knitr::opts_chunk$set(comment = "#>", collapse = TRUE) suppressWarnings(RNGversion("3.5.0")) set.seed(28999)
library(bayesCT)
The purpose of this vignette is to introduce how to simulate and analyze an adaptive Bayesian clinical trial for binomial outcomes. The simulation section compromises the design of the trial itself which provides type I error rate and power at each interim look. We use the beta-binomial conjugate prior for the estimation of posterior probabilities. Available historical data can be used as an informative prior; we use the bayesDP
package as the engine for incorporating the historical data. By default, the model uses a non-informative prior of $Beta(a = 1, b = 1)$ with or without the incorporation of historical data. Instead of using traditional R function, we use pipes to input our parameters.
Let $y$ and $N$ denote the number of events and the sample size, respectively. Let $a_0$ and $b_0$ denote the rate parameters of a Beta distribution. Then, the posterior distribution of the event rate under vague (flat) priors is
$$ \tilde{\theta}\mid y,N \; \sim \; \mathcal{B}eta\left(y+a_0,\,N-y+b_0 \right).$$ When historical data is present, $y_0$ and $N_0$ denote the number of events and sample size of the historical data. The weight of the historical data included in the study design and analysis is denoted by $\hat\alpha$. For more details on the computation of $\hat{\alpha}$, please refer to the vignette of binomial counts available at https://CRAN.R-project.org/package=bayesDP. The posterior distribution of the event rate with historical data incorporated under vague (flat) priors is
$$\tilde{\theta} \mid y,N, y_0, N_0 \; \sim \; \mathcal{B}eta\left(y+y_0\hat{\alpha}+a_0,\, N-y+\hat{\alpha}(N_0-y_0)+b_0 \right),$$ Since there is no closed-form solution for the difference in beta distributed random variables, we use Monte Carlo simulations to estimate the posterior of the treatment difference.
The estimation of the difference in proportions is $\tilde{\theta_T} - \tilde{\theta_C}$, where $\theta_T$ is the posterior of the event rates in the treatment group and $\theta_C$ is the posterior of the event rates in the control group.
The following section lays out each of the functions and inputs for carrying out simulations and analyses of Bayesian adaptive trials.
Unlike traditional R functions, the bayesCT
package depends on pipe inputs with different wrapper functions. All the wrapper functions are illustrated below along with details on the arguments for the simulation and analysis.
binomial_outcome - wrapper function for specifying event rates. Inputs:
study_details - wrapper function for specifying sample size, study length, interim looks, and loss to follow-up. Inputs:
interim_look
value must be smaller than total_sample_size
. enrollment_rate - wrapper function for specifying enrollment. Inputs:
time
. Specified as patients per day. Can be a scalar or vector. Default is 0.3, i.e. 0.3 patients per day. hypothesis - wrapper function for both simulation and analysis. Inputs:
randomize - wrapper function for specifying the randomization scheme. Inputs:
impute - wrapper function specifying imputation of outcomes at the interim looks. Inputs:
beta_prior - wrapper function for specifying the non-informative prior. Inputs:
historical_binomial - wrapper function for specifying historical data, if available. This function should only be used when historical data is incorporated in the analysis or simulation. For more details on the method and computation, please see https://CRAN.R-project.org/package=bayesDP. Inputs:
weibull
, scaledweibull
, and identity
. The discount function scaledweibull
scales the output of the Weibull CDF to have a max value of 1. The identity
discount function uses the posterior probability directly as the discount weight. Default value is "identity"
.discount_function = "identity"
.discount_function = "identity"
.mc
estimates alpha for each Monte Carlo iteration. Alternate value fixed
estimates alpha once and holds it fixed throughout the analysis. See the the
bdpsurvival
vignette vignette("bdpbinomial-vignette", package="bayesDP"
for more details.data_binomial - wrapper function for inputting data with a binomial outcome. Inputs:
analysis - wrapper function for analyzing a trial. Inputs:
type="binomial"
for a trial with binomial outcome. simulate - wrapper function for simulating trials. Should be used as the terminal end of the pipe. Inputs:
In the following section, we will discuss the design of adaptive trials using bayesCT
for binomial outcomes. We illustrate an example for one-arm trial and two-arm trials using the wrapper functions described above.
In the example below, we will illustrate how to compute power, type 1 error, and other characteristics for an objective performance criterion (OPC) trial with an observed proportion of events and hypothesis described as follows, $$H_0: \theta_{treatment} \geq 0.08 \qquad H_A:\theta_{treatment} < 0.08.$$
The most important wrapper functions are study_details and binomial_outcome (especially since there are no default values).
Binomial events are simulated using an event rate of 0.08. The total sample size is 900 with a study length of 50 days. A 10% loss to follow-up as assumed. Based on this information, the adaptive trials are simulated 2 times to obtain the following output (NOTE: for the purpose of reproducing the vignette quickly, we reduce the number of simulations to 5, you should use a much larger value, e.g., 10000). The aforementioned inputs were chosen for illustration purposes only.
value <- binomial_outcome(p_treatment = 0.08) %>% study_details(total_sample_size = 900, study_period = 50, interim_look = NULL, prop_loss_to_followup = 0.10) # Simulate 2 trials output <- value %>% simulate(no_of_sim = 2) # Structure of the simulation output str(output)
To allow for early stopping for success or futility, we can add interim looks to the design. We'll check for success or futility at the enrollment of the 600th, 700th and 800th subject. Upon adding this interim look requirement, the trial is simulated 2 times to obtain the output.
# Adding interim looks value <- value %>% study_details(total_sample_size = 900, study_period = 50, interim_look = c(600, 700, 800), prop_loss_to_followup = 0.10) # Simulate 2 trials output <- value %>% simulate(no_of_sim = 2) # Structure of the simulation output str(output)
Patient enrollment is assumed to follow a Poisson process. The default enrollment rate is 0.3 patients per day. In this simulation we'll introduce a step-wise Poisson process with rate $\lambda$ as follows:
[ \lambda = \left{ \begin{array}{ll} 0.3 & \text(time) \in [0, 25) \ 1 & \text(time) \in [25, \infty) \ \end{array} \right. ]
This enrollment scheme is illustrated below.
value <- value %>% enrollment_rate(lambda = c(0.3, 1), time = 25) output <- value %>% simulate(no_of_sim = 2) str(output)
The hypothesis is an important wrapper function which controls the probability of futility, probability of accepting the alternative hypothesis, probability of early success, the alternative hypothesis, and the treatment difference margin.
Since, in an OPC trial, the proportion of events in the treatment group are simulated using the input provided, delta
controls the maximum threshold allowed for the trial to succeed/fail. The default value of delta
is 0. Here, we'll use delta = -0.03
(i.e $\hat\theta_{treatment} - 0.08 > -0.03$).
We'll further set the futility probability to 0.05, the expected success probability for early stopping to 0.90, and the final probability of accepting the alternative to 0.95. The alternative is "less"
due to the hypothesis function specified above.
value <- value %>% hypothesis(delta = -0.03, futility_prob = 0.05, prob_accept_ha = 0.95, expected_success_prob = 0.90, alternative = "less") output <- value %>% simulate(no_of_sim = 2) str(output)
Next, we'll illustrate imputations for imputing outcomes for subjects loss to follow up. We'll carry out 5 imputations and draw 1000 values from the posterior of each imputation.
value <- value %>% impute(no_of_impute = 5, number_mcmc = 1000) output <- value %>% simulate(no_of_sim = 10) str(output)
The default non-informative beta prior used in the simulation is $\mathcal{B}eta(1, 1)$. In our OPC trial simulation, we'll change the default to $\mathcal{B}eta(5,5)$. This will increase the weight of the non-informative prior in the simulation. This non-informative beta prior is implemented using beta_prior wrapper function.
value <- value %>% beta_prior(a0 = 5, b0 = 5) output <- value %>% simulate(no_of_sim = 2) str(output)
Historical data is not required to compute the simulation. However, if historical data is available, it can be incorporated into the analysis using the discount prior approach as implemented in the bayesDP
R package.
In our OPC trial, we'll illustrate historical data incorporation. We'll assume that the historical data had 5 events in 55 subjects. We'll incorporate this historical data using the identity discount function.
For more details on the historical data incorporation method and computation, please see https://CRAN.R-project.org/package=bayesDP.
value <- value %>% historical_binomial(y0_treatment = 5, N0_treatment = 55, discount_function = "identity", y0_control = NULL, N0_control = NULL, alpha_max = 1, fix_alpha = FALSE, weibull_scale = 0.135, weibull_shape = 3, method = "fixed") output <- value %>% simulate(no_of_sim = 2) str(output)
The above flow was for illustrative purposes. Instead of inputting parameters step by step, the trial parameters can be filled in all at once as illustrated below. The pipe function connects all inputs together and the trial is simulated 2 times to obtain results.
value <- binomial_outcome(p_treatment = 0.08) %>% enrollment_rate(lambda = c(0.3, 1), time = 25) %>% study_details(total_sample_size = 900, study_period = 50, interim_look = c(600, 700, 800), prop_loss_to_followup = 0.10) %>% hypothesis(delta = -0.03, futility_prob = 0.05, prob_accept_ha = 0.95, expected_success_prob = 0.90, alternative = "less") %>% impute(no_of_impute = 25, number_mcmc = 1000) %>% beta_prior(a0 = 5, b0 = 5) %>% historical_binomial(y0_treatment = 5, N0_treatment = 55, discount_function = "identity", y0_control = NULL, N0_control = NULL, alpha_max = 1, fix_alpha = FALSE, weibull_scale = 0.135, weibull_shape = 3, method = "fixed") %>% simulate(no_of_sim = 2) str(value)
In this section, we will illustrate how to perform the design of a two-arm trial without incorporating historical data. The example will compute the type 1 error, power, and other outputs for a superiority trial. The study hypothesis is $$H_0: \theta_{treatment} - \theta_{control} \leq 0 \qquad H_A: \theta_{treatment} - \theta_{control} > 0.$$
The binomial events are simulated using an event rate of 0.15 for the treatment group and 0.12 for the control group. The total sample size is 400, with a study length of 30 days. A 15% loss to follow up is assumed. Further, we will illustrate block randomization. The following code simulates a trial 2 times using the piping procedure.
value <- binomial_outcome(p_treatment = 0.15, p_control = 0.12) %>% study_details(total_sample_size = 400, study_period = 30, interim_look = 350, prop_loss_to_followup = 0.15) %>% hypothesis(delta = 0, futility_prob = 0.10, prob_accept_ha = 0.975, expected_success_prob = 1, alternative = "greater") %>% randomize(block_size = 9, randomization_ratio = c(4, 5)) %>% impute(no_of_impute = 5, number_mcmc = 5000) %>% beta_prior(a0 = 0, b0 = 0) %>% simulate(no_of_sim = 2) str(value)
In this section, we will demonstrate how to run an adaptive Bayesian trial using bayesCT. A sample dataset is provided in the package.
The dataset binomialdata contains the results of 300 subjects from a two-arm trial with binomial outcome. The complete
column indicates whether the outcome was observed, i.e., loss to follow-up.
data(binomialdata) head(binomialdata)
The minimum input needed to run an adaptive Bayesian trial is the data itself. The data_binomial input allows the input of the data. The treatment group (0 for control, 1 for treatment) and outcome input are essential for the analysis. However, if the complete input is not provided, the function assumes the outcome data is complete. A default analysis is carried out below.
input <- data_binomial(treatment = binomialdata$treatment, outcome = binomialdata$outcome, complete = binomialdata$complete) out <- input %>% analysis(type = "binomial") str(out)
We'll now illustrate using piping to carry out the complete analysis. First, we'll assume the following hypothesis: $$H_0:\theta_{treatment} - \theta_{control} <= 0.02 \quad H_A: \theta_{treatment} - \theta_{control} > 0.02$$ The delta and alternative used to analyze the trial is 0.02 and "greater" respectively. The probability of accepting the alternative is 0.95, the probability of stopping for futility is 0.05, and the probability of stopping for success is 0.90. We will carry out imputations on subjects loss to follow up. Additionally, we will incorporate historical data on the treatment arm.
out <- data_binomial(treatment = binomialdata$treatment, outcome = binomialdata$outcome, complete = binomialdata$complete) %>% hypothesis(delta = 0.02, futility_prob = 0.05, prob_accept_ha = 0.95, expected_success_prob = 0.90, alternative = "greater") %>% impute(no_of_impute = 50, number_mcmc = 10000) %>% beta_prior(a0 = 3, b0 = 3) %>% historical_binomial(y0_treatment = 12, N0_treatment = 100, y0_control = NULL, N0_control = NULL, discount_function = "weibull", alpha_max = 1, fix_alpha = FALSE, weibull_scale = 0.135, weibull_shape = 3, method = "fixed") %>% analysis(type = "binomial") str(out)
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