# calc_predictive: Calculate a single posterior predictive value In ppseq: Design Clinical Trials using Sequential Predictive Probability Monitoring

## Description

This function is meant to be used in the context of a clinical trial with a binary endpoint. The goal is to calculate the posterior predictive probability of success at the end of a trial, given the data available at an interim analysis. For the two-arm case the number of events observed at interim analysis, the sample size at interim analysis, and the total planned sample size are denoted y0, n0, and N0 in the standard-of-care arm and y1, n1, and N1 in the experimental arm.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```calc_predictive( y, n, p0, N, direction = "greater", delta = NULL, prior = c(0.5, 0.5), S = 5000, theta = 0.95 ) ```

## Arguments

 `y` vector of length two containing number of events observed so far c(y0, y1) for two-sample case; integer of number of events y observed so far for one-sample case `n` vector of length two containing the sample size so far c(n0, n1) for two-sample case; integer of sample size so far for one-sample case `p0` The target value to compare to in the one-sample case. Set to NULL for the two-sample case. `N` the total planned sample size at the end of the trial, c(N0, N1) for two-sample case; integer of total planned sample size at end of trial N for one-sample case `direction` "greater" (default) if interest is in P(p1 > p0) and "less" if interest is in P(p1 < p0) for two-sample case. For one-sample case, "greater" if interest is in P(p > p0) and "less" if interest is in P(p < p0). `delta` clinically meaningful difference between groups. Typically 0 for the two-sample case. NULL for one-sample case (default). `prior` hyperparameters of prior beta distribution. Beta(0.5, 0.5) is default `S` number of samples, default is 5000 `theta` The target posterior probability. e.g. Efficacy decision if P(p1 > p0) > theta for the two-sample case with greater direction. Default is 0.95.

## Value

Returns the posterior predictive probability of interest

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```set.seed(123) # One-sample case calc_predictive(y = 14, n = 50, p0 = 0.2, N = 100) # Two-sample case (not run) calc_predictive( y = c(7, 12), n = c(50, 50), p0 = NULL, N = c(100, 100), delta = 0) ```

ppseq documentation built on Sept. 9, 2021, 9:06 a.m.