ldbounds (version 2.0.2)

condpower: Conditional Power Given Interim Results

Description

'condpower' determines conditional power, given interim results and hypothesized treatment effect. This is commonly used to evaluate the futility of an ongoing trial.

Usage

condpower(z.crit, z.val, accr, outcome.type, par.c, par.t=NULL, N, sigma=NULL)

Value

'condpower' returns a numeric value corresponding to the conditional probability.

Arguments

z.crit

the critical value (on the z-value scale) that will be used at the end of the trial to determine whether the experimental treatment is superior to control.

z.val

the current test statistic (on the z-value scale) using interim data. A positive value means that the experimental arm is estimated to have a more favorable outcome.

accr

the amount of information accrued at the time of the interim analysis. For binary and continuous outcomes, this is the number of subjects randomized. For a survival outcome, this is the number of events that have been observed.

outcome.type

The type of outcome: binary ("bin"), continuous ("mean"), or survival ("surv").

par.c

the hypothesized parameter value in the control group (when par.t is specified) or the hypothesized treatment effect (when par.t is missing). For a binary outcome, this is the proportion of failures in the control group. For a continuous outcome, this is the mean in the control group or the difference in means. For a survival outcome, this is the hazard in the control group or the hazard ratio.

par.t

the hypothesized parameter value in the experimental group. Leave null if par.c is the difference in means or the hazard ratio.

N

the total target sample size (or number of events for survival) in the trial.

sigma

the assumed standard deviation (continuous outcome).

Author

Charlie Casper

Details

This function calculates simple conditional power. The user specifies either the hypothesized parameter in each group (required for binary outcome) or the hypothesized treatment effect (difference for continuous outcome or hazard ratio for survival outcome).

Examples

  # Binary outcome
  # Assumptions used for power calculations in design of study
  # Poor outcome 25% in control group
  # Poor outcome 15% in experimental group
  # Total sample size 900
  # Interim analysis at 300 subjects
  # Final critical value 2.0 (to account for group sequential
  # monitoring)
  # At interim, intervention doing better with z-statistic 0.067

  condpower(2.0,0.067,300,"bin",0.25,0.15,900)