.genf.ref    Separate - Reference    Separate - Reference
#> 25       indshp.exp       Independent shape       Independent shape
#> 26   indshp.weibull       Independent shape       Independent shape
#> 27  indshp.gompertz       Independent shape       Independent shape
#> 28    indshp.llogis       Independent shape       Independent shape
#> 29     indshp.gamma       Independent shape       Independent shape
#> 30     indshp.lnorm       Independent shape       Independent shape
#> 31  indshp.gengamma       Independent shape       Independent shape
#> 32      indshp.genf       Independent shape       Independent shape
#>                 Dist             DistF    distr Intervention_name
#> 1        Exponential       Exponential      exp                 A
#> 2            Weibull           Weibull  weibull                 A
#> 3           Gompertz          Gompertz gompertz                 A
#> 4         Log Normal        Log Normal    lnorm                 A
#> 5       Log Logistic      Log Logistic   llogis                 A
#> 6  Generalized Gamma Generalized Gamma gengamma                 A
#> 7              Gamma             Gamma    gamma                 A
#> 8      Generalized F     Generalized F     genf                 A
#> 9        Exponential       Exponential      exp                 A
#> 10           Weibull           Weibull  weibull                 A
#> 11          Gompertz          Gompertz gompertz                 A
#> 12        Log Normal        Log Normal    lnorm                 A
#> 13      Log Logistic      Log Logistic   llogis                 A
#> 14 Generalized Gamma Generalized Gamma gengamma                 A
#> 15             Gamma             Gamma    gamma                 A
#> 16     Generalized F     Generalized F     genf                 A
#> 17       Exponential       Exponential      exp                 A
#> 18           Weibull           Weibull  weibull                 A
#> 19          Gompertz          Gompertz gompertz                 A
#> 20        Log Normal        Log Normal    lnorm                 A
#> 21      Log Logistic      Log Logistic   llogis                 A
#> 22 Generalized Gamma Generalized Gamma gengamma                 A
#> 23             Gamma             Gamma    gamma                 A
#> 24     Generalized F     Generalized F     genf                 A
#> 25       Exponential       Exponential      exp                 A
#> 26           Weibull           Weibull  weibull                 A
#> 27          Gompertz          Gompertz gompertz                 A
#> 28      Log Logistic      Log Logistic   llogis                 A
#> 29             Gamma             Gamma    gamma                 A
#> 30        Log Normal        Log Normal    lnorm                 A
#> 31 Generalized Gamma Generalized Gamma gengamma                 A
#> 32     Generalized F     Generalized F     genf                 A
#>    Reference_name    Status      AIC      BIC
#> 1               B Converged 5600.660 5609.089
#> 2               B Converged 5545.222 5557.866
#> 3               B Converged 5564.003 5576.647
#> 4               B Converged 5606.451 5619.094
#> 5               B Converged 5566.512 5579.156
#> 6               B Converged 5546.841 5563.699
#> 7               B Converged 5546.203 5558.847
#> 8               B Converged 5546.640 5567.713
#> 9               B Converged 3023.176 3026.697
#> 10              B Converged 2990.943 2997.986
#> 11              B Converged 3005.220 3012.263
#> 12              B Converged 3021.443 3028.486
#> 13              B Converged 3002.733 3009.776
#> 14              B Converged 2991.950 3002.514
#> 15              B Converged 2990.152 2997.195
#> 16              B Converged 2992.766 3006.852
#> 17              B Converged 2577.485 2581.006
#> 18              B Converged 2556.271 2563.314
#> 19              B Converged 2560.755 2567.798
#> 20              B Converged 2582.939 2589.982
#> 21              B Converged 2563.469 2570.512
#> 22              B Converged 2558.162 2568.727
#> 23              B Converged 2557.768 2564.810
#> 24              B Converged 2559.427 2573.512
#> 25              B Converged 5600.660 5609.089
#> 26              B Converged 5547.214 5564.072
#> 27              B Converged 5565.974 5582.833
#> 28              B Converged 5566.202 5583.060
#> 29              B Converged 5547.919 5564.778
#> 30              B Converged 5604.382 5621.241
#> 31              B Converged 5550.112 5575.400
#> 32              B Converged 5552.193 5585.910
#> 
#> $parameters_vector
#>       comshp.exp.rate.int       comshp.exp.rate.ref        comshp.exp.rate.TE 
#>              0.0047623812              0.0022103631              0.7675910101 
#>  comshp.weibull.scale.int  comshp.weibull.scale.ref  comshp.weibull.shape.int 
#>            225.3719486376            437.0599651237              1.3880696669 
#>  comshp.weibull.shape.ref   comshp.weibull.scale.TE  comshp.gompertz.rate.int 
#>              1.3880696669             -0.6623182633              0.0033789924 
#>  comshp.gompertz.rate.ref comshp.gompertz.shape.int comshp.gompertz.shape.ref 
#>              0.0013577411              0.0020717908              0.0020717908 
#>   comshp.gompertz.rate.TE   comshp.llogis.scale.int   comshp.llogis.scale.ref 
#>              0.9117551704            160.9000992103            323.1928425421 
#>   comshp.llogis.shape.int   comshp.llogis.shape.ref    comshp.llogis.scale.TE 
#>              1.9061724797              1.9061724797             -0.6974655102 
#>     comshp.gamma.rate.int     comshp.gamma.rate.ref    comshp.gamma.shape.int 
#>              0.0080245065              0.0040457815              1.6581134582 
#>    comshp.gamma.shape.ref      comshp.gamma.rate.TE  comshp.lnorm.meanlog.int 
#>              1.6581134582              0.6848254471              5.0221729357 
#>  comshp.lnorm.meanlog.ref    comshp.lnorm.sdlog.int    comshp.lnorm.sdlog.ref 
#>              5.7349959337              0.9862777518              0.9862777518 
#>   comshp.lnorm.meanlog.TE    comshp.gengamma.mu.int    comshp.gengamma.mu.ref 
#>             -0.7128229980              5.3875051273              6.0574058623 
#> comshp.gengamma.sigma.int comshp.gengamma.sigma.ref     comshp.gengamma.Q.int 
#>              0.7376518510              0.7376518510              0.9194986169 
#>     comshp.gengamma.Q.ref     comshp.gengamma.mu.TE        comshp.genf.mu.int 
#>              0.9194986169             -0.6699007349              5.3195083034 
#>        comshp.genf.mu.ref     comshp.genf.sigma.int     comshp.genf.sigma.ref 
#>              6.0061403615              0.6486737906              0.6486737906 
#>         comshp.genf.Q.int         comshp.genf.Q.ref         comshp.genf.P.int 
#>              0.7342316252              0.7342316252              0.9507499700 
#>         comshp.genf.P.ref         comshp.genf.mu.TE          sep.exp.rate.int 
#>              0.9507499700             -0.6866320581              0.0047623812 
#>          sep.exp.rate.ref     sep.weibull.scale.int     sep.weibull.scale.ref 
#>              0.0022103631            225.2127835800            436.9566692923 
#>     sep.weibull.shape.int     sep.weibull.shape.ref     sep.gompertz.rate.int 
#>              1.3840902659              1.3943919665              0.0033452613 
#>     sep.gompertz.rate.ref    sep.gompertz.shape.int    sep.gompertz.shape.ref 
#>              0.0013770185              0.0021260249              0.0020177231 
#>      sep.llogis.scale.int      sep.llogis.scale.ref      sep.llogis.shape.int 
#>            161.2189290723            324.7112201561              2.0160853126 
#>      sep.llogis.shape.ref        sep.gamma.rate.int        sep.gamma.rate.ref 
#>              1.7751939048              0.0082731268              0.0038716353 
#>       sep.gamma.shape.int       sep.gamma.shape.ref     sep.lnorm.meanlog.int 
#>              1.7085247925              1.5966917414              5.0200744483 
#>     sep.lnorm.meanlog.ref       sep.lnorm.sdlog.int       sep.lnorm.sdlog.ref 
#>              5.7540864808              0.9245220837              1.0684159722 
#>       sep.gengamma.mu.int       sep.gengamma.mu.ref    sep.gengamma.sigma.int 
#>              5.3572157739              6.1015374964              0.7495725809 
#>    sep.gengamma.sigma.ref        sep.gengamma.Q.int        sep.gengamma.Q.ref 
#>              0.6938119258              0.8349064781              1.0810413968 
#>           sep.genf.mu.int           sep.genf.mu.ref        sep.genf.sigma.int 
#>              5.3121167643              5.9645192695              0.6579307455 
#>        sep.genf.sigma.ref            sep.genf.Q.int            sep.genf.Q.ref 
#>              0.6108374874              0.7011635404              0.6013766571 
#>            sep.genf.P.int            sep.genf.P.ref       indshp.exp.rate.int 
#>              0.8037990819              1.7674326582              0.0047623812 
#>       indshp.exp.rate.ref        indshp.exp.rate.TE  indshp.weibull.scale.int 
#>              0.0022103631              0.7675910101            225.2127807052 
#>  indshp.weibull.scale.ref  indshp.weibull.shape.int  indshp.weibull.shape.ref 
#>            436.9566724857              1.3840902608              1.3943919552 
#>   indshp.weibull.scale.TE   indshp.weibull.shape.TE  indshp.gompertz.rate.int 
#>             -0.6627883952             -0.0074153734              0.0033453627 
#>  indshp.gompertz.rate.ref indshp.gompertz.shape.int indshp.gompertz.shape.ref 
#>              0.0013771028              0.0021259492              0.0020175414 
#>   indshp.gompertz.rate.TE  indshp.gompertz.shape.TE   indshp.llogis.scale.int 
#>              0.8875932775              0.0001084078            161.2182070372 
#>   indshp.llogis.scale.ref   indshp.llogis.shape.int   indshp.llogis.shape.ref 
#>            324.7128381498              2.0160768391              1.7752113169 
#>    indshp.llogis.scale.TE    indshp.llogis.shape.TE     indshp.gamma.rate.int 
#>             -0.7001824462              0.1272339965              0.0082721418 
#>     indshp.gamma.rate.ref    indshp.gamma.shape.int    indshp.gamma.shape.ref 
#>              0.0038711789              1.7083852182              1.5965550369 
#>      indshp.gamma.rate.TE     indshp.gamma.shape.TE  indshp.lnorm.meanlog.int 
#>              0.7593343613              0.0677004015              5.0200744478 
#>  indshp.lnorm.meanlog.ref    indshp.lnorm.sdlog.int    indshp.lnorm.sdlog.ref 
#>              5.7540824669              0.9245219161              1.0684159578 
#>   indshp.lnorm.meanlog.TE     indshp.lnorm.sdlog.TE    indshp.gengamma.mu.int 
#>             -0.7340080191             -0.1446556608              5.3572365418 
#>    indshp.gengamma.mu.ref indshp.gengamma.sigma.int indshp.gengamma.sigma.ref 
#>              6.1015975586              0.7495580016              0.6937828807 
#>     indshp.gengamma.Q.int     indshp.gengamma.Q.ref     indshp.gengamma.mu.TE 
#>              0.8349304188              1.0813995751             -0.7443610168 
#>  indshp.gengamma.sigma.TE        indshp.genf.mu.int        indshp.genf.mu.ref 
#>              0.0773246421              5.3121786256              5.9638342995 
#>     indshp.genf.sigma.int     indshp.genf.sigma.ref         indshp.genf.Q.int 
#>              0.6581219654              0.6103736384              0.7013283752 
#>         indshp.genf.Q.ref         indshp.genf.P.int         indshp.genf.P.ref 
#>              0.5991052605              0.8019159789              1.7770415367 
#>         indshp.genf.mu.TE      indshp.genf.sigma.TE          indshp.genf.Q.TE 
#>             -0.6516556739              0.0753189804              0.1022231148 
#>          indshp.genf.P.TE 
#>             -0.7957013646 
#> 
#> $config
#> $config$data
#>     USUBJID ARMCD PFS_days PFS_event
#> 1         1     A      141         1
#> 2         2     A      173         1
#> 3         3     A      197         1
#> 4         4     A      133         1
#> 5         5     A      100         1
#> 6         6     A      525         1
#> 7         7     A      464         1
#> 8         8     A      305         1
#> 9         9     A      673         0
#> 10       10     A       39         1
#> 11       11     A      160         1
#> 12       12     A      270         1
#> 13       13     A       39         1
#> 14       14     A      256         1
#> 15       15     A      193         1
#> 16       16     A      203         1
#> 17       17     A       30         1
#> 18       18     A      114         1
#> 19       19     A      111         1
#> 20       20     A      244         1
#> 21       21     A      403         1
#> 22       22     A      613         1
#> 23       23     A      219         1
#> 24       24     A       72         1
#> 25       25     A       85         1
#> 26       26     A      257         1
#> 27       27     A      175         1
#> 28       28     A       99         1
#> 29       29     A      208         1
#> 30       30     A      108         1
#> 31       31     A      105         1
#> 32       32     A      319         1
#> 33       33     A      123         1
#> 34       34     A       76         1
#> 35       35     A      495         0
#> 36       36     A      161         1
#> 37       37     A      241         1
#> 38       38     A       22         1
#> 39       39     A      199         1
#> 40       40     A      304         1
#> 41       41     A      142         1
#> 42       42     A      372         1
#> 43       43     A      161         1
#> 44       44     A       84         1
#> 45       45     A      197         1
#> 46       46     A      132         1
#> 47       47     A      281         1
#> 48       48     A       14         1
#> 49       49     A      302         1
#> 50       50     A      208         1
#> 51       51     A       26         1
#> 52       52     A      234         1
#> 53       53     A       35         1
#> 54       54     A      232         1
#> 55       55     A       81         1
#> 56       56     A       58         1
#> 57       57     A      277         1
#> 58       58     A      347         1
#> 59       59     A      295         1
#> 60       60     A      157         1
#> 61       61     A      266         1
#> 62       62     A      116         1
#> 63       63     A      620         1
#> 64       64     A       77         1
#> 65       65     A       48         1
#> 66       66     A      127         1
#> 67       67     A       41         1
#> 68       68     A      226         1
#> 69       69     A       76         1
#> 70       70     A      217         1
#> 71       71     A      181         1
#> 72       72     A      112         1
#> 73       73     A      212         1
#> 74       74     A      147         1
#> 75       75     A      103         1
#> 76       76     A      113         1
#> 77       77     A      169         1
#> 78       78     A      404         1
#> 79       79     A      330         1
#> 80       80     A       74         1
#> 81       81     A      373         1
#> 82       82     A      568         0
#> 83       83     A      304         1
#> 84       84     A      158         1
#> 85       85     A      117         1
#> 86       86     A      409         0
#> 87       87     A      293         1
#> 88       88     A      163         1
#> 89       89     A      299         1
#> 90       90     A      166         1
#> 91       91     A      211         1
#> 92       92     A       84         1
#> 93       93     A      275         1
#> 94       94     A       59         1
#> 95       95     A       61         1
#> 96       96     A      217         1
#> 97       97     A      186         1
#> 98       98     A      275         1
#> 99       99     A      174         1
#> 100     100     A      183         1
#> 101     101     A      130         1
#> 102     102     A      163         1
#> 103     103     A      136         1
#> 104     104     A       38         1
#> 105     105     A      157         1
#> 106     106     A      372         1
#> 107     107     A       51         1
#> 108     108     A      172         1
#> 109     109     A       75         1
#> 110     110     A       92         1
#> 111     111     A       21         1
#> 112     112     A      464         0
#> 113     113     A       27         1
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#> 115     115     A      265         1
#> 116     116     A      213         1
#> 117     117     A      239         1
#> 118     118     A      126         1
#> 119     119     A      135         1
#> 120     120     A      133         1
#> 121     121     A       35         1
#> 122     122     A      431         1
#> 123     123     A      151         1
#> 124     124     A       92         1
#> 125     125     A      372         1
#> 126     126     A      108         1
#> 127     127     A        4         1
#> 128     128     A       89         1
#> 129     129     A       95         1
#> 130     130     A      193         1
#> 131     131     A      176         1
#> 132     132     A      172         1
#> 133     133     A      108         1
#> 134     134     A      212         1
#> 135     135     A      325         1
#> 136     136     A      529         1
#> 137     137     A      213         1
#> 138     138     A       67         1
#> 139     139     A      125         1
#> 140     140     A      514         1
#> 141     141     A       96         1
#> 142     142     A      165         1
#> 143     143     A       70         1
#> 144     144     A       15         1
#> 145     145     A      457         1
#> 146     146     A      311         1
#> 147     147     A      150         1
#> 148     148     A      210         1
#> 149     149     A      225         1
#> 150     150     A      421         0
#> 151     151     A      262         1
#> 152     152     A      148         1
#> 153     153     A      641         0
#> 154     154     A       72         1
#> 155     155     A      258         1
#> 156     156     A      213         1
#> 157     157     A      110         1
#> 158     158     A       43         1
#> 159     159     A       55         1
#> 160     160     A       87         1
#> 161     161     A      140         1
#> 162     162     A      386         1
#> 163     163     A      122         1
#> 164     164     A      169         1
#> 165     165     A       35         1
#> 166     166     A      106         1
#> 167     167     A      297         1
#> 168     168     A      169         1
#> 169     169     A      470         0
#> 170     170     A      119         1
#> 171     171     A        3         1
#> 172     172     A      152         1
#> 173     173     A      443         1
#> 174     174     A      342         1
#> 175     175     A      142         1
#> 176     176     A      330         1
#> 177     177     A       55         1
#> 178     178     A      330         1
#> 179     179     A      165         1
#> 180     180     A      100         1
#> 181     181     A      564         1
#> 182     182     A       62         1
#> 183     183     A      397         0
#> 184     184     A      105         1
#> 185     185     A      213         1
#> 186     186     A      187         1
#> 187     187     A       67         1
#> 188     188     A      103         1
#> 189     189     A      146         1
#> 190     190     A      161         1
#> 191     191     A      402         1
#> 192     192     A       63         1
#> 193     193     A      128         1
#> 194     194     A      129         1
#> 195     195     A      124         1
#> 196     196     A      378         0
#> 197     197     A      230         1
#> 198     198     A      375         0
#> 199     199     A      332         1
#> 200     200     A      330         1
#> 201     201     A      537         1
#> 202     202     A       76         1
#> 203     203     A      324         1
#> 204     204     A      140         1
#> 205     205     A      198         1
#> 206     206     A       30         1
#> 207     207     A      173         1
#> 208     208     A      207         1
#> 209     209     A      128         1
#> 210     210     A       55         1
#> 211     211     A      310         1
#> 212     212     A      451         0
#> 213     213     A      286         1
#> 214     214     A       40         1
#> 215     215     A      179         1
#> 216     216     A      173         1
#> 217     217     A       56         1
#> 218     218     A      323         1
#> 219     219     A      202         1
#> 220     220     A        7         1
#> 221     221     A      129         1
#> 222     222     A       76         1
#> 223     223     A      237         1
#> 224     224     A      368         1
#> 225     225     A      171         1
#> 226     226     A       70         1
#> 227     227     A      436         1
#> 228     228     A       23         1
#> 229     229     A      103         1
#> 230     230     A      150         1
#> 231     231     A      291         1
#> 232     232     A       36         1
#> 233     233     A       11         1
#> 234     234     A      132         1
#> 235     235     A      367         1
#> 236     236     A      293         1
#> 237     237     A      153         1
#> 238     238     A       48         1
#> 239     239     A      110         1
#> 240     240     A      192         1
#> 241     241     A      135         1
#> 242     242     A      328         1
#> 243     243     A      173         1
#> 244     244     A      190         1
#> 245     245     A       61         1
#> 246     246     A      466         1
#> 247     247     A      464         1
#> 248     248     A      274         1
#> 249     249     A      355         1
#> 250     250     A       11         1
#> 251     251     B      232         1
#> 252     252     B        6         1
#> 253     253     B       44         1
#> 254     254     B      227         1
#> 255     255     B      125         1
#> 256     256     B        6         1
#> 257     257     B      194         1
#> 258     258     B      272         1
#> 259     259     B      480         0
#> 260     260     B      284         1
#> 261     261     B       71         1
#> 262     262     B      461         0
#> 263     263     B      492         1
#> 264     264     B      125         1
#> 265     265     B      460         1
#> 266     266     B      381         0
#> 267     267     B      201         1
#> 268     268     B      385         0
#> 269     269     B      660         0
#> 270     270     B      539         0
#> 271     271     B      264         1
#> 272     272     B      215         1
#> 273     273     B      227         1
#> 274     274     B      170         1
#> 275     275     B      441         0
#> 276     276     B      543         0
#> 277     277     B        7         1
#> 278     278     B      310         1
#> 279     279     B      505         0
#> 280     280     B      253         1
#> 281     281     B      240         1
#> 282     282     B      216         1
#> 283     283     B      275         1
#> 284     284     B      531         1
#> 285     285     B       49         1
#> 286     286     B       81         1
#> 287     287     B      712         0
#> 288     288     B      488         0
#> 289     289     B      279         1
#> 290     290     B      145         1
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#> 301     301     B      584         0
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#> 320     320     B      625         1
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#> 433     433     B      468         1
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#> 435     435     B      396         1
#> 436     436     B      360         1
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#> 438     438     B      231         1
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#> 443     443     B      380         0
#> 444     444     B      208         1
#> 445     445     B      404         1
#> 446     446     B      468         0
#> 447     447     B      447         1
#> 448     448     B      313         1
#> 449     449     B      447         1
#> 450     450     B      705         0
#> 451     451     B      326         1
#> 452     452     B      401         1
#> 453     453     B      158         1
#> 454     454     B      194         1
#> 455     455     B      564         1
#> 456     456     B      296         1
#> 457     457     B      129         1
#> 458     458     B      116         1
#> 459     459     B      303         1
#> 460     460     B      727         0
#> 461     461     B      374         1
#> 462     462     B      236         1
#> 463     463     B      167         1
#> 464     464     B      563         1
#> 465     465     B      535         0
#> 466     466     B      398         1
#> 467     467     B      101         1
#> 468     468     B       31         1
#> 469     469     B      465         0
#> 470     470     B      413         0
#> 471     471     B      126         1
#> 472     472     B      388         1
#> 473     473     B      100         1
#> 474     474     B      383         1
#> 475     475     B      215         1
#> 476     476     B      536         0
#> 477     477     B      424         0
#> 478     478     B      119         1
#> 479     479     B       94         1
#> 480     480     B      138         1
#> 481     481     B      438         0
#> 482     482     B      211         1
#> 483     483     B      135         1
#> 484     484     B      587         1
#> 485     485     B       50         1
#> 486     486     B      314         1
#> 487     487     B       70         1
#> 488     488     B      425         1
#> 489     489     B      552         0
#> 490     490     B      681         0
#> 491     491     B      385         0
#> 492     492     B      338         1
#> 493     493     B      367         0
#> 494     494     B      273         1
#> 495     495     B      181         1
#> 496     496     B       35         1
#> 497     497     B      403         1
#> 498     498     B      462         1
#> 499     499     B       31         1
#> 500     500     B      421         1
#> 
#> $config$time_var
#> [1] "PFS_days"
#> 
#> $config$event_var
#> [1] "PFS_event"
#> 
#> $config$weight_var
#> [1] ""
#> 
#> $config$model.type
#> [1] "Common shape"      "Independent shape" "Separate"         
#> 
#> $config$distr
#> [1] "exp"      "weibull"  "gompertz" "lnorm"    "llogis"   "gengamma" "gamma"   
#> [8] "genf"    
#> 
#> $config$int_name
#> [1] "A"
#> 
#> $config$strata_var
#> [1] "ARMCD"
#> 
#> $config$ref_name
#> [1] "B"

Estimate survival from the models and plot the curves

Survival at a given time, t, is estimated as follows:

\[ S(t) = P({T>t}) = 1 - F(t) \]

Where F(t) is the cumulative distribution function.

To cross check survival estimates in Excel models, the following functions in R can be used to estimate the cumulative distribution function at given time points for each distribution explored in this package (the estimates from the cumulative distribution function can then be subtracted from 1 to estimate the survival probability):

  • Exponential: pexp()

  • Weibull: pweibull()

  • Gompertz: pgompertz()

  • Log-normal: plnorm()

  • Log-logistic: pllogis()

  • Generalized gamma: pgengamma()

  • Gamma: pgamma()

  • Generalized F: pgenf()

The parameters outputted from each of the fitted models are used as inputs to these functions. The code below gives some examples.

# Landmark survival

# vector of times to estimate survival (days)
landmark_times <- c(0, 100, 200, 300)


# Example 1: intervention arm, Weibull distribution, common shape model
surv_comshp_weibull_int <- 1 - pweibull(landmark_times,
                                        shape = psm_PFS_all$parameters_vector["comshp.weibull.shape.int"],
                                        scale = psm_PFS_all$parameters_vector["comshp.weibull.scale.int"])
surv_comshp_weibull_int
#> [1] 1.0000000 0.7234631 0.4286008 0.2259593


# Example 2: intervention arm, log-normal distribution, separate model
surv_sep_lnorm_int <- 1 - plnorm(landmark_times,
                                 meanlog = psm_PFS_all$parameters_vector["sep.lnorm.meanlog.int"],
                                 sdlog = psm_PFS_all$parameters_vector["sep.lnorm.sdlog.int"])
surv_sep_lnorm_int
#> [1] 1.0000000 0.6732037 0.3817230 0.2297939


# Example 3: reference arm, Generalized Gamma distribution, independent shape model
surv_indshp_gengamma_ref <- 1 - pgengamma(landmark_times,
                                          mu = psm_PFS_all$parameters_vector["indshp.gengamma.mu.ref"],
                                          sigma = psm_PFS_all$parameters_vector["indshp.gengamma.sigma.ref"],
                                          Q = psm_PFS_all$parameters_vector["indshp.gengamma.Q.ref"])
surv_indshp_gengamma_ref
#> [1] 1.0000000 0.8790137 0.7164446 0.5563410

To simplify these actions a helper function is included in the flexsurvPlus package that will extract these values directly. This will calculate the same values as above.


# repeat the prior example for landmark times

landmarks_df <- summaryPSM(x = psm_PFS_all,
                        types = "survival",
                        t = landmark_times
                        )

landmarks_df %>%
  filter(Model == "Common shape", Dist == "Weibull")
#> # A tibble: 8 × 11
#>   Model    ModelF Dist  DistF distr Strata StrataName type  variable  time value
#>   <chr>    <ord>  <chr> <ord> <chr> <chr>  <chr>      <chr> <chr>    <dbl> <dbl>
#> 1 Common … Commo… Weib… Weib… weib… Inter… A          surv… est          0 1    
#> 2 Common … Commo… Weib… Weib… weib… Inter… A          surv… est        100 0.723
#> 3 Common … Commo… Weib… Weib… weib… Inter… A          surv… est        200 0.429
#> 4 Common … Commo… Weib… Weib… weib… Inter… A          surv… est        300 0.226
#> 5 Common … Commo… Weib… Weib… weib… Refer… B          surv… est          0 1    
#> 6 Common … Commo… Weib… Weib… weib… Refer… B          surv… est        100 0.879
#> 7 Common … Commo… Weib… Weib… weib… Refer… B          surv… est        200 0.713
#> 8 Common … Commo… Weib… Weib… weib… Refer… B          surv… est        300 0.553

The same functions can be used to generate the data required to plot the survival curves, overlaid on top of the KM plot. The time argument should reflect how long you want to the extrapolate for and the unit of time is the same as the input data (in this example, days).


# Plot the common shape models (Weibull distribution) with the Kaplan-Meier

# vector of times to estimate survival (days)
times <- c(seq(from = 0, to = 1000, by = 0.1))


# Survival probabilities: intervention arm, Weibull distribution, common shape model
surv_comshp_weibull_int <- 1 - pweibull(times,
                                  shape = psm_PFS_all$parameters_vector["comshp.weibull.shape.int"],
                                  scale = psm_PFS_all$parameters_vector["comshp.weibull.scale.int"])

# Survival probabilities: reference arm, Weibull distribution, common shape model
surv_comshp_weibull_ref <- 1 - pweibull(times,
                                  shape = psm_PFS_all$parameters_vector["comshp.weibull.shape.ref"],
                                  scale = psm_PFS_all$parameters_vector["comshp.weibull.scale.ref"])

# Create two data frames that include the survival probablaities and times
surv_comshp_weibull_int_times <- data.frame(Time = times,
                                            Surv = surv_comshp_weibull_int,
                                            Trt = "Intervention")
surv_comshp_weibull_ref_times <- data.frame(Time = times,
                                            Surv = surv_comshp_weibull_ref,
                                            Trt = "Reference")

# Plot Kaplan-Meier
plot(km.est.PFS, 
     col = c(blue, pink), # plot colours
     lty = c(1:2), # line type
     xlab = "Time (Days)", # x-axis label
     ylab = "Progression-free survival", # y-axis label
     xlim = c(0, 1000)) 

# Add legend
legend(x = 500, 
       y = .9, 
       legend = c("Arm A", "Arm B"), 
       lty = c(1:2), 
       col = c(blue, pink))

# Add model estimates
lines(x = surv_comshp_weibull_int_times$Time, y = surv_comshp_weibull_int_times$Surv, col = blue)
lines(x = surv_comshp_weibull_ref_times$Time, y = surv_comshp_weibull_ref_times$Surv, col = pink)

The same helper function can also be used to generate plots.


# repeat the prior example for plot data

plot_esurv_df <- summaryPSM(x = psm_PFS_all,
                            types = "survival",
                            t = times
                            )

# a similar function will estimate the values needed for KM estimates

plot_km_df <- summaryKM(data = PFS_data,
                        time_var="PFS_days",
                        event_var="PFS_event",
                        strata_var = "ARMCD",
                        int_name="A",
                        ref_name = "B",
                        types = "survival"
                        )
                      

# can then combine these to plot

plot_esurv_df %>%
  dplyr::filter(Model == "Common shape",
                          Dist == "weibull") %>%
  dplyr::bind_rows(plot_km_df) %>%
  dplyr::filter(type == "survival", variable == "est") %>%
  dplyr::mutate(Model_Dist = paste(Model, Dist, sep = " - ")) %>%
  ggplot(aes(x = time, y = value, color = StrataName, linetype = Model_Dist)) +
  geom_step(data = function(x){dplyr::filter(x, Model == "Kaplan Meier") }) +
  geom_line(data = function(x){dplyr::filter(x, Model != "Kaplan Meier") }) 

Adressing uncertainty - Bootstrapping

Bootstrapping has been used to estimate the uncertainty of the parameters from the survival models. Boostrapping is used for two reasons motivated by intent of this package to support further modeling in excel. 1. To simplify and accelerate calculations in excel while maintaining correlations between parameters (as is commonly done for NMA) 2. To maintain correlations across multiple endpoints (see separate vignette for details)

Bootstrapping involves:

  1. Sampling, with replacement, from all patients
  2. Estimating all specified parametric survival models

This procedure is repeated multiple times to obtain a distribution of parameters. For this example, bootstrap estimates of the parameters were calculated using the boot package. An argument for the boot function is statistic which is a function which when applied to data returns a vector containing the statistic(s) of interest. The bootPSM function in the flexsurvPlus package can be used for this purpose.

The inputs for the bootPSM function are identical to the runPSM function, however there is one additional argument:

  • i - Index used to select a sample within boot

As the parameters are stored in the config object returned by runPSM it is possible to use do.call to simplify these calls assuming that models have already been fit using runPSM.


# illustrative example using original analysis models
# only create 2 replicates for illustration
set.seed(2358)
boot_psm_PFS_all <- do.call(boot, args = c(psm_PFS_all$config, R = 2, statistic = bootPSM))

# is the same as
set.seed(2358)
boot_psm_PFS_demo <- boot(
  R = 2, # number of bootstrap samples
  statistic = bootPSM, # bootstrap function
  data=PFS_data,
  time_var="PFS_days",
  event_var="PFS_event",
  model.type= c("Common shape",
                "Independent shape", 
                "Separate"),
  distr = c('exp',
            'weibull',
            'gompertz',
            'lnorm',
            'llogis',
            'gengamma',
            'gamma',
            'genf'),
  strata_var = "ARMCD",
  int_name="A",
  ref_name = "B"
)


all(boot_psm_PFS_all$t==boot_psm_PFS_demo$t, na.rm = TRUE)
#> [1] TRUE

For speed and to examine how this can be used we will repeat this selecting only 4 models.


set.seed(2358)
# To minimize vignette computation time only 100 bootstrap samples are taken. In general more samples should be used.
n.sim <- 100 

psm_PFS_selected <- runPSM(
  data=PFS_data,
  time_var="PFS_days",
  event_var="PFS_event",
  model.type = c("Common shape", "Separate"),
  distr = c('weibull', 'gamma'),
  strata_var = "ARMCD",
  int_name = "B",
  ref_name = "A"
)

PSM_bootstraps_PFS <- do.call(boot, args = c(psm_PFS_selected$config,
                                             statistic = bootPSM, # bootstrap function
                                             R=n.sim # number of bootstrap samples
                                             )
                              )

To use the result of these samples it is helpful to do some post processing to make the resulting samples easier to interpret.

# first extract the bootstrapped parameters into a tibble
PFS_bootsamples <- as_tibble(PSM_bootstraps_PFS$t)
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
#> `.name_repair` is omitted as of tibble 2.0.0.
#>  Using compatibility `.name_repair`.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
# then add column names so can identify model and parameter more easily
colnames(PFS_bootsamples) <- names(PSM_bootstraps_PFS$t0)

# show the first 3 samples
PFS_bootsamples[1:3,]
#> # A tibble: 3 × 18
#>   comshp.weibull.scale.int comshp.weibull.scale.ref comshp.weibull.shape.int
#>                      <dbl>                    <dbl>                    <dbl>
#> 1                     454.                     230.                     1.41
#> 2                     453.                     252.                     1.29
#> 3                     425.                     224.                     1.42
#> # ℹ 15 more variables: comshp.weibull.shape.ref <dbl>,
#> #   comshp.weibull.scale.TE <dbl>, comshp.gamma.rate.int <dbl>,
#> #   comshp.gamma.rate.ref <dbl>, comshp.gamma.shape.int <dbl>,
#> #   comshp.gamma.shape.ref <dbl>, comshp.gamma.rate.TE <dbl>,
#> #   sep.weibull.scale.int <dbl>, sep.weibull.scale.ref <dbl>,
#> #   sep.weibull.shape.int <dbl>, sep.weibull.shape.ref <dbl>,
#> #   sep.gamma.rate.int <dbl>, sep.gamma.rate.ref <dbl>, …

Estimating quantities from the sample

As the flexsurv parameterisations are used any quantity of interest can be simply calculated for all models and samples through use of the flexsurv functions such as the extrapolated means via flexsurv::mean_weibull. For this example we assume we have decided that separate models for reference and intervention are most appropriate and that for the reference arm a gamma model is preferred while for the intervention arm a weibull model is best.

# we can now calculate the mean PFS for the selected models for each bootstrap sample
# using weibull for the reference
PFS_ref_mean <- with(PFS_bootsamples, flexsurv::mean_gamma(shape = sep.gamma.shape.ref, rate = sep.gamma.rate.ref))
# using gamma for the intervention
PFS_int_mean <- with(PFS_bootsamples, flexsurv::mean_weibull(shape = sep.weibull.shape.int, scale = sep.weibull.scale.int))

# we could also calculate these values also for the original data for a deterministic estimate 
PFS_means <- summaryPSM(psm_PFS_selected,
                        type = "mean") 

PFS_means %>%
  dplyr::transmute(Model, Dist, Strata,StrataName, value) %>%
  dplyr::filter((Dist == "Gamma" & Model == "Separate - Reference") |
                  (Dist == "Weibull" & Model == "Separate - Intervention") )
#> # A tibble: 2 × 5
#>   Model                   Dist    Strata       StrataName value
#>   <chr>                   <chr>   <chr>        <chr>      <dbl>
#> 1 Separate - Intervention Weibull Intervention B           399.
#> 2 Separate - Reference    Gamma   Reference    A           207.
                  

# we can then calculate the incremental mean PFS from these
PFS_delta = PFS_int_mean - PFS_ref_mean 


# if we are interested we can then estimate quantiles from this
quantile(PFS_delta, probs = c(0.025,0.975))
#>     2.5%    97.5% 
#> 149.4424 236.3659

# or plot the density of this derived quantity

density <- density(PFS_delta)
plot(density, 
     lwd = 2, 
     main = "Density")

Outputing parameters to excel

The primary use of the bootstrap samples is to be used in probabilistic sensitivity analyses in economic models.

Once all the models have been fit and bootstrap samples estimated they can be output to excel. By selecting the “Main Estimates” the estimates for the original data are returned. To run PSA in excel only a random number between 1 and the number of samples needs to be generated and the associated Bootstrap sample selected.


# combine the estimates from the main analysis with the bootstrap samples
# and add meta data to include details of analysis

# first we can get combine the main estimates for the models with those that were bootstrapped
parameters_PFS <- rbind(PSM_bootstraps_PFS$t0, as_tibble(PSM_bootstraps_PFS$t))

# we can now add names
colnames(parameters_PFS) <- names(PSM_bootstraps_PFS$t0)

# we can then label the samples and add some metadata
metadata_PFS <- tibble(Estimate = c("Main Estimates", paste("Bootstrap Sample",1:n.sim)),
                       Study_name = "Study ABC",
                        Datacut = "Final",
                        Population = "ITT",
                        Endpoint = "PFS",
                        RefArmName = PSM_bootstraps_PFS$call$ref_name,
                        IntArmName = PSM_bootstraps_PFS$call$int_name)

pfs_for_export <- cbind(metadata_PFS, parameters_PFS)

# not run
#write.csv(parameters_PFS, "params_for_model.csv")

References

[1]
Latimer N. NICE DSU technical support document 14: Survival analysis for economic evaluations alongside clinical trials-extrapolation with patient-level data. Sheffield: Report by the Decision Support Unit 2011;2013.
[2]
Rutherford MJ, Lambert PC, Sweeting MJ, Pennington B, Crowther MJ, Abrams KR, et al. NICE DSU TECHNICAL SUPPORT DOCUMENT 21: Flexible methods for survival analysis 2020.
[3]
CADTH. Procedures for CADTH drug reimbursement reviews 2020.