computeSampleSize: Compute the Sample Size for a SMART.

In smartsizer: Power Analysis for a SMART Design

Description

Computes the necessary sample size to enroll in an arbitrary SMART design for a specified power with the goal of determining optimal embedded dynamic treatment regime (EDTR). The power is the probability of excluding from the set of best EDTRs all EDTRs inferior to the best by min_Delta or more.

Usage

computeSampleSize(V, Delta, min_Delta, alpha = 0.05, desired_power)

Arguments

V	The covariance matrix of mean EDTR estimators.
Delta	The vector of effect sizes with the first zero indicating the best EDTR.
min_Delta	The minimum desired detectable effect size.
alpha	The Type I error rate for not including the true best EDTR.
desired_power	The desired power.

Details

The true best EDTR is included in the set of best with probability at least 1-alpha. Multiple comparisons are adjusted for using the Multiple Comparison with the Best methodology.

Value

The minimum sample size in order to achieve a power of desired_power to exclude EDTRs from the set of best which are inferior to the optimal EDTR by min_Delta or more.

See Also

computePower

Examples

  
  V <- rbind(c(1, 0.3, 0.3, 0.3),
             c(0.3, 1, 0.3, 0.3),
             c(0.3, 0.3, 1, 0.3),
             c(0.3, 0.3, 0.3, 1))

  #Compute sample size to achieve power of 80% to exclude EDTRs inferior
  #to the best by 0.3 or more. The first DTR is best and the other
  #three are inferior by 0.2, 0.6, and 0.3
  #The best EDTR is included with probability greater than or equal to 95%.
  computeSampleSize(V,
                    Delta = c(0, 0.2, 0.6, 0.3),
                    min_Delta = 0.3,
                    alpha = 0.05,
                    desired_power = 0.8)
  

smartsizer documentation built on Jan. 6, 2021, 9:08 a.m.