Author: Manoj Khanal khanal_manoj@lilly.com
Date: 2025-02-11

# 2. Matching and Weighting Application with Dynamic Borrowing

#### Manoj Khanal khanal_manoj@lilly.com

#### Eli Lilly & Company

#### 2025-02-11

In the previous article on matching/weighting methods, we had used various matching/weighting methods to minimize selection bias. In this article, we will use matched/weighted cohort for Bayesian dynamic borrowing. This article is a follow-up to the matching/weighting article, which demonstrates methods to mitigate selection bias when designing externally controlled trial. In this article, we will illustrate Bayesian dynamic borrowing approaches using the matched and weighted cohort. We will use the `psborrow2`(Isaac Gravestock 2024) R package to demonstrate the use of Bayesian methods with an example for time to event outcome. In addition to using `psborrow2` package, we will also demonstrate how to implement power and commensurate prior models with piecewise exponential baseline hazard function.

We consider the following time to event outcome models in this article

- Exponential distribution with constant hazard
- Weibull distribution with proportional hazards parametrization
- Piecewise Exponential distribution (not implemented yet in `psborrow2` R package)

We will also consider several Bayesian dynamic borrowing methods

- Commensurate prior
- Power prior

# Hazard function

The hazard function of \(T\) at time \(t\) is defined as the instantaneous rate of the event occurence that is still at risk at time \(t\). It is defined as

\[\begin{equation} h(t)=\lim_\limits{\Delta \rightarrow 0 } P\frac{(t\leq T<t+\Delta t|T\geq t)}{\Delta t}=\frac{f(t)}{S(t)}, \end{equation}\]

where \(f(t)\) is the density function of \(T\) given \(\theta\), and \(S(t)=P(T>t)\) is the survival function of \(T\).

# Cox proportional hazards model

We start with the Cox proportional hazards model (Cox 1972) for observation \(i\) as follows. To estimate the regression coefficients under the Bayesian framework, the baseline hazard function needs to be specified. This is not the case under a Frequentist Cox regression. The hazard at time \(t\) is

\[\begin{equation} \label{eq:coxmodel} \lambda(t|\boldsymbol{X}_i,Z_i)=\lambda_0(t|\alpha) \exp(\gamma Z_i +\beta^T \boldsymbol{X}_i), \end{equation}\]

where \(\boldsymbol{X}_i\) is the vector of baseline covariates and \(Z_i\) is the treatment indicator.

# Baseline hazard function

Baseline hazard functions can be modeled using common probability distributions in survival analysis, such as exponential, Weibull, and Gompertz. However, these standard specifications have limited flexibility and cannot capture irregular behaviors. Alternatively, more flexible hazard shapes can be specified using piecewise constant, piecewise exponential or spline functions, allowing for the representation of multimodal patterns and accommodating diverse irregularities (Lázaro, Armero, and Alvares 2021). The misspecification of the baseline hazard can lead to the loss of important model details which can be challenging to accurately estimate outcomes of interest, such as probabilities or survival curves. In this article, we consider the following baseline hazard functions.

- Exponential distribution with constant hazard
  - \(\lambda_0(t|\alpha)=exp(\alpha)\)
- Weibull distribution with proportional hazards parametrization
  - \(\lambda_0(t|\zeta,\mu)=\alpha t\mu^{\zeta -1} \exp(-\mu t^\zeta)\) with shape parameter \(\zeta\) and scale parameter \(\mu\).
- Piecewise exponential distribution
  - \(\lambda_0(t|\alpha_1,...,\alpha_K)=exp(\alpha_k)\) for \(t \in (s_{k-1},s_k],\) where \((s_{k-1},s_k]\) is the \(k^{th}\) interval and \(K\) is the total number of pre-specified intervals as in Murray et al. (2014). In our example below, we will consider \(s_k=(100*k/K)^{th}\) to the percentile of the event times from the treated group.

# Likelihood

We now introduce the likelihood along with the weight \(w_i\) which is a subject specific weight. For the purpose of demonstration, we have considered the subject specific weight from inverse probability of treatment weighting method estimating ATT. In other words, \(w_i=1\) for subject \(i\) in RCT and \(0 \leq w_i \leq 1\) for subject \(i\) in RWD. Let \({Y}_{i}\) be the observed time, \({\delta}_{i}\) be an event indicator, \(\boldsymbol{X}_{i}\) be the covariates, and \({Z}_{i}\) be the treatment indicator in the trial data. Similarly, let \({Y}_{0,i}\) be the observed time, \({\delta}_{0,i}\) be an event indicator, \(\boldsymbol{X}_{0,i}\) be the covariates and \({Z}_{0,i}\) be the treatment indicator in external control data. The time axis is partitioned into \(K\) intervals: \((0,\kappa_1],(\kappa_1,\kappa_2],\ldots,(\kappa_{K-1},\kappa_K)\). Let \(\lambda_0(t|\alpha_k)=\exp{(\alpha_k)}\) be the piecewise exponential baseline hazard function with \(t \in I_k=(\kappa_{k-1},\kappa_k)\) for \(k=1,\ldots,K\). Denote \(\boldsymbol{\alpha}=(\alpha_1,\ldots,\alpha_K)^\intercal\). Let \(\boldsymbol\beta\) and \(\gamma\) be the covariate and treatment effects respectively in trial. Similarly, let \(\boldsymbol{\beta}_0\) and \(\boldsymbol{\alpha}_0\) represent the parameters in external control.

The joint likelihood of trial and external control for right censored observations after taking account the weight \(w_i\) with baseline hazard as a piecewise exponential function and \(K\) intervals can be written by slightly extending Murray et al. (2014) as

\[ \begin{equation} \small \begin{split} & L(\boldsymbol{\alpha},\boldsymbol{\beta},\boldsymbol{\gamma},\boldsymbol{\alpha}_0,\boldsymbol{\beta}_0 |\boldsymbol{D},\boldsymbol{D}_0 ) \\ & = L(\boldsymbol{\alpha},\boldsymbol{\beta},\boldsymbol{\gamma}|\boldsymbol{D},\boldsymbol \mu) L(\boldsymbol{\alpha}_0,\boldsymbol{\beta}_0|\boldsymbol{D}_0) \\ & =\prod_{i=1}^n \prod_{k=1}^K \Bigg[\exp{\bigg\{\mathcal I(Y_{i} \in I_k)\mathcal S_{ik}\bigg\}}^{\delta_{i}} \exp\bigg\{-\mathcal I(Y_{i} \in I_k) \bigg( \sum_{\ell=1}^{k-1} (\kappa_\ell - \kappa_{\ell-1}) \\ & \times \exp{(\mathcal S_{i\ell})} + (Y_{i} - \kappa_{k-1}) \exp{(\mathcal S_{ik}) } \bigg) \bigg\} \Bigg] \Bigg[ \exp{\bigg\{\mathcal I(Y_{0,i} \in I_k) \mathcal S_{0,ik} \bigg\} }^{\delta_{0,i}} \\ & \times \exp\bigg\{-\mathcal I(Y_{0,i} \in I_k) \bigg( \sum_{\ell=1}^{k-1} (\kappa_\ell - \kappa_{\ell-1})\exp{(\mathcal S_{0,i\ell})} + (Y_{0,i} - \kappa_{k-1})\\ & \times \exp{(\mathcal S_{0,ik}) } \bigg) \bigg\} \Bigg], \end{split} \end{equation} \]

where \(\mathcal S_{0,ik}=\alpha_{0,k} + \boldsymbol{\beta}_0^\intercal \boldsymbol{X}_{0,i}\), \(\mathcal S_{ik}=\alpha_{k} + \boldsymbol{\beta}^\intercal \boldsymbol{X}_{i} + {\gamma} {Z}_{i}\), \(n=n_T+n_E\); \(n_T\) and \(n_E\) are the sample size in trial and external control data respectively. If \(w_i=1\), then the above likelihood becomes the traditional likelihood for Cox proportional hazards model. Above notations are similar as in Khanal (2022).

The joint likelihood can also be constructed in a similar way for other baseline hazard functions.

# Power prior model

We denote the data for the randomized controlled trial (RCT) as \(D_1\) with the corresponding likelihood as \(L(\gamma,\beta,\alpha│D_1 )\) and external control (EC) data as \(D_0\) with corresponding likelihood as \(L(\gamma,\beta,\alpha│D_0)\). The formulation of power prior is (Ibrahim et al. 2015) \[p(\gamma,\beta,\alpha│D_0,ν) \propto L(\gamma,\beta,\alpha│D_0 )^ν *p(\gamma)*p(\beta)*p(\alpha),\] where \(0 \leq ν \leq 1\) is the vector of subject specific weights in the EC data, and \(p(\gamma)\), \(p(\beta)\), and \(p(\alpha)\) are the priors for \(\gamma\), \(\beta\) and \(\alpha\) respectively.

The posterior distribution is \[p(\beta│D_1,D_0,ν) \propto L(\gamma,\beta,\alpha│D_1 ) L(\gamma,\beta,\alpha│D_0 )^ν *p(\gamma)*p(\beta)*p(\alpha).\]

We consider the non-informative prior distributions for the parameters as \(\gamma \sim N(0,0.001)\), \(\beta_j \sim N(0,0.001)\) for \(j=1...,P\) with \(P\) baseline covariates, and \(\alpha_k \sim N(0,0.001)\) for \(k=1,...,K\) intervals.

Note: To avoid confusion, \(N(a,b)\) means a normal distribution with mean \(a\) and precision \(b\).

# Commensurate prior model

Let \(\boldsymbol{\eta}=(\beta_1,...,\beta_P,\alpha_1,...,\alpha_K)\) and \(\boldsymbol{\eta}_0=(\beta_{10},...,\beta_{P0},\alpha_{10},...,\alpha_{K0})\) be the parameter vector for RCT and RWD respectively. We consider the following commensurate prior

\[\eta_\ell|\eta_{0,\ell},\tau_\ell \sim N(\eta_{\ell 0},\tau_\ell) \quad \quad \ell=1,\cdots,P+K,\]

where \(\tau_\ell\) is the precision parameter that determines the degree of borrowing, \(P\) is the total number of covariates and \(K\) is the total number of intervals. For the precision parameter, we assign a gamma prior as \(\tau_\ell \sim Gamma(0.01,0.01)\).

# Conducting Analysis

We first load the following libraries.

    #Loading the libraries
    library(R2jags)
    #> Warning: package 'R2jags' was built under R version 4.3.2
    #> Warning: package 'coda' was built under R version 4.3.2
    library(psborrow2)
    library(cmdstanr)
    #> Warning: package 'cmdstanr' was built under R version 4.3.3
    library(posterior)
    library(bayesplot)
    #> Warning: package 'bayesplot' was built under R version 4.3.2
    library(kableExtra)
    #> Warning: package 'kableExtra' was built under R version 4.3.2
    library(survival)
    #> Warning: package 'survival' was built under R version 4.3.3

## Example data

We also load the data set `data_with_weights`.

- The continuous variables are
  - `Age` (years)
  - `Weight` (lbs)
  - `Height` (inches)
  - `Biomarker1`
  - `Biomarker2`
- The categorical variables are
  - `Smoker`: Yes = 1 and No = 0
  - `Sex`: Male = 1 and Female = 0
  - `ECOG1`: ECOG 1 = 1 and ECOG 0 = 0
- The treatment indicator is
  - `group`: group = 1 is treatment and group = 0 is placebo
- The time to event variable is
  - `time`
- The event indicator variable is
  - `event`: event = 1 represents an event occurred and event = 0 indicates the event was censored
- The data source indicator variable is
  - `indicator`: indicator=1 is the RCT data and indicator=0 is the EC data
- The estimated weights after applying matching/weighting methods are
  - `ratio1_caliper_weights_lps`: Weights after 1:1 Nearest Neighbor Propensity score matching with a caliper width of 0.2 of the standard deviation of the logit of the propensity score
  - `ratio_caliper_weights`: Weights after 1:1 Nearest Neighbor Propensity score matching with a caliper width of 0.2 of the standard deviation of raw propensity score
  - `genetic_ratio1_weights`: Weights after 1:1 Genetic matching with replacement
  - `genetic_ratio1_weights_no_replace`: Weights after 1:1 Genetic matching without replacement
  - `optimal_ratio1_weights`: Weights after 1:1 Optimal matching
  - `weights_gbm`: Weights (ATT) after using Generalized Boosted Model to estimate propensity score
  - `eb_weights`: Weights (ATT) after Entropy Balancing
  - `invprob_weights`: Weights (ATT) after Inverse Probability Treatment Weighting using logistic regression

    data_with_weights <- read.csv("data_with_weights.csv")
    data_with_weights$cnsr <- 1 - data_with_weights$event
    data_with_weights <- data_with_weights[, sapply(data_with_weights, class) %in% c("numeric", "integer")]
    data_with_weights$ext <- 1 - data_with_weights$indicator #External control flag
    data_with_weights$norm.weights=data_with_weights$invprob_weights
    data_with_weights$norm.weights[data_with_weights$ext==1]=data_with_weights$norm.weights[data_with_weights$ext==1]/max(data_with_weights$norm.weights[data_with_weights$ext==1])
    data_with_weights <- as.matrix(data_with_weights)

The first six observations of the data set is shown below.

    head(data_with_weights)
    #>      X      Age   Weight   Height Biomarker1 Biomarker2 Smoker Sex ECOG1 group
    #> [1,] 1 56.63723 150.8678 65.79587   39.69306   47.71659      0   1     0     1
    #> [2,] 2 52.21456 145.0049 63.72807   29.83998   48.36954      1   1     1     1
    #> [3,] 3 53.46470 146.6449 63.73311   37.64033   48.64362      0   0     0     0
    #> [4,] 4 51.89528 147.6856 66.44799   41.08949   46.04826      1   1     1     1
    #> [5,] 5 52.17130 151.8084 66.16895   34.48727   51.26657      1   1     1     0
    #> [6,] 6 57.01979 144.6115 64.03365   34.82447   52.03498      0   1     0     0
    #>           time event indicator ratio1_caliper_weights_lps
    #> [1,] 0.4089043     0         1                          1
    #> [2,] 0.9721405     0         1                          0
    #> [3,] 0.2956919     0         1                          1
    #> [4,] 4.3111823     0         1                          1
    #> [5,] 0.2644848     0         1                          0
    #> [6,] 0.3103544     0         1                          1
    #>      ratio1_caliper_weights genetic_ratio1_weights
    #> [1,]                      1                      1
    #> [2,]                      0                      1
    #> [3,]                      1                      1
    #> [4,]                      1                      1
    #> [5,]                      0                      1
    #> [6,]                      0                      1
    #>      genetic_ratio1_weights_no_replace optimal_ratio1_weights weights_gbm
    #> [1,]                                 0                      1           1
    #> [2,]                                 1                      1           1
    #> [3,]                                 0                      1           1
    #> [4,]                                 0                      1           1
    #> [5,]                                 1                      1           1
    #> [6,]                                 1                      1           1
    #>      eb_weights invprob_weights cnsr ext norm.weights
    #> [1,]          1               1    1   0            1
    #> [2,]          1               1    1   0            1
    #> [3,]          1               1    1   0            1
    #> [4,]          1               1    1   0            1
    #> [5,]          1               1    1   0            1
    #> [6,]          1               1    1   0            1

# Exponential distribution with constant hazard with gamma prior distribution (Using `psborrow2` R package)

The `psborrow2` package will be used to conduct the analysis.

We use exponential distribution for the outcome model as follows. Non-informative normal prior is used for the baseline parameter. The data `data_with_weights` contain the time and censoring variable. For demonstration purpose, we choose the subject level weight `invprob_weights` calculated using inverse probability of treatment weighting (IPTW). In this step we extract the time, censoring indicator and the normalized weight variables denoted by `time`, `cnsr` and `norm.weights` respectively.

    #Outcome
    outcome <- outcome_surv_exponential(
      time_var = "time",
      cens_var = "cnsr",
      baseline_prior = prior_normal(0, 1000),
      weight_var = "norm.weights"
    )

    outcome
    #> Outcome object with class OutcomeSurvExponential
    #>
    #> Outcome variables:
    #>       time_var       cens_var     weight_var
    #>         "time"         "cnsr" "norm.weights"
    #>
    #> Baseline prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Next, the borrowing method is implemented as shown below. We consider Bayesian Dynamic Borrowing (BDB) in which gamma prior is assigned for the commensurability parameter. The `tau_prior` shown below is the hyperparameter of the commensurate prior which determines the degree of borrowing. We assign a gamma prior for this hyperparameter. Furhtermore, we also need to specify a flag for external data which is denoted by `ext`.

    #Borrowing
    borrowing <- borrowing_hierarchical_commensurate(
      ext_flag_col = "ext",
      tau_prior = prior_gamma(0.001, 0.001)
    )

    borrowing
    #> Borrowing object using the  Bayesian dynamic borrowing with the hierarchical commensurate prior  approach
    #>
    #> External control flag: ext
    #>
    #> Commensurability parameter prior:
    #> Gamma Distribution
    #> Parameters:
    #>  Stan  R     Value
    #>  alpha shape 0.001
    #>  beta  rate  0.001
    #> Constraints: <lower=0>

Similarly, details regarding the treatment variable is specified below. A Non-informative prior is used for the treatment effect parameter \(\gamma\).

    #Treatment
    treatment <- treatment_details(
      trt_flag_col = "group",
      trt_prior = prior_normal(0, 1000)
    )
    treatment
    #> Treatment object
    #>
    #> Treatment flag column: group
    #>
    #> Treatment effect prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Now, all the pieces are brought together to create the analysis object as shown below.

    #Application
    anls_obj <- create_analysis_obj(
      data_matrix = data_with_weights,
      outcome = outcome,
      borrowing = borrowing,
      treatment = treatment
    )
    #> Inputs look good.
    #> Stan program compiled successfully!
    #> Ready to go! Now call `mcmc_sample()`.

Finally, we run the MCMC sample for \(10,000\) iterations with \(3\) chains.

    niter = 10000
    results.exp.psborrow<- mcmc_sample(anls_obj,
      iter_warmup = round(niter/3),
      iter_sampling = niter,
      chains = 3,
      seed = 123
    )
    #> Running MCMC with 3 sequential chains...
    #>
    #> Chain 1 finished in 13.3 seconds.
    #> Chain 2 finished in 13.2 seconds.
    #> Chain 3 finished in 12.4 seconds.
    #>
    #> All 3 chains finished successfully.
    #> Mean chain execution time: 13.0 seconds.
    #> Total execution time: 39.2 seconds.

    draws1 <- results.exp.psborrow$draws()
    draws1 <- rename_draws_covariates(draws1, anls_obj)

The summary is shown below.

    results.exp.psborrow$summary()
    #> # A tibble: 6 × 10
    #>   variable     mean    median     sd    mad          q5      q95  rhat ess_bulk
    #>   <chr>       <dbl>     <dbl>  <dbl>  <dbl>       <dbl>    <dbl> <dbl>    <dbl>
    #> 1 lp__     -718.    -718.     1.51   1.29   -721.       -716.     1.00   10716.
    #> 2 beta_trt   -0.357   -0.359  0.110  0.111    -0.536      -0.175  1.00   11632.
    #> 3 tau         0.119    0.0505 0.183  0.0694    0.000443    0.470  1.00   12583.
    #> 4 alpha[1]   -0.621   -0.620  0.0910 0.0924   -0.772      -0.474  1.00   11977.
    #> 5 alpha[2]    2.36     2.38   0.374  0.368     1.70        2.93   1.00   18690.
    #> 6 HR_trt      0.704    0.699  0.0778 0.0773    0.585       0.839  1.00   11632.
    #> # ℹ 1 more variable: ess_tail <dbl>

The histogram of the posterior samples for hazard ratio and commensurability parameter as shown below.

    bayesplot::mcmc_hist(draws1, c("treatment HR"))

plot of chunk unnamed-chunk-10

    bayesplot::mcmc_hist(draws1, c("commensurability parameter"))

plot of chunk unnamed-chunk-10

    bayesplot::color_scheme_set("mix-blue-pink")

The \(95\%\) credible interval can be calculated as follows.

    summarize_draws(draws1, ~ quantile(.x, probs = c(0.025, 0.975)))
    #> # A tibble: 6 × 3
    #>   variable                                `2.5%`  `97.5%`
    #>   <chr>                                    <dbl>    <dbl>
    #> 1 lp__                               -722.       -716.
    #> 2 treatment log HR                     -0.569      -0.141
    #> 3 commensurability parameter            0.000109    0.628
    #> 4 baseline log hazard rate, internal   -0.801      -0.448
    #> 5 baseline log hazard rate, external    1.56        3.02
    #> 6 treatment HR                          0.566       0.869

We can graph other plots that help us evaluate convergence and diagnose problems with the MCMC sampler, such as trace plot.

    bayesplot::color_scheme_set("mix-blue-pink")

    bayesplot::mcmc_trace(
      draws1[1:(round(niter/2)), 1:2, ], # Using a subset of draws only
      pars = c("treatment HR", "commensurability parameter"),
      n_warmup = niter/10
    )

plot of chunk unnamed-chunk-12

# Time-to-event analysis with Weibull distribution and proportional hazards parametrization

We conduct analysis using a Weibull distribution for the outcome model using the `psborrow2` package as follows.

Non-informative normal prior is used for the baseline parameter \(\mu\). An exponential prior is used for the Weibull shape parameter \(\zeta\).

    #Outcome
    outcome <- outcome_surv_weibull_ph(
      time_var = "time",
      cens_var = "cnsr",
      shape_prior=prior_exponential(1),
      baseline_prior = prior_normal(0, 1000),
      weight_var = "norm.weights"
    )

    outcome
    #> Outcome object with class OutcomeSurvWeibullPH
    #>
    #> Outcome variables:
    #>       time_var       cens_var     weight_var
    #>         "time"         "cnsr" "norm.weights"
    #>
    #> Baseline prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000
    #>
    #> shape_weibull prior:
    #> Exponential Distribution
    #> Parameters:
    #>  Stan R    Value
    #>  beta rate 1
    #> Constraints: <lower=0>

Next, the borrowing method is implemented as shown below. We consider Bayesian Dynamic Borrowing (BDB) in which a non-informative gamma prior is assigned for the commensurability parameter.

    #Borrowing
    borrowing <- borrowing_hierarchical_commensurate(
      ext_flag_col = "ext",
      tau_prior = prior_gamma(0.001, 0.001)
    )

    borrowing
    #> Borrowing object using the  Bayesian dynamic borrowing with the hierarchical commensurate prior  approach
    #>
    #> External control flag: ext
    #>
    #> Commensurability parameter prior:
    #> Gamma Distribution
    #> Parameters:
    #>  Stan  R     Value
    #>  alpha shape 0.001
    #>  beta  rate  0.001
    #> Constraints: <lower=0>

Similarly, details regarding the treatment variable are specified below. Non-informative prior is used for treatment effect \(\gamma\).

    #Treatment
    treatment <- treatment_details(
      trt_flag_col = "group",
      trt_prior = prior_normal(0, 1000)
    )
    treatment
    #> Treatment object
    #>
    #> Treatment flag column: group
    #>
    #> Treatment effect prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Now, all the pieces are brought together to create the analysis object as shown below.

    #Application
    data_with_weights <- as.matrix(data_with_weights)
    anls_obj <- create_analysis_obj(
      data_matrix = data_with_weights,
      outcome = outcome,
      borrowing = borrowing,
      treatment = treatment
    )
    #> Inputs look good.
    #> Stan program compiled successfully!
    #> Ready to go! Now call `mcmc_sample()`.

Finally, we run the MCMC sample for \(10,000\) iterations with \(3\) chains.

    results.weib.psborrow<- mcmc_sample(anls_obj,
      iter_warmup = round(niter/3),
      iter_sampling = niter,
      chains = 3,
      seed = 123
    )
    #> Running MCMC with 3 sequential chains...
    #>
    #> Chain 1 finished in 29.3 seconds.
    #> Chain 2 finished in 29.0 seconds.
    #> Chain 3 finished in 31.5 seconds.
    #>
    #> All 3 chains finished successfully.
    #> Mean chain execution time: 30.0 seconds.
    #> Total execution time: 90.2 seconds.

    draws2 <- results.weib.psborrow$draws()
    draws2 <- rename_draws_covariates(draws2, anls_obj)

The summary is shown below.

    results.weib.psborrow$summary()
    #> # A tibble: 7 × 10
    #>   variable          mean   median     sd    mad       q5      q95  rhat ess_bulk
    #>   <chr>            <dbl>    <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
    #> 1 lp__          -708.    -7.07e+2 1.68   1.49   -7.11e+2 -706.     1.00   11530.
    #> 2 beta_trt        -0.302 -3.03e-1 0.110  0.109  -4.82e-1   -0.120  1.00   16857.
    #> 3 shape_weibull    0.836  8.35e-1 0.0322 0.0322  7.83e-1    0.890  1.00   21009.
    #> 4 tau              0.180  7.77e-2 0.288  0.106   6.77e-4    0.693  1.00   14413.
    #> 5 alpha[1]        -0.509 -5.08e-1 0.0931 0.0934 -6.62e-1   -0.358  1.00   16847.
    #> 6 alpha[2]         1.97   2.00e+0 0.386  0.380   1.29e+0    2.57   1.00   20959.
    #> 7 HR_trt           0.744  7.39e-1 0.0823 0.0804  6.17e-1    0.887  1.00   16857.
    #> # ℹ 1 more variable: ess_tail <dbl>

Histogram of the posterior samples for hazard ratio and commensurability parameter are produced.

    bayesplot::mcmc_hist(draws2, c("treatment HR"))

plot of chunk unnamed-chunk-19

    bayesplot::mcmc_hist(draws2, c("commensurability parameter"))

plot of chunk unnamed-chunk-19

    bayesplot::color_scheme_set("mix-blue-pink")

The \(95\%\) credible interval can be calculated as follows.

    summarize_draws(draws2, ~ quantile(.x, probs = c(0.025, 0.975)))
    #> # A tibble: 7 × 3
    #>   variable                                `2.5%`   `97.5%`
    #>   <chr>                                    <dbl>     <dbl>
    #> 1 lp__                               -712.       -705.
    #> 2 treatment log HR                     -0.515      -0.0855
    #> 3 Weibull shape parameter               0.774       0.900
    #> 4 commensurability parameter            0.000170    0.945
    #> 5 baseline log hazard rate, internal   -0.692      -0.331
    #> 6 baseline log hazard rate, external    1.15        2.66
    #> 7 treatment HR                          0.598       0.918

We can see other plots that help us evaluate convergence and diagnose problems with the MCMC sampler, such as trace plot.

    bayesplot::color_scheme_set("mix-blue-pink")

    bayesplot::mcmc_trace(
      draws2[1:(round(niter/2)), 1:2, ], # Using a subset of draws only
      pars = c("treatment HR", "commensurability parameter"),
      n_warmup = round(niter/10)
    )

plot of chunk unnamed-chunk-21

# Power prior with piecewise exponential distribution (not implemented in `psborrow2` R package)

We will consider the Bayesian power prior with subject specific power parameters. The weights from various matching and weighting methods can be used as subject specific power parameters for external control subjects. The baseline hazard function will be based on piecewise exponential distribution. The following JAGS code, adapted from (Alvares et al. 2021), implements the power prior with piecewise exponential baseline hazard. We will consider a subject specific power parameter as the weights from inverse probability weighting method. The subject specific weights for external control patients are normalized by dividing each weight by the maximum weight.

    model{
            #POWER PRIOR JAGS CODE
      #Trial Data
      for (i in 1:n){
        # Pieces of the cumulative hazard function
        for (k in 1:int.obs[i]) {
          cond[i,k] <- step(time[i] - a[k + 1])
          HH[i , k] <- cond[i,k] * (a[k + 1] - a[k]) * exp(alpha[k]) +
          (1 - cond[i,k] ) * (time[i] - a[k]) * exp(alpha[k])
        }
        # Cumulative hazard function
        H[i] <- sum(HH[i,1 : int.obs[i]] )
      }
      for ( i in 1:n) {
        # Linear predictor
        elinpred[i] <- exp(inprod(beta[],X[i,]))
        # Log-hazard function
        logHaz[i] <- log( exp(alpha[int.obs[i]]) * elinpred[i])
        # Log-survival function
        logSurv[i] <- -H[i]  * elinpred[i]
        # Definition of the log-likelihood using zeros trick
        phi[i] <- 100000 - status[i] * logHaz[i] - logSurv[i]
        zeros[i] ~ dpois(phi[i])
      }

      #Real World or External Control Data
       for (i in 1:nR){
        # Pieces of the cumulative hazard function
        for (k in 1:int.obsR[i]) {
          condR[i,k] <- step(timeR[i] - a[k + 1])
          HHR[i , k] <- condR[i,k] * (a[k + 1] - a[k]) * exp(alpha[k]) +
          (1 - condR[i,k] ) * (timeR[i] - a[k]) * exp(alpha[k])
        }
        # Cumulative hazard function
        HR[i] <- sum(HHR[i,1 : int.obsR[i]] )
      }
      for ( i in 1:nR) {
        # Linear predictor
        elinpredR[i] <- exp(inprod(beta[],XR[i,]))
        # Log-hazard function
        logHazR[i] <- log(exp(alpha[int.obsR[i]]) * elinpredR[i])
        # Log-survival function
        logSurvR[i] <- -HR[i] * elinpredR[i]
        # Definition of the log-likelihood using zeros trick
        phiR[i] <- 100000 - rho[i] * statusR[i] * logHazR[i] - rho[i] * logSurvR[i]
        #rho[i] is the subject level weight as a power prior
        zerosR[i] ~ dpois(phiR[i])
      }
      # Prior distributions
      for(l in 1: Nbetas ){
        beta[l] ~ dnorm(0 , 0.001)
      }

      for( k in 1 :m) {
      alpha[k] ~ dnorm(0,0.001)
      }

    }

We now define all the variables as in input to JAGS.

    data_with_weights=data.frame(data_with_weights)

    #Trial Data
    trial.data <- data_with_weights[data_with_weights$indicator==1,]

    #External Control Data
    ext.control.data <- data_with_weights[data_with_weights$indicator==0,]

    #Input in JAGS code
    n <- nrow(trial.data) #Sample size in trial data
    nR <- nrow(ext.control.data) #Sample size in external control data

    time <- trial.data$time #Survival time in trial data
    timeR <- ext.control.data$time #Survival time in external control data

    status <- trial.data$event #Event indicator in trial data
    statusR <- ext.control.data$event #Event indicator in external control data

    rho <- ext.control.data$invprob_weights/max(ext.control.data$invprob_weights) #Power prior weights

    X <- as.matrix(trial.data[,10]) #Covariates in trial data
    XR <- as.matrix(ext.control.data[,10]) #Covariates in external control data

    Nbetas <- ncol(X)

    zeros = rep(0, n)
    zerosR = rep(0, nR)

We also need to pre-specify the number of intervals. For simplicity, we will consider \(K=1\) interval. However, this method also works \(K>1\).

    # Time axis partition
    K <- 1 # number of intervals

    #Cut points (Using the event times in trial data)
    a=c(0,quantile(trial.data$time[trial.data$event==1],seq(0,1,by=1/K))[-c(1,K+1)],
        max(c(trial.data$time,ext.control.data$time))+0.0001)

    #Trial data
    # int.obs: vector that tells us at which interval each observation is
    int.obs <- matrix(data = NA, nrow = nrow(trial.data), ncol = length(a) - 1)
    d <- matrix(data = NA, nrow = nrow(trial.data), ncol = length(a) - 1)
    for(i in 1:nrow(trial.data)) {
      for(k in 1:(length(a) - 1)) {
        d[i, k] <- ifelse(trial.data$time[i] - a[k] > 0, 1, 0) *
          ifelse(a[k + 1] - trial.data$time[i] > 0, 1,0)
        int.obs[i, k] <- d[i, k] * k
      }
    }
    int.obs <- rowSums(int.obs)

    #External control data
    # int.obs: vector that tells us at which interval each observation is
    int.obsR <- matrix(data = NA, nrow = nrow(ext.control.data), ncol = length(a) - 1)
    d <- matrix(data = NA, nrow = nrow(ext.control.data), ncol = length(a) - 1)
    for(i in 1:nrow(ext.control.data)) {
      for(k in 1:(length(a) - 1)) {
        d[i, k] <- ifelse(ext.control.data$time[i] - a[k] > 0, 1, 0) *
          ifelse(a[k + 1] - ext.control.data$time[i] > 0, 1,0)
        int.obsR[i, k] <- d[i, k] * k
      }
    }
    int.obsR <- rowSums(int.obsR)

We now put all variables into a list as data inputs for JAGs.

    ### JAGS ####
    d.jags <- list("n", "nR", "time", "timeR",
                   "a", "X", "XR","int.obs",
                   "int.obsR","Nbetas",
                   "zeros","zerosR","rho",
                   "status", "statusR","K")

We specify the parameter of interest as follows.

    #Parameter of interest
    p.jags <- c("beta","alpha")

The standard normal distribution is used as an initial values for the parameter of interest.

    #Initial values for each parameter
    i.jags <- function(){
      list(beta = rnorm(ncol(X)), alpha = rnorm(K))
    }

Now, we call the `jags` function to conduct MCMC sampling with \(3\) chains as shown below. We set number of iterations as \(niter=10,000\) with a burn in of \(5000\) and thinning of \(10\).

    set.seed(1)
    model1 <- jags(data = d.jags, model.file = "powerprior.txt", inits = i.jags, n.chains = 3,
                   parameters=p.jags,n.iter=niter,n.burnin = round(niter/2),n.thin = 10)
    #> Compiling model graph
    #>    Resolving undeclared variables
    #>    Allocating nodes
    #> Graph information:
    #>    Observed stochastic nodes: 1100
    #>    Unobserved stochastic nodes: 2
    #>    Total graph size: 17432
    #>
    #> Initializing model

The summary can be produced using the following command.

    model1$BUGSoutput$summary
    #>                   mean         sd          2.5%           25%           50%
    #> alpha    -5.566809e-01 0.08954522 -7.394070e-01 -6.170039e-01 -5.543440e-01
    #> beta     -4.200419e-01 0.10776354 -6.340181e-01 -4.921314e-01 -4.202084e-01
    #> deviance  2.200015e+08 1.99156129  2.200015e+08  2.200015e+08  2.200015e+08
    #>                    75%         97.5%     Rhat n.eff
    #> alpha    -4.952955e-01 -3.884554e-01 1.000476  1500
    #> beta     -3.479730e-01 -2.076608e-01 1.000497  1500
    #> deviance  2.200015e+08  2.200015e+08 1.000000     1

The summary of the hazard ratio for treatment variable is shown below.

    c(summary(exp(model1$BUGSoutput$sims.list$beta[,1])),
      quantile(exp(model1$BUGSoutput$sims.list$beta[,1]),probs=c(0.025,0.975)))
    #>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      2.5%     97.5%
    #> 0.4931593 0.6113220 0.6569099 0.6608469 0.7061179 0.9601307 0.5304561 0.8124826

The histogram of the treatment hazard ratio can also be produced.

    hist(exp(model1$BUGSoutput$sims.list$beta[,1]),xlab="treatmentHR",main="",ylab="")

plot of chunk unnamed-chunk-31

The traceplot can be generated as follows.

    traceplot(model1,varname=c("alpha","beta"))

plot of chunk unnamed-chunk-32

plot of chunk unnamed-chunk-32

# Commensurate prior with piecewise exponential distribution (not implemented in `psborrow2` R package)

We will consider the Bayesian commensurate prior model.

    model{
      #Trial Data
      for (i in 1:n){
        # Pieces of the cumulative hazard function
        for (k in 1:int.obs[i]) {
          cond[i,k] <- step(time[i] - a[k + 1])
          HH[i , k] <- cond[i,k] * (a[k + 1] - a[k]) * exp(alpha[k]) +
          (1 - cond[i,k] ) * (time[i] - a[k]) * exp(alpha[k])
        }
        # Cumulative hazard function
        H[i] <- sum(HH[i,1 : int.obs[i]] )
      }
      for ( i in 1:n) {
        # Linear predictor
        elinpred[i] <- exp(inprod(beta[],X[i,]))
        # Log-hazard function
        logHaz[i] <- log(exp(alpha[int.obs[i]]) * elinpred[i])
        # Log-survival function
        logSurv[i] <- -H[i] * elinpred[i]
        # Definition of the log-likelihood using zeros trick
        phi[i] <- 100000 - wt[i]*status[i] * logHaz[i] - wt[i]*logSurv[i]
        zeros[i] ~ dpois(phi[i])
      }

      #Real World or External Control Data
       for (i in 1:nR){
        # Pieces of the cumulative hazard function
        for (k in 1:int.obsR[i]) {
          condE[i,k] <- step(timeR[i] - a[k + 1])
          HHE[i , k] <- condE[i,k] * (a[k + 1] - a[k]) * exp(alphaR[k]) +
          (1 - condE[i,k] ) * (timeR[i] - a[k]) * exp(alphaR[k])
        }
        # Cumulative hazard function
        HE[i] <- sum(HHE[i,1 : int.obsR[i]] )
      }
      for ( i in 1:nR) {
        # Linear predictor
        elinpredE[i] <- exp(inprod(beta0[],XR[i,]))
        # Log-hazard function
        logHazE[i] <- log(exp(alphaR[int.obsR[i]]) * elinpredE[i])
        # Log-survival function
        logSurvE[i] <- -HE[i] * elinpredE[i]
        # Definition of the log-likelihood using zeros trick
        phiE[i] <- 100000 - wtR[i]*statusR[i] * logHazE[i] - wtR[i] * logSurvE[i]
        zerosR[i] ~ dpois(phiE[i])
      }

      # Commensurate prior on the covariate effect
      for(l in 1: Nbetas ){
        beta0[l] ~ dnorm(0,0.0001);
        tau[l] ~ dgamma(0.01,0.01);
        beta[l] ~ dnorm(beta0[l],tau[l]);
      }

      # Normal prior on the piecewise exponential parameters for each interval
      for( m in 1 : K) {
      taualpha[m] ~ dgamma(0.01,0.01);
      alpha[m] ~ dnorm(alphaR[m],taualpha[m]);
      alphaR[m] ~ dnorm(0,0.0001);
      }

    }

We now define all the variables as in input to JAGS.

    data_with_weights=data.frame(data_with_weights)

    #Trial Data
    trial.data <- data_with_weights[data_with_weights$indicator==1,]

    #External Control Data
    ext.control.data <- data_with_weights[data_with_weights$indicator==0,]

    #Input in JAGS code
    n <- nrow(trial.data) #Sample size in trial data
    nR <- nrow(ext.control.data) #Sample size in external control data

    time <- trial.data$time #Survival time in trial data
    timeR <- ext.control.data$time #Survival time in external control data

    status <- trial.data$event #Event indicator in trial data
    statusR <- ext.control.data$event #Event indicator in external control data

    wt <- trial.data$norm.weights #Vector of weights in trial data
    wtR <- ext.control.data$norm.weights #Vector of weights in external control data

    X <- as.matrix(trial.data[,10]) #Treatment indicator in trial data
    XR <- as.matrix(ext.control.data[,10]) #Treatment indicator in trial data

    Nbetas <- ncol(X)

    zeros = rep(0, n)
    zerosR = rep(0, nR)

We also need to pre-specify the number of intervals. For simplicity, we will consider \(K=1\) interval. However, this method also works when \(K>1\).

    # Time axis partition
    K <- 1 # number of intervals

    #Cut points (Using the event times in trial data)
    a=c(0,quantile(trial.data$time[trial.data$event==1],seq(0,1,by=1/K))[-c(1,K+1)],
        max(c(trial.data$time,ext.control.data$time))+0.0001)

    #Trial data
    # int.obs: vector that tells us at which interval each observation is
    int.obs <- matrix(data = NA, nrow = nrow(trial.data), ncol = length(a) - 1)
    d <- matrix(data = NA, nrow = nrow(trial.data), ncol = length(a) - 1)
    for(i in 1:nrow(trial.data)) {
      for(k in 1:(length(a) - 1)) {
        d[i, k] <- ifelse(trial.data$time[i] - a[k] > 0, 1, 0) *
          ifelse(a[k + 1] - trial.data$time[i] > 0, 1, 0)
        int.obs[i, k] <- d[i, k] * k
      }
    }
    int.obs <- rowSums(int.obs)

    #External control data
    # int.obs: vector that tells us at which interval each observation is
    int.obsR <- matrix(data = NA, nrow = nrow(ext.control.data), ncol = length(a) - 1)
    d <- matrix(data = NA, nrow = nrow(ext.control.data), ncol = length(a) - 1)
    for(i in 1:nrow(ext.control.data)) {
      for(k in 1:(length(a) - 1)) {
        d[i, k] <- ifelse(ext.control.data$time[i] - a[k] > 0, 1, 0) *
          ifelse(a[k + 1] - ext.control.data$time[i] > 0, 1,0)
        int.obsR[i, k] <- d[i, k] * k
      }
    }
    int.obsR <- rowSums(int.obsR)

We now put all variables into a list as data inputs for JAGs. Note that the letter \(R\) in the following corresponds to RWD. For example, \(XR\) is the covariate matrix in RWD.

    ### JAGS ####
    d.jags <- list("n", "nR", "time", "timeR",
                   "a", "X", "XR","int.obs",
                   "int.obsR","Nbetas",
                   "zeros","zerosR","wt", "wtR",
                   "status", "statusR","K")

We specify the parameter of interest as follows. Note that `alphaR` is the baseline hazard parameter for external control.

    #Parameter of interest
    p.jags <- c("beta","alpha","alphaR","tau")

We generate initial values for \(\beta\) and \(\alpha\) from standard normal distributions, and \(\tau\) from a non-informative Gamma distribution.

    #Initial values for each parameter
    i.jags <- function(){
      list(beta = rnorm(ncol(X)), beta0 = rnorm(ncol(X)),
           alpha = rnorm(K),alphaR = rnorm(K),tau = rgamma(ncol(X),shape = 0.01,scale=0.01))
    }

Now, we call the `jags` function to conduct MCMC sampling with \(3\) chains as shown below. We set number of iterations as \(niter=10,000\) with a burn in of \(5000\) and thinning of \(10\).

    set.seed(1)
    model2 <- jags(data = d.jags, model.file = "commensurateprior.txt", inits = i.jags, n.chains = 3,
                   parameters=p.jags,n.iter=niter,n.burnin = round(niter/2),n.thin = 10)
    #> Compiling model graph
    #>    Resolving undeclared variables
    #>    Allocating nodes
    #> Graph information:
    #>    Observed stochastic nodes: 1100
    #>    Unobserved stochastic nodes: 5
    #>    Total graph size: 18642
    #>
    #> Initializing model

The summary can be produced using the following command.

    model2$BUGSoutput$summary
    #>                   mean         sd          2.5%           25%           50%
    #> alpha    -6.191206e-01 0.08842641 -7.909870e-01 -6.787401e-01 -6.193462e-01
    #> alphaR    2.366979e+00 0.36366809  1.593958e+00  2.144550e+00  2.379864e+00
    #> beta     -3.596931e-01 0.10541071 -5.626927e-01 -4.319852e-01 -3.571647e-01
    #> deviance  2.200014e+08 2.44607662  2.200014e+08  2.200014e+08  2.200014e+08
    #> tau       1.254783e-01 0.18316338  4.693678e-04  1.536327e-02  5.811439e-02
    #>                    75%         97.5%     Rhat n.eff
    #> alpha    -5.575406e-01 -4.477827e-01 1.001772  1100
    #> alphaR    2.627147e+00  3.007402e+00 1.004616   440
    #> beta     -2.884953e-01 -1.600480e-01 1.000624  1500
    #> deviance  2.200014e+08  2.200014e+08 1.000000     1
    #> tau       1.585465e-01  6.215560e-01 1.001367  1500

The summary of the hazard ratio for treatment variable is shown below.

    c(summary(exp(model2$BUGSoutput$sims.list$beta[,1])),
      quantile(exp(model2$BUGSoutput$sims.list$beta[,1]),probs=c(0.025,0.975)))
    #>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      2.5%     97.5%
    #> 0.4932769 0.6492190 0.6996573 0.7017649 0.7493903 0.9788045 0.5696732 0.8521031

The histogram of the treatment hazard ratio can also be produced.

    hist(exp(model2$BUGSoutput$sims.list$beta[,1]),xlab="treatmentHR",main="",ylab="")

plot of chunk unnamed-chunk-42

The histogram of the commensurability parameter can also be produced.

    hist(exp(model2$BUGSoutput$sims.list$tau[,1]),xlab="Commensurability Parameter",main="",ylab="")

plot of chunk unnamed-chunk-43

The traceplot can be created as follows.

    traceplot(model2,varname=c("alpha","alphaR","beta","tau"))

plot of chunk unnamed-chunk-44

plot of chunk unnamed-chunk-44

plot of chunk unnamed-chunk-44

plot of chunk unnamed-chunk-44

# Exponential distribution (constant hazard) and no borrowing: Using `psborrow2` R package

In addition to specifying an exponential distribution using Bayesian dynamic borrowing, naive no-borrowing and full-borrowing analysis can be conducted using the exponential distribution following similar steps as above. These ype of analysis could be used as a sensitivity or exploratory analysis.

The `psborrow2` package will be used to conduct the analysis. Following are the steps.

We use exponential distribution for the outcome model as follows. Non-informative normal prior is used for the baseline parameter.

    #Outcome
    outcome <- outcome_surv_exponential(
      time_var = "time",
      cens_var = "cnsr",
      baseline_prior = prior_normal(0, 1000)
    )

    outcome
    #> Outcome object with class OutcomeSurvExponential
    #>
    #> Outcome variables:
    #> time_var cens_var
    #>   "time"   "cnsr"
    #>
    #> Baseline prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Next, the borrowing method is implemented as shown below. We consider no borrowing as shown below.

    #Borrowing
    borrowing <- borrowing_none(
      ext_flag_col = "ext"
    )

    borrowing
    #> Borrowing object using the  No borrowing  approach
    #>
    #> External control flag: ext

Similarly, details regarding the treatment variable is specified below. Non-informative prior is used a prior for treatment effect.

    #Treatment
    treatment <- treatment_details(
      trt_flag_col = "group",
      trt_prior = prior_normal(0, 1000)
    )
    treatment
    #> Treatment object
    #>
    #> Treatment flag column: group
    #>
    #> Treatment effect prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Now, all the pieces are brought together to create the analysis object as shown below.

    data_with_weights <- as.matrix(data_with_weights)
    #Application
    anls_obj <- create_analysis_obj(
      data_matrix = data_with_weights,
      outcome = outcome,
      borrowing = borrowing,
      treatment = treatment
    )
    #> Inputs look good.
    #> NOTE: excluding `ext` == `1`/`TRUE` for no borrowing.
    #> Stan program compiled successfully!
    #> Ready to go! Now call `mcmc_sample()`.

Finally, we run the MCMC sample with \(3\) chains.

    results.no.psborrow<- mcmc_sample(anls_obj,
      iter_warmup = round(niter/3),
      iter_sampling = niter,
      chains = 3,
      seed = 123
    )
    #> Running MCMC with 3 sequential chains...
    #>
    #> Chain 1 finished in 4.0 seconds.
    #> Chain 2 finished in 4.1 seconds.
    #> Chain 3 finished in 4.2 seconds.
    #>
    #> All 3 chains finished successfully.
    #> Mean chain execution time: 4.1 seconds.
    #> Total execution time: 12.7 seconds.

    draws3 <- results.no.psborrow$draws()
    draws3 <- rename_draws_covariates(draws3, anls_obj)

The summary is shown below.

    results.no.psborrow$summary()
    #> # A tibble: 4 × 10
    #>   variable     mean   median     sd    mad       q5      q95  rhat ess_bulk
    #>   <chr>       <dbl>    <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
    #> 1 lp__     -727.    -727.    0.983  0.706  -729.    -726.     1.00   10417.
    #> 2 beta_trt   -0.355   -0.356 0.109  0.109    -0.534   -0.174  1.00    8852.
    #> 3 alpha      -0.623   -0.622 0.0910 0.0901   -0.776   -0.477  1.00    8695.
    #> 4 HR_trt      0.705    0.700 0.0773 0.0757    0.586    0.840  1.00    8852.
    #> # ℹ 1 more variable: ess_tail <dbl>

The histogram of the posterior samples for hazard ratio and commensurability parameter as shown below.

    bayesplot::mcmc_hist(draws3, c("treatment HR"))

plot of chunk unnamed-chunk-51

    bayesplot::color_scheme_set("mix-blue-pink")

The \(95\%\) credible interval can be calculated as follows.

    summarize_draws(draws3, ~ quantile(.x, probs = c(0.025, 0.975)))
    #> # A tibble: 4 × 3
    #>   variable                   `2.5%`  `97.5%`
    #>   <chr>                       <dbl>    <dbl>
    #> 1 lp__                     -730.    -726.
    #> 2 treatment log HR           -0.568   -0.139
    #> 3 baseline log hazard rate   -0.806   -0.451
    #> 4 treatment HR                0.567    0.870

We can see other plots that help us understand and diagnose problems with the MCMC sampler, such as trace plot.

    bayesplot::color_scheme_set("mix-blue-pink")

    bayesplot::mcmc_trace(
      draws3[1:(round(niter/2)), 1:2, ], # Using a subset of draws only
      pars = c("treatment HR"),
      n_warmup = round(niter/10)
    )

plot of chunk unnamed-chunk-53

# Exponential distribution (constant hazard) and full borrowing: Using `psborrow2` R package

The `psborrow2` package will be used to conduct the analysis. Following are the steps.

We use exponential distribution for the outcome model as follows. Non-informative normal prior is used for the baseline parameter.

    #Outcome
    outcome <- outcome_surv_exponential(
      time_var = "time",
      cens_var = "cnsr",
      baseline_prior = prior_normal(0, 1000)
    )

    outcome
    #> Outcome object with class OutcomeSurvExponential
    #>
    #> Outcome variables:
    #> time_var cens_var
    #>   "time"   "cnsr"
    #>
    #> Baseline prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Next, the borrowing method is implemented as shown below. We consider full borrowing as shown below.

    #Borrowing
    borrowing <- borrowing_full(ext_flag_col = "ext")

    borrowing
    #> Borrowing object using the  Full borrowing  approach
    #>
    #> External control flag: ext

Similarly, details regarding the treatment variable is specified below. Non-informative prior is used a prior for treatment effect.

    #Treatment
    treatment <- treatment_details(
      trt_flag_col = "group",
      trt_prior = prior_normal(0, 1000)
    )
    treatment
    #> Treatment object
    #>
    #> Treatment flag column: group
    #>
    #> Treatment effect prior:
    #> Normal Distribution
    #> Parameters:
    #>  Stan  R    Value
    #>  mu    mean    0
    #>  sigma sd   1000

Now, all the pieces are brought together to create the analysis object as shown below.

    #Application
    anls_obj <- create_analysis_obj(
      data_matrix = data_with_weights,
      outcome = outcome,
      borrowing = borrowing,
      treatment = treatment
    )
    #> Inputs look good.
    #> NOTE: dropping column `ext` for full borrowing.
    #> Stan program compiled successfully!
    #> Ready to go! Now call `mcmc_sample()`.

Finally, we run the MCMC sample with \(3\) chains.

    results.full.psborrow<- mcmc_sample(anls_obj,
      iter_warmup = round(niter/3),
      iter_sampling = niter,
      chains = 3,
      seed = 123
    )
    #> Running MCMC with 3 sequential chains...
    #>
    #> Chain 1 finished in 5.8 seconds.
    #> Chain 2 finished in 5.7 seconds.
    #> Chain 3 finished in 5.9 seconds.
    #>
    #> All 3 chains finished successfully.
    #> Mean chain execution time: 5.8 seconds.
    #> Total execution time: 17.9 seconds.

    draws4 <- results.full.psborrow$draws()
    draws4 <- rename_draws_covariates(draws4, anls_obj)

The summary is shown below.

    results.full.psborrow$summary()
    #> # A tibble: 4 × 10
    #>   variable     mean   median     sd    mad       q5      q95  rhat ess_bulk
    #>   <chr>       <dbl>    <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
    #> 1 lp__     -747.    -747.    1.01   0.710  -749.    -746.     1.00   13060.
    #> 2 beta_trt   -1.51    -1.51  0.0765 0.0762   -1.64    -1.39   1.00   10894.
    #> 3 alpha       0.534    0.535 0.0467 0.0463    0.458    0.610  1.00   10829.
    #> 4 HR_trt      0.221    0.220 0.0169 0.0168    0.194    0.250  1.00   10894.
    #> # ℹ 1 more variable: ess_tail <dbl>

The histogram of the posterior samples for hazard ratio and commensurability parameter as shown below.

    bayesplot::mcmc_hist(draws4, c("treatment HR"))

plot of chunk unnamed-chunk-60

    bayesplot::color_scheme_set("mix-blue-pink")

The \(95\%\) credible interval can be calculated as follows.

    summarize_draws(draws4, ~ quantile(.x, probs = c(0.025, 0.975)))
    #> # A tibble: 4 × 3
    #>   variable                   `2.5%`  `97.5%`
    #>   <chr>                       <dbl>    <dbl>
    #> 1 lp__                     -750.    -746.
    #> 2 treatment log HR           -1.66    -1.36
    #> 3 baseline log hazard rate    0.442    0.625
    #> 4 treatment HR                0.190    0.256

We can see other plots that help us understand and diagnose problems with the MCMC sampler, such as trace plot.

    bayesplot::color_scheme_set("mix-blue-pink")

    bayesplot::mcmc_trace(
      draws4[1:(round(niter/2)), 1:2, ], # Using a subset of draws only
      pars = c("treatment HR"),
      n_warmup = round(niter/10)
    )

plot of chunk unnamed-chunk-62

# Cox proportional hazards model using frequentist approach (No borrowing)

Now, we also demonstrate how researchers would fit a frequentist Cox model. For the first model, we will not borrow any information from external controls and use the trial data only.

    data_with_weights = data.frame(data_with_weights)
    f1 <- coxph(Surv(time,event)~group, data=data_with_weights[data_with_weights$ext==0,])
    summary(f1)
    #> Call:
    #> coxph(formula = Surv(time, event) ~ group, data = data_with_weights[data_with_weights$ext ==
    #>     0, ])
    #>
    #>   n= 600, number of events= 389
    #>
    #>          coef exp(coef) se(coef)      z Pr(>|z|)
    #> group -0.2540    0.7757   0.1105 -2.298   0.0216 *
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #>       exp(coef) exp(-coef) lower .95 upper .95
    #> group    0.7757      1.289    0.6246    0.9633
    #>
    #> Concordance= 0.527  (se = 0.013 )
    #> Likelihood ratio test= 5.1  on 1 df,   p=0.02
    #> Wald test            = 5.28  on 1 df,   p=0.02
    #> Score (logrank) test = 5.31  on 1 df,   p=0.02

# Cox proportional hazards model using frequentist approach (Full borrowing from external control)

A frequentist Cox model can also be fitted using all of the external control data via full borrowing.

    f2 <- coxph(Surv(time,event)~group, data=data_with_weights)
    summary(f2)
    #> Call:
    #> coxph(formula = Surv(time, event) ~ group, data = data_with_weights)
    #>
    #>   n= 1100, number of events= 727
    #>
    #>           coef exp(coef) se(coef)      z Pr(>|z|)
    #> group -1.10052   0.33270  0.07978 -13.79   <2e-16 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    #>
    #>       exp(coef) exp(-coef) lower .95 upper .95
    #> group    0.3327      3.006    0.2845     0.389
    #>
    #> Concordance= 0.664  (se = 0.007 )
    #> Likelihood ratio test= 198.5  on 1 df,   p=<2e-16
    #> Wald test            = 190.3  on 1 df,   p=<2e-16
    #> Score (logrank) test = 205.9  on 1 df,   p=<2e-16

# Summarizing all the results together

Now, we concatenate the results from all of the Bayesian and frequentist analysis approaches together.

    hr1=results.exp.psborrow$summary()[6,c(2)]
    hr1conf = summarize_draws(draws1, ~ quantile(.x, probs = c(0.025, 0.975)))[6,c(2,3)]
    hr1.conf=c(hr1$mean,hr1conf$`2.5%`,hr1conf$`97.5%`)

    hr2=c(mean(exp(model1$BUGSoutput$sims.list$beta[,1])),
      quantile(exp(model1$BUGSoutput$sims.list$beta[,1]),probs=c(0.025,0.975)))
    hr2.conf=c(hr2)

    hr3=results.weib.psborrow$summary()[7,c(2)]
    hr3conf = summarize_draws(draws2, ~ quantile(.x, probs = c(0.025, 0.975)))[7,c(2,3)]
    hr3.conf=c(hr3$mean,hr3conf$`2.5%`,hr3conf$`97.5%`)

    hr4=c(mean(exp(model2$BUGSoutput$sims.list$beta[,1])),
      quantile(exp(model2$BUGSoutput$sims.list$beta[,1]),probs=c(0.025,0.975)))
    hr4.conf=c(hr4)

    hr5=results.no.psborrow$summary()[4,c(2)]
    hr5conf = summarize_draws(draws3, ~ quantile(.x, probs = c(0.025, 0.975)))[4,c(2,3)]
    hr5.conf=c(hr5$mean,hr5conf$`2.5%`,hr5conf$`97.5%`)

    hr6=results.full.psborrow$summary()[4,c(2)]
    hr6conf = summarize_draws(draws4, ~ quantile(.x, probs = c(0.025, 0.975)))[4,c(2,3)]
    hr6.conf=c(hr6$mean,hr6conf$`2.5%`,hr6conf$`97.5%`)

    hr7.conf=c(exp(coef(f1)),exp(confint(f1)))

    hr8.conf=c(exp(coef(f2)),exp(confint(f2)))

    out = round(rbind(hr1.conf,hr2.conf,hr3.conf,hr4.conf,hr5.conf,hr6.conf,hr7.conf,hr8.conf),4)

    rownames(out)= NULL
    out = data.frame(Method=c("Exponential distribution (constant hazard) and gamma prior",
                              "Piecewise exponential distribution (proportional hazard) and power prior",
                              "Weibull distribution (proportional hazard) and gamma prior",
                              "Piecewise exponential distribution (proportional hazard) and commensurate prior",
                              "Exponential distribution (constant hazard): No borrowing",
                              "Exponential distribution (constant hazard): Full borrowing",
                              "Cox model (Frequentist approach): No borrowing",
                              "Cox model (Frequentist approach): Full borrowing"),out)
    colnames(out)[2:4] = c("Hazard Ratio","Lower 95% CI","Upper 95% CI")

The hazard ratio estimates and \(95\%\) credible intervals for all the methods are shown below.

Method | Hazard Ratio | Lower 95% CI | Upper 95% CI
--- | --- | --- | ---
Exponential distribution (constant hazard) and gamma prior | 0.7040 | 0.5660 | 0.8688
Piecewise exponential distribution (proportional hazard) and power prior | 0.6608 | 0.5305 | 0.8125
Weibull distribution (proportional hazard) and gamma prior | 0.7437 | 0.5978 | 0.9180
Piecewise exponential distribution (proportional hazard) and commensurate prior | 0.7018 | 0.5697 | 0.8521
Exponential distribution (constant hazard): No borrowing | 0.7054 | 0.5666 | 0.8699
Exponential distribution (constant hazard): Full borrowing | 0.2209 | 0.1896 | 0.2560
Cox model (Frequentist approach): No borrowing | 0.7757 | 0.6246 | 0.9633
Cox model (Frequentist approach): Full borrowing | 0.3327 | 0.2845 | 0.3890

Note: CI: Credible Interval for Bayesian methods and Confidence interval for Frequentist method

# References

Alvares, Danilo, Elena Lázaro, Virgilio Gómez-Rubio, and Carmen Armero. 2021. “Bayesian Survival Analysis with BUGS.” Statistics in Medicine 40 (12): 2975–3020.
Cox, David R. 1972. “Regression Models and Life-Tables.” Journal of the Royal Statistical Society: Series B (Methodological) 34 (2): 187–202.
Ibrahim, Joseph G, Ming-Hui Chen, Yeongjin Gwon, and Fang Chen. 2015. “The Power Prior: Theory and Applications.” Statistics in Medicine 34 (28): 3724–49.
Isaac Gravestock, Matthew H Secrest. 2024. “Psborrow2: An r Package for Bayesian Dynamic Borrowing Simulation Study and Analysis.” https://github.com/Genentech/psborrow2.
Khanal, Manoj. 2022. “Semiparametric Regression Models for Clustered Right-Censored Data Under Clinical Trials and Observational Studies.” PhD thesis, The Medical College of Wisconsin.
Lázaro, Elena, Carmen Armero, and Danilo Alvares. 2021. “Bayesian Regularization for Flexible Baseline Hazard Functions in Cox Survival Models.” Biometrical Journal 63 (1): 7–26.
Murray, Thomas A, Brian P Hobbs, Theodore C Lystig, and Bradley P Carlin. 2014. “Semiparametric Bayesian Commensurate Survival Model for Post-Market Medical Device Surveillance with Non-Exchangeable Historical Data.” Biometrics 70 (1): 185–91.