This `psborrow`

package aims to evaluate the effect of external borrowing using an integrated approach that combines propensity score and Bayesian dynamic borrowing methods. Users are encouraged to check the helper page for each function to understand the requirements for parameter inputs and the format of the outputs.

Here we demonstrate the usage of the package with an example. The goal is to:

- Simulate covariates for external control, internal control, and internal treatment arms
- Generate censored survival times
- Run Markov chain Monte Carlo (MCMC) to generate the posterior distribution for Bayesian hierarchical model
- Summarize posterior distributions for treatment effect estimate

`library(psborrow)`

We start with making the sample size for internal control, internal treatment, and external control arms to be 200, 180 and 400 respectively.

`= set_n(ssC = 200, ssE = 180, ssExt = 400) ss `

We call function `set_cov()`

to create two binary covariates \(x_1\), \(x_2\), and one continuous covariate \(x_3\) from the following multivariate normal distribution with covariance between each pair of the variables specified by `cov`

in the order of \(Cov(x_1, x_2)\), \(Cov(x_1, x_3)\), and \(Cov(x_2, x_3)\).

\[\begin{aligned} \begin{bmatrix} x_1\\x_2\\x_3\\ \end{bmatrix} &\sim N \left( \begin{bmatrix} 0\\ 0.5\\0.5 \end{bmatrix}, \begin{bmatrix} 1 & 0.5 & 0.7\\ 0.5 & 1.2 & 0.9\\ 0.7 & 0.9 & 1\\ \end{bmatrix} \right) \end{aligned}\]

```
= set_cov(n_cat = 2, n_cont = 1,
covset1 mu_int = c(0, 0.5, 0.5), mu_ext = c(0.7, 0.5, 0.9),
var = c(1, 1.2, 1), cov = c(0.5, 0.7, 0.9),
prob_int = c(0.45, 0.55), prob_ext = c(0.65, 0.55))
```

\(x_1\) and \(x_2\) are further converted to binary variables \(x_1\) and \(x_2\) using quantiles of the normal distribution calculated from the probability specified by `prob_int`

for internal trial and by `prob_ext`

for external trial:

\[\begin{aligned} \text{internal: Pr} (X_1 = 1) &= 0.45 \\ \text{Pr}(X_2 = 1) &= 0.55 \\ \text{external: Pr} (X_1 = 1) &= 0.65\\ P(X_2 = 1) &= 0.55\\ \end{aligned}\]

The package enables the user to add covariates in batch. `covset2`

includes information for two continuous covariates and three binary covariates. We set `mu_int = 0`

to have the mean for all five variables in this cluster to be 0 in the internal trial. The same rule applies to parameter `mu_ext`

, `var`

, `cov`

, `prob_int`

and `prob_ext`

.

```
= set_cov(n_cat = 2, n_cont = 3,
covset2 mu_int = 0, mu_ext = 0.2, var = 0.7, cov = 0.7,
prob_int = 0.5)
```

We then combine `covset1`

and `covset2`

, and call function `simu_cov()`

to simulate the covariates and add them to the treatment variables saved in `ss`

.

In this step, we also set the **true** hazard ratio `HR`

between the internal treatment and control arm, and the hazard ratio `driftHR`

between the external control and internal control arms. User can provide a list of different `HR`

and `driftHR`

values to test a range of different hazard ratios between the three arms.

The number of simulations `nsim`

and seed parameters `seed`

are included in the same statement to produce reproducible results. `sample_cov`

is a list of 8 matrices.

```
<- c(covset1, covset2)
cov_list <- simu_cov(ssObj = ss, covObj = cov_list, HR = c(0.67, 1), driftHR = c(1, 1.2), nsim = 10, seed = 47) sample_cov
```

*Example of one simulated dataset (matrix) with covariates and treatment arm information from output sample_cov:*

```
head(sample_cov[[1]], 5)
#> driftHR HR ext trt cov1 cov2 cov3 cov4 cov5 cov6
#> [1,] 1 0.67 1 0 1 1 1.426442353 0 0 0.06551554
#> [2,] 1 0.67 1 0 0 0 0.413408109 0 0 -0.86978706
#> [3,] 1 0.67 1 0 0 0 -0.667792987 0 0 -0.52705948
#> [4,] 1 0.67 1 0 1 0 -0.008703362 0 0 -0.66796308
#> [5,] 1 0.67 1 0 0 0 -0.941826735 1 1 0.90304808
#> cov7 cov8
#> [1,] 0.06551554 0.06551554
#> [2,] -0.86978706 -0.86978706
#> [3,] -0.52705948 -0.52705948
#> [4,] -0.66796308 -0.66796308
#> [5,] 0.90304808 0.90304808
```

The second part involves generating time-to-events from either a piece-wise exponential distribution or weibull distribution. In the example below, we assume the survival time follows the piece-wise exponential distirbution such that baseline hazard rate is constant in months 1-2, and from month 3 onward. The baseline hazard rate (`lambdaC`

) for patients with all covariate equaling 0 in the internal control arm in these time periods are 0.0135 and 0.2, respectively. The baseline hazard rate for the internal treatment and external control arm are the product of `lambdaC`

and `HR`

, `driftHR`

, respectively.

The calculation below details how we draw event time for each patient following an exponential distribution. For each patient, we calculate a specific \(\lambda\) value based on the trial and treatment indicator, along with the covariate values for this patient.

\[\begin{aligned} f(t) &= v\lambda t^{v-1} e^{-\lambda t^v} \\ F(t) &= 1 - e^{-\lambda t^v} \\ \lambda(t) &= \lambda v t ^{v-1} \\ \\ v &\text{: shape parameter (user-specified)}\\ \lambda &\text{: rate parameter}\\ \lambda_c &\text{: baseline hazard rate at time 0 for internal control patient with covariates } X = 0 \\ \\ \text{Alternative parameterizations}\\ f(t) &= \frac{v}{k} \left( \frac{t}{k} \right) ^{v-1} e^{-\frac{t}{k}^v} \\ &= v \left( \frac{1}{k} \right) ^{v} t^{v-1} e^{- \left(\frac{t}{k} \right) ^v} \\ \lambda &= \left( \frac{1}{k} \right) ^ v\\ \lambda(t) &= \lambda v t ^{v-1} \\ log(k) &= - \frac{1}{v} log(\lambda) \\ k &\text{: scale parameter (input for rweibull in R, one k for each patient)}\\ \\ \text{Internal control}\\ \lambda &= \lambda_c \times e^{X \beta}\\ \lambda(t) &= \lambda_c \times e^{X \beta} \times v t ^{v-1} \\ log(k) &= - \frac{1}{v} \Bigl[ log (\lambda_c) + X \beta \Bigr] \\ \\ \text{Internal treatment}\\ \lambda &= \lambda_c \times \text{HR} \times e^{X \beta} \\ \lambda(t) & = \lambda_c \times \text{HR} \times e^{X \beta} \times v t ^{v-1}\\ log(k) &= - \frac{1}{v} \Bigl[ log(\lambda_c) + log(\text{HR}) + e^{X \beta} \Bigr] \\ \\ \text{External control} \\ \lambda &= \lambda_c \times \text{driftHR} \times e^{X \beta} \\ \lambda(t) &= \lambda_c \times \text{driftHR} \times e^{X \beta} \times v t ^{v-1} \\ log(k) &= - \frac{1}{v} \Bigl[ log(\lambda_c) + log(\text{driftHR}) + e^{X \beta} \Bigr] \\ \end{aligned}\]

Since the covariate data we generate in `sample_match`

contains 8 covariates, we also want to make sure the length of `beta`

is 8.

```
= set_event(event = "pwexp", lambdaC = c(0.0135, 0.02), t_itv = 2,
evt beta = c(rep(0.2,3), 0.3, rep(0.5,2), rep(2, 2)))
```

Additionally, users can choose to expand the covariates when calculating the hazard rate. In the example below, the hazard rate for patient i from the internal control will be calculated as below. Same as internal treatment and external control arms.

\[\begin{aligned} \lambda_i = \lambda_C \times e^{0.2 \times x_3 + 0.3 \times (x_1 \times x_3) + 0.5 \times (x_2 + x_3) + 2 \times x_1^3} \end{aligned}\]

```
set_event(event = "pwexp", lambdaC = c(0.0135, 0.02), t_itv = 2,
beta = c(0.2, 0.3, 0.5, 2),
change = list(c("cov1", "*", "cov3"), c("cov2", "+", "cov3"),
c("cov1", "^", "3")), keep = "cov3")
```

Time-to-events can also follow a weibull distribution. Under this setting, we are expected to enter the shape of the weibull distribution which is 0.9 in this case. We let *beta = 0.5* to be the coefficients for all covariates. Users can use `?set_event`

to see more options.

`set_event(event = "weibull", shape = 0.9, lambdaC = 0.0135, beta = 0.5)`

For Weibull distribution, the goal is to find the hazard rate \(\lambda(t)\) for patient with covariates \(X\).

\[\begin{aligned} f(t) &= \lambda e^{-\lambda t} \\ F(t) &= 1 - e^{-\lambda t} \\ \lambda(t) &= \lambda \\ \\ \lambda_c &\text{: baseline constant hazard rate for internal control patient with covariates } X = 0 \\ \text{HR} &\text{: hazard ratio between internal treatment vs control arm (proportional hazards)} \\ \text{driftHR} &\text{: hazard ratio between external vs internal control arm (proportional hazards)} \\ \text{I}(\text{trt}) &\text{: treatment arm indicator} \\ \text{Internal control}\\ \lambda &= \lambda_c \times e^{log(\text{HR}) \times \text{I}(\text{trt}) + X \beta} \\ & = \lambda_c \times e^0 \times e^{X_{i} \beta} \\ & = \lambda_c \times e^{X \beta} \\ \text{Internal treatment}\\ \lambda &= \lambda_c \times e^{log(\text{HR}) \times \text{I}(\text{trt}) + X \beta} \\ & = \lambda_c \times e^{log(\text{HR}) + X \beta} \\ & = \lambda_c \times \text{HR} \times e^{X \beta} \\ \text{External control} \\ \lambda &= \lambda_c \times \text{driftHR} \times e^{X \beta} \\ \text{Reparameterization for rweibull} \\ f(t) &= \lambda e^{-\lambda t} \\ F(t) &= 1 - e^{-\lambda t} \\ \lambda(t) &= \lambda \end{aligned}\]

Next, we specify the parameters for enrollment time, drop-out pattern and analysis start time. Both enrollment time and time-to-exit follow piece-wise exponential distribution.

The parameters are set separately for internal and external trials. For internal trial, we recruit 2 patients per month in the first 5 months, 3 patients from month 6 to month 1, and 16 patients per month after month 10. We assume dropouts at a rate of `etaC = 1`

per month for the control arm in the first 2 month and the rate increases to 1.2 afterwards.

The analysis is set to start at 45 months after the study begins (enrollment of the first patient) and a warning message will show if any patient enrolled after this cut-off date and they will be excluded from any future analysis.

`= set_clin(gamma = c(2,3,16), e_itv = c(5,10), CCOD = "fixed-first", CCOD_t = 45, etaC = c(0.02, 0.03), etaE = c(0.2, 0.3), d_itv = 2) c_int `

For the external trial, both the recruitment rate `gamma`

and the drop-out rate `etaC`

are assumed to be constant. We can skip `etaE`

since the external trial contains data on control arm only. Note that in this case, we do not need to specify *e_itv* or *d_itv*. The cut-off date `CCOD = "event"`

means the analysis starts once 150 events have been observed.

`= set_clin(gamma = 10, CCOD = "event", CCOD_t = 150, etaC = 0.05) c_ext `

With all the information provided above, we simulate enrollment pattern, time-to-events and time-to-dropout using function `simu_time()`

. User can also provide a seed value here too.

`<- simu_time(dt = sample_cov, eventObj = evt, clinInt = c_int, clinExt = c_ext, seed = 47) sample_time `

*Example of one simulated dataset with time-to-events information from output sample_time:*

```
head(sample_time[[1]], 5)
#> driftHR HR ext trt cov1 cov2 cov3 cov4 cov5 cov6 cov7
#> [1,] 1 0.67 0 1 1 1 1.69867515 1 1 1.4069318 1.4069318
#> [2,] 1 0.67 0 1 0 0 -0.42546841 0 0 -0.3578853 -0.3578853
#> [3,] 1 0.67 0 1 0 0 -0.04927021 1 1 0.1370222 0.1370222
#> [4,] 1 0.67 0 1 0 0 -0.42705058 1 1 0.3709728 0.3709728
#> [5,] 1 0.67 0 1 1 0 -0.10903808 1 1 0.2500303 0.2500303
#> cov8 time cnsr
#> [1,] 1.4069318 0.03071956 0
#> [2,] -0.3578853 1.58600229 1
#> [3,] 0.1370222 0.31843432 1
#> [4,] 0.3709728 1.73614131 0
#> [5,] 0.2500303 16.18520439 1
```

In the last part, we want to generate a sequence of samples from the posterior distribution of the parameters of interest using the JAGS model. Here we call `set_prior()`

to use all covariates as predictors in the weibull distribution and use a half-cauchy distribution for the precision parameter in hierarchical dynamic borrowing.

Users are expected to enter the initial values for the following parameters in the JAGS model:

`r0`

: shape of the weibull distribution for time-to-events`alpha`

: log of baseline hazard rate for external and internal control arms`d`

: precision parameter

For each dataset simulated, we can run Bayesian analysis using different predictors or priors simultaneously. For this batch of priors for testing, we choose to account for all covariates in the model.

```
<- set_prior(pred = "all", prior = "cauchy", r0 = 1, alpha = c(0, 0), sigma = 0.03)
pr1 <- set_prior(pred = "all", prior = "gamma", r0 = 1, alpha = c(0, 0))
pr2 <- set_prior(pred = "all", prior = "no_ext", r0 = 1, alpha = 0)
pr3 <- set_prior(pred = "all", prior = "full_ext", r0 = 1, alpha = 0) pr4
```

In the next example, we use a propensity score calculated based on two covariates `cov1`

and `cov2`

to be the predictor in the weibull model. The propensity score are generated using a logistic regression model.

`set_prior(pred = c("ps", "cov1", "cov2"), prior = "gamma", r0 = 1, alpha = c(0, 0))`

Below we present some other examples of `set_prior()`

. Users can use `?set_prior`

to see more options.

```
# Use no covariate. External arm will not be included in the Bayesian model
set_prior(pred = "none", prior = "no_ext", r0 = 1, alpha = 0)
# Use propensity score calculated using all covariates as predictor. Hazard ratio = 1 for external and internal control arms
set_prior(pred = "ps", prior = "full_ext", r0 = 1, alpha = 0)
# Use a subset of covaraites as predictors
set_prior(pred = c("cov2", "cov5"), prior = "no_ext", r0 = 1, alpha = 0)
```

Lastly, we combine all four priors, and run MCMC to generate samples from posterior distributions. The number of parallel chains for the model, number of iterations for adaptation, number of iterations discarded as burn-in and number of iterations to monitor are 2, 100, 100, 200 respectively. User can also provide a seed value. User can choose to leverage parallel processing through calling `run_mcmc_p()`

instead of `run_mcmc()`

.

If folder path `path`

is provided, `run_mcmc()`

and `run_mcmc_p()`

will output and save the MCMC result and summary statistics.

```
= c(pr1, pr2, pr3, pr4)
pr_list <- run_mcmc(dt = sample_time, pr_list, n.chains = 2, n.adapt = 10, n.burn = 10, n.iter = 20, seed = 47)
res # res <- run_mcmc_p(dt = sample_time, pr_list, n.chains = 2, n.adapt = 100, n.burn = 100, n.iter = 200, seed = 2)
```

To generate summary statistics of one or multiple simulation scenarios, we can call `get_summary()`

.

`<- get_summary(res) summ `

*Example of the summary statistics from output summ:*

`head(summ, 5)`

To visualize the results, users can call `plot_hr`

, `plot_type1error`

and `plot_power`

to see the Bayesian posterior mean hazard ratios between the treatment and control arm, as well as power, type I error implications.

`plot_type1error(summ, driftHR = 1.2, pred = "all")`

`plot_power(summ, HR = 0.67, driftHR = 1, pred = "all")`

`plot_hr(summ, HR = 0.67, driftHR = 1.2, pred = "all")`

`plot_bias(summ, HR = 1, driftHR = 1.2, pred = "all")`

`plot_mse(summ, HR = 0.67, driftHR = 1, pred = "all")`