Type:	Package
Title:	Self-Adapting Mixture (SAM) Priors
Version:	3.0.0
Maintainer:	Peng Yang <pyang7@mdanderson.org>
Description:	Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) <doi:10.1111/biom.13927>.
Depends:	R (≥ 3.5.0), RBesT, MatchIt
Imports:	Metrics, assertthat, checkmate, ggplot2
Suggests:	rmarkdown, knitr, testthat (≥ 2.0.0), foreach, purrr, rstanarm (≥ 2.17.2), scales, tools, broom, tidyr, parallel
VignetteBuilder:	knitr
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-04-27 20:54:08 UTC; pyang7
Author:	Peng Yang [aut, cre], Ying Yuan [aut]
Repository:	CRAN
Date/Publication:	2026-04-27 22:10:02 UTC

Simulated Data for the Construction of Propensity Score-Integrated Informative Priors

Description

This dataset demonstrates the construction of a Propensity Score-Integrated (PS) SAM prior. It simulates a two-arm randomized clinical trial (RCT) with a 2:1 randomization ratio between treatment and control arms, considering both binary and continuous endpoints.

Usage

PS_SAM_data

Format

A data frame with 600 observations.

• "A" is the treatment assignment (1 = treated, 0 = control).

• "G" is the study indicator (1 = current, 0 = historical).

• "X_1" is a binary covariate.

• "X_2" is a continuous covariate.

• "X_3" is a continuous covariate.

• "Y_{binary}" is binary outcome.

• "Y_{continuous}" is continuous outcome.

Details

The dataset includes:

• Sample size for treatment arm: n_t = 200.

• Sample size for control arm: n_c = 100.

• Sample size for historical control study: n_h = 300.

Covariates for the control arm were generated from

X_1 \sim Ber(0.5), ~~ X_2 \sim N(0, 1), ~~ X_3 \sim N(0.5, 1),

where Ber(\cdot) stands for Bernoulli distribution. Covariates for the historical controls were generated from a mixture distribution, with half were generated the same as for the control arm, while the other half were drawn from

X_1 \sim Ber(0.8), ~~ X_2 \sim N(-0.4, 1), ~~ X_3 \sim N(-0.2, 1).

For the binary endpoint, y_i were generated from the logit model:

logit(\Pr(y_i = 1 | X_{1i}, X_{2i}, X_{3i}, A_i)) = -1.4 - 0.5 X_{1i} + X_{2i} + 2 X_{3i} + \lambda A_i,

where \lambda is the treatment effect size, and we let \lambda = 0.9 to generate a moderate treatment effect size so that they study has a reasonable power.

For the continuous endpoint, y_i were generated from the following normal model:

y_i = 1.8 X_{1i} + 0.9 X_{2i} - 2 X_{3i} + \lambda A_i + \epsilon_i,

where we let \lambda = 1, and \epsilon_i \sim N(0, 3.5^2).

This dataset enables evaluation of the PS-SAM prior's performance in addressing heterogeneity between the RCT control arm and historical controls.

Examples

# Load the dataset
data(PS_SAM_data)

# View the structure
str(PS_SAM_data)

Calculating the Propensity Score-Integrated Informative Priors

Description

The PS_prior function is designed to calculate the Propensity Score-Integrated (PS) informative prior constructed based on historical data.

Usage

PS_prior(
  formula,
  data,
  outcome,
  study,
  treat,
  method,
  distance,
  ratio,
  ps.method,
  trim
)

PS_prior.default(
  formula,
  data,
  outcome,
  study,
  treat,
  method,
  distance,
  ratio,
  ps.method,
  trim
)

PS_prior.beta(
  formula,
  data,
  outcome,
  study,
  treat,
  method,
  distance,
  ratio,
  ps.method,
  trim
)

PS_prior.norm(
  formula,
  data,
  outcome,
  study,
  treat,
  method,
  distance,
  ratio,
  ps.method,
  trim
)

Arguments

Details

This function aims to calculate informative priors using historical data by incorporating covariate information to enhance borrowing strength and address prior-data conflicts.

Let G be the study indicator, where G = 1 indicate patient is from current control study, and G = 0 indicate patient is from historical control study. Given the covariates data X, the propensity score is defined as follows,

e(X) = \Pr(G = 1 | X),

where distance allows different methods to estimate the propensity scores.

Calculate informative prior through PS matching is to identify a subset of historical data (D_h^*) that have similar PS as current control data (D). Various algorithms are available for PS matching, please refer to method. The informative prior can then be calculated based on the matched historical dataset.

Alternative, we can utilize the inverse probability of treatment weighting (IPTW) to adjust the distribution of X in historical data D_h, making it similar to that in D. Specifically, for the ith subject, we assign a weight \alpha_i to the outcome y_i in D_h based on its PS e(X_i) and a fixed weight \alpha_i = 1 to X_i in D, as follows:

\alpha_i = G_1 + (1 - G_i) \frac{e(X_i)}{1 - e(X_i)}.

To avoid extremely large weights that may compromise IPTW, symmetric trimming rule can be used to trim the tails of the PS distribution by input trim with default [0.1,0.9], that is to trim observations whose estimated PS is outside of this range.

To standardized \alpha, we compute the effective sample size (ESS), which approximately reflects the level of precision or equivalently its sample size, retained in the sample after weight as n^{*}_h = (\sum \alpha_i)^2 / \sum{\alpha_i^2}. The standardized weight is given by

\alpha_i^{*} = G_i + (1 - G_i)\frac{G_i}{\sum{\alpha_i} / n_h^{*}}.

For binary endpoint Y \sim Ber(\theta), the informative prior \pi_1(\theta) can be constructed as follows,

\pi_1(\theta) \propto L(\theta | D_h, \alpha^{*}) \pi_0(\theta) = Beta(a + \sum \alpha_i^{*}y_i, b + n_h^* - \sum \alpha_i^{*}y_i )\},

where \pi_0(\theta) is a non-informative prior, a natural choice is Beta(a, b), with a = b = 1.

For continuous endpoint Y \sim N(0, \sigma^2), suppose \sigma^2 is unknown, with non-informative prior p(\theta, \sigma^2) \propto 1/\sigma^2, \pi_1(\theta) follows a student-t distribution with degree of freedom n_h^{*} - 1. Given that n_h^{*} is moderate and large, it can be approximated by a normal distribution N(\bar{y}^{*}, {s^{*}}^2 / n_h^{*}) with

\bar{y}^{*} = \sum \alpha_i^* y_i / \alpha_i^*, ~~ {s^{*}}^2 = \sum \alpha_i^* (y_i - \bar{y}^{*})^2 / (n_h^{*} - 1).

Value

Displays the informative prior calculated from historical data based on the selected PS method.

Functions

• PS_prior.default(): The function calculates the Propensity Score-Integrated informative prior based on historical data for binary and continuous endpoint.

• PS_prior.beta(): The function calculates the Propensity Score-Integrated informative prior based on historical data for binary endpoint.

• PS_prior.norm(): The function calculates the Propensity Score-Integrated informative prior based on historical data for continuous endpoint.

References

Zhao Y, Laird G, Chen J, Yuan Y. PS-SAM: doubly robust propensity-score-integrated self-adapting mixture prior to dynamically borrow information from historical data.

See Also

matchit

Examples

## Load example data
data('PS_SAM_data')
## Subset the data to contain historical data and current control
dat <- PS_SAM_data[PS_SAM_data$A == 0, ]
str(dat)

## Examples for binary endpoints
## Generate the informative prior based on historical data using PS Matching
summary(PS_prior(formula = 'G ~ X_1 + X_2 + X_3',
                 data = dat, ps.method = 'Matching', method = 'nearest',
                 outcome = 'Y_binary', study = 'G', treat = 'A'))

## Generate the informative prior based on historical data using PS Weighting
summary(PS_prior(formula = 'G ~ X_1 + X_2 + X_3',
                 data = dat, ps.method = 'Weighting',
                 outcome = 'Y_binary', study = 'G', treat = 'A'))

## Examples for continuous endpoints
## Generate the informative prior based on historical data using PS Matching
summary(PS_prior(formula = 'G ~ X_1 + X_2 + X_3',
                 data = dat, ps.method = 'Matching', method = 'nearest',
                 outcome = 'Y_continuous', study = 'G', treat = 'A'))

## Generate the informative prior based on historical data using PS Weighting
summary(PS_prior(formula = 'G ~ X_1 + X_2 + X_3',
                 data = dat, ps.method = 'Weighting',
                 outcome = 'Y_continuous', study = 'G', treat = 'A'))

Calculating SAM priors

Description

The SAM_prior function is designed to display the SAM prior, given the informative prior (constructed from historical data), non-informative prior, and the mixture weight calculated using SAM_weight function (Yang, et al., 2023).

Usage

SAM_prior(if.prior, nf.prior, weight, ...)

## S3 method for class 'betaMix'
SAM_prior(if.prior, nf.prior, weight, ...)

## S3 method for class 'gammaMix'
SAM_prior(if.prior, nf.prior, weight, ...)

## S3 method for class 'normMix'
SAM_prior(if.prior, nf.prior, weight, ..., sigma)

Arguments

Details

SAM prior is constructed by mixing an informative prior \pi_1(\theta), constructed based on historical data, with a non-informative prior \pi_0(\theta) using the mixture weight w determined by SAM_weight function to achieve the degree of prior-data conflict (Schmidli et al., 2015, Yang et al., 2023).

Let \theta and \theta_h denote the treatment effects associated with the current arm data D and historical data D_h, respectively. Let \delta denote the clinically significant difference such that if |\theta_h - \theta| \ge \delta, then \theta_h is regarded as clinically distinct from \theta, and it is therefore inappropriate to borrow any information from D_h. Consider two hypotheses:

H_0: \theta = \theta_h, ~ H_1: \theta = \theta_h + \delta ~ or ~ \theta = \theta_h - \delta.

H_0 represents that D_h and D are consistent (i.e., no prior-data conflict) and thus information borrowing is desirable, whereas H_1 represents that the treatment effect of D differs from D_h to such a degree that no information should be borrowed.

The SAM prior uses the likelihood ratio test (LRT) statistics R to quantify the degree of prior-data conflict and determine the extent of information borrowing.

R = P(D | H_0, \theta_h) / P(D | H_1, \theta_h) = P(D | \theta = \theta_h) / \max(P(D | \theta = \theta_h + \delta), P(D | \theta = \theta_h - \delta)) ,

where P(D | \cdot) denotes the likelihood function. An alternative Bayesian choice is the posterior probability ratio (PPR):

R = P(D | H_0, \theta_h) / P(D | H_1, \theta_h) = P(H_0) / P( H_1) \times BF,

where P(H_0) and P(H_1) is the prior probabilities of H_0 and H_1 being true. BF is the Bayes Factor that in this case is the same as the LRT.

The SAM prior, denoted as \pi_{sam}(\theta), is then defined as a mixture of an informative prior \pi_1(\theta), constructed based on D_h and a non-informative prior \pi_0(\theta):

\pi_{sam}(\theta) = w\pi_1(\theta) + (1-w)\pi_0(\theta),

where the mixture weight w is calculated as:

w = R / (1 + R).

As the level of prior-data conflict increases, the likelihood ratio R decreases, resulting in a decrease in the weight w assigned to the informative prior and thus a decrease in information borrowing. As a result, \pi_{sam}(\theta) is data-driven and has the ability to self-adapt the information borrowing based on the degree of prior-data conflict.

Value

Displays the SAM prior as a mixture of an informative prior (constructed based on the historical data) and a non-informative prior.

Methods (by class)

• SAM_prior(betaMix): The function calculates the SAM prior for beta mixture distribution. The default nf.prior is set to be mixbeta(c(1,1,1)) which represents a uniform prior Beta(1,1).

• SAM_prior(gammaMix): The function calculates the SAM prior for gamma mixture distribution. The default nf.prior is set to be mixgamma(c(1,0.001,0.001)) which represents a vague gamma prior Gamma(0.001,0.001).

• SAM_prior(normMix): The function calculates the SAM prior for normal mixture distribution. The default nf.prior is set to be mixnorm(c(1,summary(if.prior)['mean'], sigma)) which represents a unit-information prior.

References

Yang P, Zhao Y, Nie L, Vallejo J, Yuan Y. SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials. Biometrics 2023; 79(4), 2857-2868.

Schmidli H, Gsteiger S, Roychoudhury S, O'Hagan A, Spiegelhalter D, Neuenschwander B. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics 2014; 70(4):1023-1032.

See Also

SAM_weight

Examples

set.seed(123)
## Examples for binary endpoints
## Suppose that the informative prior constructed based on historical data is
## beta(40, 60)
prior.historical <- mixbeta(c(1, 40, 60))
## Data of the control arm
data.control     <- rbinom(60, size = 1, prob = 0.42)
## Calculate the mixture weight of the SAM prior
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.15,        ## Clinically significant difference
                   data = data.control  ## Control arm data
                   )
## Assume beta(1,1) as the non-informative prior used for mixture
nf.prior  <- mixbeta(nf.prior = c(1,1,1))
## Generate the SAM prior
SAM.prior <- SAM_prior(if.prior = prior.historical, ## Informative prior
                       nf.prior = nf.prior,         ## Non-informative prior
                       weight = wSAM                ## Mixture weight of the SAM prior
                       )
plot(SAM.prior)

## Examples for continuous endpoints
## Suppose that the informative prior constructed based on historical data is
## N(0, 3)
sigma      <- 3
prior.mean <- 0
prior.se   <- sigma/sqrt(100)
prior.historical <- mixnorm(c(1, prior.mean, prior.se), sigma = sigma)
## Data of the control arm
data.control <- rnorm(80, mean = 0, sd = sigma)
## Calculate the mixture weight of the SAM prior
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.2 * sigma,    ## Clinically significant difference
                   data = data.control     ## Control arm data
                   )
## Assume unit-information prior N(0,3) as the non-informative prior used
## for the mixture
nf.prior         <- mixnorm(nf.prior = c(1,prior.mean, sigma),
                            sigma = sigma)
## Generate the SAM prior
SAM.prior <- SAM_prior(if.prior = prior.historical, ## Informative prior
                       nf.prior = nf.prior,         ## Non-informative prior
                       weight = wSAM                ## Mixture weight of the SAM prior
                       )
plot(SAM.prior)

Calculating Mixture Weight of SAM Priors

Description

The SAM_weight function is designed to calculate the mixture weight of the SAM priors according to the degree of prior-data conflicts (Yang, et al., 2023).

Usage

SAM_weight(if.prior, theta.h, method.w, prior.odds, data, delta, ...)

## S3 method for class 'betaMix'
SAM_weight(if.prior, theta.h, method.w, prior.odds, data, delta, n, r, ...)

## S3 method for class 'normMix'
SAM_weight(
  if.prior,
  theta.h,
  method.w,
  prior.odds,
  data,
  delta,
  m,
  n,
  sigma,
  ...
)

## S3 method for class 'gammaMix'
SAM_weight(if.prior, theta.h, method.w, prior.odds, data, delta, u, w, ...)

Arguments

Details

SAM prior is constructed by mixing an informative prior \pi_1(\theta), constructed based on historical data, with a non-informative prior \pi_0(\theta) using the mixture weight w determined by SAM_weight function to achieve the degree of prior-data conflict (Schmidli et al., 2015, Yang et al., 2023).

Let \theta and \theta_h denote the treatment effects associated with the current arm data D and historical data D_h, respectively. Let \delta denote the clinically significant difference such that if |\theta_h - \theta| \ge \delta, then \theta_h is regarded as clinically distinct from \theta, and it is therefore inappropriate to borrow any information from D_h. Consider two hypotheses:

H_0: \theta = \theta_h, ~ H_1: \theta = \theta_h + \delta ~ or ~ \theta = \theta_h - \delta.

H_0 represents that D_h and D are consistent (i.e., no prior-data conflict) and thus information borrowing is desirable, whereas H_1 represents that the treatment effect of D differs from D_h to such a degree that no information should be borrowed.

The SAM prior uses the likelihood ratio test (LRT) statistics R to quantify the degree of prior-data conflict and determine the extent of information borrowing.

R = P(D | H_0, \theta_h) / P(D | H_1, \theta_h) = P(D | \theta = \theta_h) / \max(P(D | \theta = \theta_h + \delta), P(D | \theta = \theta_h - \delta)) ,

where P(D | \cdot) denotes the likelihood function. An alternative Bayesian choice is the posterior probability ratio (PPR):

R = P(D | H_0, \theta_h) / P(D | H_1, \theta_h) = P(H_0) / P( H_1) \times BF,

where P(H_0) and P(H_1) is the prior probabilities of H_0 and H_1 being true. BF is the Bayes Factor that in this case is the same as the LRT.

The SAM prior, denoted as \pi_{sam}(\theta), is then defined as a mixture of an informative prior \pi_1(\theta), constructed based on D_h and a non-informative prior \pi_0(\theta):

\pi_{sam}(\theta) = w\pi_1(\theta) + (1-w)\pi_0(\theta),

where the mixture weight w is calculated as:

w = R / (1 + R).

As the level of prior-data conflict increases, the likelihood ratio R decreases, resulting in a decrease in the weight w assigned to the informative prior and thus a decrease in information borrowing. As a result, \pi_{sam}(\theta) is data-driven and has the ability to self-adapt the information borrowing based on the degree of prior-data conflict.

Value

The mixture weight of the SAM priors.

Methods (by class)

• SAM_weight(betaMix): The function calculates the mixture weight of SAM priors for beta mixture distribution. The input data can be patient-level data (i.e., a vector of 0 and 1 representing the response status of each patient) or summary statistics (i.e., the number of patients and the number of responses).

• SAM_weight(normMix): The function calculates the mixture weight of SAM priors for normal mixture distribution. The input data should be a vector of patient-level observations. The input data can be patient-level data (i.e., a vector of continuous response of each patient) or summary statistics (i.e., the mean estimate, number of subjects, and the standard deviation in the control arm).

• SAM_weight(gammaMix): The function calculates the mixture weight of SAM priors for gamma mixture distribution. The input data can be patient-level data (i.e., a matrix with the first row as the censoring indicator and the second row recording the observed time) or summary statistics (i.e., the number of uncensored observations u and total observed time w).

References

Yang P, Zhao Y, Nie L, Vallejo J, Yuan Y. SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials. Biometrics 2023; 79(4), 2857-2868.

Examples

set.seed(123)
## Examples for binary endpoints
## Example 1: no prior-data conflict
## Suppose that the informative prior constructed based on historical data is
## beta(40, 60)
prior.historical <- mixbeta(c(1, 40, 60))
## Data of control arm
data.control     <- rbinom(60, size = 1, prob = 0.42)
## Calculate the mixture weight of the SAM prior
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.15,        ## Clinically significant difference
                   data = data.control  ## Control arm data
                   )
print(wSAM)

## Example 2: in the presence of prior-data conflict, where the current data
## has 12 responses in 60 patients
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.15,    ## Clinically significant difference
                   ## Methods to determine mixture weight for the SAM priors
                   ## by Posterior Probability Ratio
                   method.w = 'PPR',
                   ## Prior odds of favoring no prior-data conflicts to
                   ## the presence of prior-data conflict
                   prior.odd = 1/9,
                   n = 60,          ## Number of patients in the control arm
                   r = 12           ## Number of responses in the control arm
                   )
print(wSAM)

## Example 3: in the presence of prior-data conflict, where the current data
## has 12 responses in 60 patients
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.15, ## Clinically significant difference
                   n = 60,       ## Number of patients in the control arm
                   r = 12        ## Number of responses in the control arm
                   )
print(wSAM)

## Examples for continuous endpoints
## Example 1: no prior-data conflict
## Suppose that the informative prior constructed from historical data is
## N(0, 3)
sigma      <- 3
prior.mean <- 0
prior.se   <- sigma/sqrt(100)
prior.historical <- mixnorm(c(1, prior.mean, prior.se), sigma = sigma)
## Data of the control arm
data.control     <- rnorm(80, mean = 0, sd = sigma)
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.3 * sigma,    ## Clinically significant difference
                   data = data.control     ## Control arm data
                   )
print(wSAM)

## Example 2: in the presence of prior-data conflict, where the current data
## has mean of 0.5
data.control     <- rnorm(80, mean = 1, sd = sigma)
wSAM  <- SAM_weight(if.prior = prior.historical,
                    delta = 0.3 * sigma,    ## Clinically significant difference
                    data = data.control     ## Control arm data
                    )
print(wSAM)

## Examples for survival endpoints
## Example 1: no prior-data conflict
## Suppose the survival times from historical data follows exp(1) distribution
## with random censoring time follows U(0.5, 5) distribution
T_hi <- rexp(100, rate = 1)
C_hi <- runif(100, min = 0.5, max = 5)
## Indicators of the uncensored events
delta_hi <- as.numeric(T_hi < C_hi)
## Observed survival times from historical data
U_hi     <- T_hi
U_hi[delta_hi == 0] <- C_hi[delta_hi == 0]
## Construct the informative prior based on simulated historical data
prior.historical <- mixgamma(c(1, sum(delta_hi), sum(U_hi)),
                             param = 'ab', likelihood = 'exp')
## Suppose the survival times from control data follows exp(0.95) distribution
## with random censoring time follows U(0.5, 5) distribution
T_ci <- rexp(100, rate = 0.95)
C_ci <- runif(100, min = 0.5, max = 5)
## Indicators of the uncensored events
delta_ci <- as.numeric(T_ci < C_ci)
## Observed survival times from control data
U_ci     <- T_ci
U_ci[delta_ci == 0] <- C_ci[delta_ci == 0]
## Data of the control arm
data.control     <- rbind(sum(delta_ci), sum(U_ci))
wSAM <- SAM_weight(if.prior = prior.historical,
                   delta = 0.2,            ## Clinically significant difference
                   data = data.control     ## Control arm data
                   )
print(wSAM)

## Example 2: in the presence of prior-data conflict, where the current survival
## times follows exp(2) distribution with random censoring time follows U(0.5, 5)
## distribution
T_ci <- rexp(100, rate = 2)
C_ci <- runif(100, min = 0.5, max = 5)
## Indicators of the uncensored events
delta_ci <- as.numeric(T_ci < C_ci)
## Observed survival times from control data
U_ci     <- T_ci
U_ci[delta_ci == 0] <- C_ci[delta_ci == 0]
## Data of the control arm
data.control     <- rbind(sum(delta_ci), sum(U_ci))
wSAM  <- SAM_weight(if.prior = prior.historical,
                    delta = 0.2,            ## Clinically significant difference
                    data = data.control     ## Control arm data
                    )
print(wSAM)

Calibrate Posterior Probability Cutoff for a Two-Arm Comparative Trial

Description

The calibrate_cutoff_2arm function is designed to calibrate the posterior probability cutoff for a two-arm comparative trial under the specified endpoint type and borrowing strategy: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

calibrate_cutoff_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  target = 0.05,
  n.t,
  n,
  theta.t = NULL,
  theta = NULL,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  interval = c(0.5, 0.999),
  rel.tol = 1e-05,
  oc_rel.tol = 1e-06,
  ...
)

## S3 method for class 'betaMix'
calibrate_cutoff_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  target = 0.05,
  n.t,
  n,
  theta.t = NULL,
  theta = NULL,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  interval = c(0.5, 0.999),
  rel.tol = 1e-05,
  oc_rel.tol = 1e-08,
  ...
)

## S3 method for class 'normMix'
calibrate_cutoff_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  target = 0.05,
  n.t,
  n,
  theta.t = NULL,
  theta = NULL,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  interval = c(0.5, 0.999),
  rel.tol = 1e-05,
  oc_rel.tol = 1e-06,
  sigma.t,
  sigma,
  n_sd_int = 8,
  ...
)

Arguments

Details

For binary endpoints, the function dispatches to calibrate_cutoff_bin_2arm. For continuous endpoints, it dispatches to calibrate_cutoff_cont_2arm.

The function calibrates the posterior probability cutoff C for a two-arm comparative trial so that the repeated-sampling rejection probability under the specified calibration scenario equals target. Inference is based on the posterior probability of the treatment effect \theta_t - \theta, where \theta_t and \theta denote the treatment-arm and control-arm parameters, respectively.

For alternative = "greater", the posterior decision rule is based on

P(\theta_t - \theta > \Delta \mid D) > C,

where \Delta denotes the clinical margin and D denotes the observed data. Under the default settings, calibration is performed under the null scenario \theta_t = \theta = \theta_h, where \theta_h = E(\code{if.prior}) is the posterior mean of the informative prior constructed from historical data. This corresponds to a superiority test when \Delta = 0.

For alternative = "less", the posterior decision rule is based on

P(\theta_t - \theta < -\Delta \mid D) > C.

Calibration is again performed under the null scenario \theta_t = \theta = \theta_h, where \theta_h = E(\code{if.prior}). This corresponds to an inferiority test when \Delta = 0.

This function acts as a generic front-end and dispatches according to the class of if.prior. For binary endpoints, it calls calibrate_cutoff_bin_2arm. For continuous endpoints, it calls calibrate_cutoff_cont_2arm.

Value

A list with the following elements:

cutoff

Calibrated posterior probability cutoff.

objective

Value of the root-finding objective at the solution.

target

Target rejection probability.

method

Borrowing method used.

alternative

Direction of the posterior decision.

margin

Clinical margin used for inference.

theta

True control-arm parameter under the calibration scenario.

theta.t

True treatment-arm parameter under the calibration scenario.

interval

Search interval used for calibration.

Methods (by class)

• calibrate_cutoff_2arm(betaMix): Calibrate the posterior probability cutoff for a two-arm binary endpoint.

• calibrate_cutoff_2arm(normMix): Calibrate the posterior probability cutoff for a two-arm continuous endpoint.

See Also

calibrate_cutoff_bin_2arm, calibrate_cutoff_cont_2arm

Examples

## Example: calibrate the posterior probability cutoff for a two-arm
## binary trial using a SAM prior for the control arm

## Informative prior constructed from historical data
if.prior <- mixbeta(c(1, 20, 40))

## Sample sizes
n.t <- 100
n <- 50

## Calibrate the posterior probability cutoff
PPC <- calibrate_cutoff_2arm(
  if.prior = if.prior,             ## Informative prior from historical data
  nf.prior = mixbeta(c(1, 1, 1)),  ## Non-informative prior for SAM prior
  prior.t = mixbeta(c(1, 1, 1)),   ## Prior for treatment arm
  target = 0.05,                   ## Targeted type I error rate
  n.t = n.t,                       ## Sample size for treatment arm
  n = n,                           ## Sample size for control arm
  theta.t = summary(if.prior)["mean"],  ## True treatment-arm response rate
  theta = summary(if.prior)["mean"],    ## True control-arm response rate
  method = "SAM",                  ## Borrowing method
  delta = 0.2,                     ## Clinically significant difference
  alternative = "greater",         ## Superiority test
  margin = 0                       ## Clinical margin
)

PPC

Calibrate Posterior Probability Cutoff for a Two-Arm Comparative Trial with Binary Endpoint

Description

The calibrate_cutoff_bin_2arm function is designed to calibrate the posterior probability cutoff for a two-arm comparative trial with a binary endpoint under one borrowing strategy: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

calibrate_cutoff_bin_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  target = 0.05,
  n.t,
  n,
  theta.t = NULL,
  theta = NULL,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  interval = c(0.5, 0.999),
  rel.tol = 1e-05,
  oc_rel.tol = 1e-08
)

Arguments

Details

The calibrated cutoff is chosen so that the repeated-sampling rejection probability under a specified scenario equals the target value.

The function solves for the posterior probability cutoff such that the scenario-level rejection probability returned by eval_scenario_bin_2arm matches target. Under null scenarios, this corresponds to calibration of the type I error.

Value

A list with the following elements:

cutoff

Calibrated posterior probability cutoff.

objective

Value of the root-finding objective at the solution.

target

Target rejection probability.

method

Borrowing method used.

alternative

Direction of the posterior decision.

margin

Clinical margin used for inference.

theta

True control-arm response rate under the calibration scenario.

theta.t

True treatment-arm response rate under the calibration scenario.

interval

Search interval used for calibration.

Calibrate Posterior Probability Cutoff for a Two-Arm Comparative Trial with Continuous Endpoint

Description

The calibrate_cutoff_cont_2arm function is designed to calibrate the posterior probability cutoff for a two-arm comparative trial with a continuous endpoint under one borrowing strategy: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

calibrate_cutoff_cont_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  target = 0.05,
  n.t,
  n,
  sigma.t,
  sigma,
  theta.t = NULL,
  theta = NULL,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  interval = c(0.5, 0.999),
  rel.tol = 1e-05,
  oc_rel.tol = 1e-06,
  n_sd_int = 8
)

Arguments

Details

The calibrated cutoff is chosen so that the repeated-sampling rejection probability under a specified scenario equals the target value.

The function solves for the posterior probability cutoff such that the scenario-level rejection probability returned by eval_scenario_cont_2arm matches target. Under null scenarios, this corresponds to calibration of the type I error.

Value

A list with the following elements:

cutoff

Calibrated posterior probability cutoff.

objective

Value of the root-finding objective at the solution.

target

Target rejection probability.

method

Borrowing method used.

alternative

Direction of the posterior decision.

margin

Clinical margin used for inference.

theta

True control-arm mean under the calibration scenario.

theta.t

True treatment-arm mean under the calibration scenario.

interval

Search interval used for calibration.

Evaluate Multiple Scenarios for a Two-Arm Comparative Trial with Binary Endpoint

Description

The eval_oc_bin_2arm function is designed to evaluate repeated-sampling operating characteristics for multiple scenarios in a two-arm comparative trial with a binary endpoint under one or more borrowing strategies: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

eval_oc_bin_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  theta,
  theta.t,
  n.t,
  n,
  delta,
  method = c("SAM", "rMAP", "NP"),
  cutoff,
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  rel.tol = 1e-08
)

Arguments

Details

For each scenario, the function computes the repeated-sampling rejection probability, bias, RMSE, and mean borrowing weight using eval_scenario_bin_2arm.

The vectors theta and theta.t must have the same length. Each pair (theta[i], theta.t[i]) defines one scenario.

The cutoff argument may be common across methods or method-specific. This is useful when each borrowing method is calibrated separately before operating characteristics are evaluated.

Value

A data frame with one row per scenario-method combination and columns:

scenario

Scenario index.

theta

True control arm response rate.

theta.t

True treatment arm response rate.

delta_true

True treatment effect, \tau = \theta_t - \theta.

method

Borrowing method used.

alternative

Direction of the posterior decision.

cutoff

Posterior probability cutoff used.

margin

Clinical margin used for inference.

reject_prob

Repeated-sampling rejection probability.

bias

Bias of the posterior mean estimator of \theta.

rmse

Root mean squared error of the posterior mean estimator of \theta.

mean_weight

Average borrowing weight under the specified method.

Evaluate Multiple Scenarios for a Two-Arm Comparative Trial with Continuous Endpoint

Description

The eval_oc_cont_2arm function is designed to evaluate repeated-sampling operating characteristics for multiple scenarios in a two-arm comparative trial with a continuous endpoint under one or more borrowing strategies: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

eval_oc_cont_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  theta,
  theta.t,
  n.t,
  n,
  sigma.t,
  sigma,
  delta,
  method = c("SAM", "rMAP", "NP"),
  cutoff,
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  rel.tol = 1e-06,
  n_sd_int = 8
)

Arguments

Details

For each scenario, the function computes the repeated-sampling rejection probability, bias, RMSE, and mean borrowing weight using eval_scenario_cont_2arm.

The vectors theta and theta.t must have the same length. Each pair (theta[i], theta.t[i]) defines one scenario.

The cutoff argument may be common across methods or method-specific. This is useful when each borrowing method is calibrated separately before operating characteristics are evaluated.

Value

A data frame with one row per scenario-method combination and columns:

scenario

Scenario index.

theta

True control arm mean.

theta.t

True treatment arm mean.

delta_true

True treatment effect, \tau = \theta_t - \theta.

method

Borrowing method used.

alternative

Direction of the posterior decision.

cutoff

Posterior probability cutoff used.

margin

Clinical margin used for inference.

reject_prob

Repeated-sampling rejection probability.

bias

Bias of the posterior mean estimator of \theta.

rmse

Root mean squared error of the posterior mean estimator of \theta.

mean_weight

Average borrowing weight under the specified method.

Evaluate One Scenario for a Two-Arm Comparative Trial with Binary Endpoint

Description

The eval_scenario_bin_2arm function is designed to evaluate repeated-sampling operating characteristics for a two-arm comparative trial with a binary endpoint under one borrowing strategy: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

eval_scenario_bin_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  n.t,
  n,
  theta.t,
  theta,
  cutoff,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  rel.tol = 1e-08
)

Arguments

Details

The treatment effect is defined as \tau = \theta_t - \theta, where \theta_t and \theta denote the true response rates in the treatment and control arms, respectively.

For a given true scenario (\theta_t, \theta), this function computes the repeated-sampling rejection probability, bias, root mean squared error (RMSE), and mean borrowing weight. The rejection probability is accelerated by exploiting monotonicity of the posterior decision in the treatment-arm response count for each fixed control-arm response count.

Value

A one-row data frame with the following columns:

theta

True control arm response rate.

theta.t

True treatment arm response rate.

delta_true

True treatment effect, \tau = \theta_t - \theta.

method

Borrowing method used.

alternative

Direction of the posterior decision.

cutoff

Posterior probability cutoff used for decision making.

margin

Clinical margin used for inference.

reject_prob

Repeated-sampling rejection probability.

bias

Bias of the posterior mean estimator of \theta.

rmse

Root mean squared error of the posterior mean estimator of \theta.

mean_weight

Average borrowing weight under the specified method.

Evaluate One Scenario for a Two-Arm Comparative Trial with Continuous Endpoint

Description

The eval_scenario_cont_2arm function is designed to evaluate repeated-sampling operating characteristics for a two-arm comparative trial with a continuous endpoint under one borrowing strategy: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

eval_scenario_cont_2arm(
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  n.t,
  n,
  sigma.t,
  sigma,
  theta.t,
  theta,
  cutoff,
  delta,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  rel.tol = 1e-06,
  n_sd_int = 8
)

Arguments

Details

The treatment effect is defined as \tau = \theta_t - \theta, where \theta_t and \theta denote the true means in the treatment and control arms, respectively.

For a given true scenario (\theta_t, \theta), this function computes the repeated-sampling rejection probability, bias, root mean squared error (RMSE), and mean borrowing weight using one-dimensional numerical integration.

The rejection probability is computed by reducing the repeated-sampling decision rule to a one-dimensional integral over the control-arm sample mean.

Bias and RMSE are evaluated for the posterior mean estimator of the control arm mean \theta. Both are computed from one-dimensional first and second moments of the control-arm posterior mean. The mean borrowing weight is computed by one-dimensional integration over the control-arm sample mean.

Under null scenarios, reject_prob corresponds to type I error. Under alternative scenarios, it corresponds to power.

Value

A one-row data frame with the following columns:

theta

True control arm mean.

theta.t

True treatment arm mean.

delta_true

True treatment effect, \tau = \theta_t - \theta.

method

Borrowing method used.

alternative

Direction of the posterior decision.

cutoff

Posterior probability cutoff used for decision making.

margin

Clinical margin used for inference.

reject_prob

Repeated-sampling rejection probability.

bias

Bias of the posterior mean estimator of \theta.

rmse

Root mean squared error of the posterior mean estimator of \theta.

mean_weight

Average borrowing weight under the specified method.

Generating Operating Characteristics of SAM Priors Using Analytical Engines

Description

The get_OC function generates operating characteristics of SAM priors using the analytical operating characteristic engines for two-arm trials with binary or continuous endpoints. As an option, the operating characteristics of robust MAP priors can also be generated for comparison.

Usage

get_OC(
  if.prior,
  theta.h,
  method.w,
  prior.odds,
  nf.prior,
  prior.t = nf.prior,
  delta,
  n,
  n.t,
  target = 0.05,
  if.rMAP = FALSE,
  weight.rMAP = 0.5,
  theta,
  theta.t,
  alternative = c("greater", "less"),
  margin = 0,
  rel.tol = 1e-05,
  oc_rel.tol = 1e-06,
  interval = c(0.5, 0.999),
  n_sd_int = 8,
  ...
)

Arguments

Details

Compared with the original simulation-based implementation, this function does not rely on trial simulation. Instead, it first calibrates the posterior probability cutoff for each borrowing method to achieve the target type I error, and then evaluates operating characteristics analytically across the requested scenarios.

For each borrowing method, the function first calibrates the posterior probability cutoff so that the repeated-sampling rejection probability under the boundary null scenario equals the target type I error target. Specifically, calibration is based on the first value of theta. Let \theta = \code{theta}[1] denote the control-arm parameter under the calibration scenario. Then the treatment-arm parameter is set to \theta_t = \theta + margin when alternative = "greater", and to \theta_t = \theta - margin when alternative = "less". Thus, when margin = 0, calibration is performed under the null scenario \theta_t = \theta, corresponding to no treatment effect difference between the treatment and control arms. After the cutoff is calibrated, the function evaluates the operating characteristics across all requested scenarios using the analytical two-arm engines.

Value

A data frame with one row per scenario-method combination. The columns are:

Scenarios

Scenario index.

theta

True control-arm parameter value.

theta.t

True treatment-arm parameter value.

Methods

Borrowing method, one of "NP", "rMAP", or "SAM".

Cutoffs

Calibrated posterior probability cutoff for the method.

Bias of theta

Bias of the posterior mean estimator of \theta.

RMSE of theta

Root mean squared error of the posterior mean estimator of \theta.

Weight

Average borrowing weight under the method.

Probability of Rejection

Repeated-sampling rejection probability.

References

Yang P, Zhao Y, Nie L, Vallejo J, Yuan Y. SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials. Biometrics 2023;79(4):2857-2868.

Schmidli H, Gsteiger S, Roychoudhury S, O'Hagan A, Spiegelhalter D, Neuenschwander B. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics 2014;70(4):1023-1032.

See Also

eval_oc_bin_2arm, eval_oc_cont_2arm, calibrate_cutoff_bin_2arm, calibrate_cutoff_cont_2arm

Examples

## Example: operating characteristics for a two-arm binary trial
## using a SAM prior without rMAP comparison

## Informative prior constructed from historical data
if.prior <- mixbeta(c(1, 20, 40))

## Evaluate operating characteristics
OC <- get_OC(
  if.prior = if.prior,
  nf.prior = mixbeta(c(1, 1, 1)),   ## Non-informative prior for control mixture
  prior.t = mixbeta(c(1, 1, 1)),    ## Prior for treatment arm
  delta = 0.2,                      ## Clinically significant difference for SAM
  n = 50,                           ## Sample size for control arm
  n.t = 100,                        ## Sample size for treatment arm
  target = 0.05,                    ## Target type I error for cutoff calibration
  if.rMAP = FALSE,                  ## Do not include rMAP comparison
  theta = c(summary(if.prior)["mean"], summary(if.prior)["mean"]),
  theta.t = c(summary(if.prior)["mean"], 0.50),
  alternative = "greater",          ## Superiority test
  margin = 0                        ## Clinical margin
)

OC

Support of Distributions

Description

Returns the support of a distribution.

Usage

mixlink(mix, x)

Details

takes x and transforms it according to the defined link function of the mixture

Posterior Summary for Two-Arm Comparative Trial with Binary Endpoint

Description

The post_summary_bin_2arm function is designed to compute the posterior summary for the treatment effect in a two-arm comparative trial with a binary endpoint under one of three borrowing strategies: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

post_summary_bin_2arm(
  x.t,
  x,
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  n.t,
  n,
  delta,
  cutoff,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1,
  rel.tol = 1e-08
)

Arguments

Details

The treatment effect is defined as \tau = \theta_t - \theta, where \theta_t and \theta denote the response rates in the treatment and control arms, respectively. Inference is based on the posterior distribution of \tau given the observed response counts from the two arms.

The posterior for the treatment arm is obtained by updating prior.t using the observed response count x.t. The posterior for the control arm depends on the selected borrowing strategy:

• SAM: the prior for the control arm is a mixture of if.prior and nf.prior, with adaptive mixture weight determined by the SAM borrowing rule.

• rMAP: the prior for the control arm is a fixed mixture of if.prior and nf.prior, with fixed weight weight_rMAP.

• NP: the prior for control arm is nf.prior.

When alternative = "greater", inference is based on P(\theta_t - \theta > margin \mid X_t, X). When alternative = "less", inference is based on P(\theta_t - \theta < -margin \mid X_t, X).

The posterior mean and posterior variance of \tau are defined as

E(\tau \mid X_t, X) = E(\theta_t \mid X_t) - E(\theta \mid X),

and

\mathrm{Var}(\tau \mid X_t, X) = \mathrm{Var}(\theta_t \mid X_t) + \mathrm{Var}(\theta \mid X),

where independence between the treatment and control arm posteriors is assumed conditional on the current and historical data.

Value

A list containing the posterior probability in the requested direction, posterior mean and variance of \tau, decision indicator, borrowing weight used for the control arm prior, and the corresponding trial data and method information.

See Also

SAM_weight, SAM_prior

Posterior Summary for Two-Arm Comparative Trial with Continuous Endpoint

Description

The post_summary_cont_2arm function is designed to compute the posterior summary for the treatment effect in a two-arm comparative trial with a continuous endpoint under one of three borrowing strategies: self-adapting mixture prior (SAM), robust MAP prior with fixed weight (rMAP), or non-informative prior (NP).

Usage

post_summary_cont_2arm(
  ybar_t,
  ybar,
  if.prior,
  nf.prior,
  prior.t = nf.prior,
  n.t,
  n,
  sigma.t,
  sigma,
  delta,
  cutoff,
  method = c("SAM", "rMAP", "NP"),
  alternative = c("greater", "less"),
  margin = 0,
  weight_rMAP = 0.5,
  method.w = "LRT",
  prior.odds = 1
)

Arguments

Details

The treatment effect is defined as \tau = \theta_t - \theta, where \theta_t and \theta denote the means in the treatment and control arms, respectively. Inference is based on the posterior distribution of \tau given the observed sample means from the two arms.

The posterior for the treatment arm is obtained by updating prior.t using the observed sample mean ybar_t. The posterior for the control arm depends on the selected borrowing strategy:

• SAM: the control arm prior is a mixture of if.prior and nf.prior, with adaptive mixture weight determined by the SAM borrowing rule.

• rMAP: the control arm prior is a fixed mixture of if.prior and nf.prior, with fixed weight weight_rMAP.

• NP: the control arm prior is nf.prior only.

When alternative = "greater", inference is based on P(\theta_t - \theta > margin \mid \bar{Y}_t, \bar{Y}). When alternative = "less", inference is based on P(\theta_t - \theta < -margin \mid \bar{Y}_t, \bar{Y}).

The posterior mean and posterior variance of \tau are defined as are defined as

E(\tau \mid \bar{Y}_t, \bar{Y}) = E(\theta_t \mid \bar{Y}_t) - E(\theta \mid \bar{Y}),

and

\mathrm{Var}(\tau \mid \bar{Y}_t, \bar{Y}) = \mathrm{Var}(\theta_t \mid \bar{Y}_t) + \mathrm{Var}(\theta \mid \bar{Y}),

where independence between the treatment and control arm posteriors is assumed conditional on the current and historical data.

Value

A list containing the posterior probability in the requested direction, posterior mean and variance of \tau, decision indicator, borrowing weight used for the control arm prior, and the corresponding trial data and method information.

See Also

SAM_weight, SAM_prior