cps.did.normal: Power simulations for cluster-randomized trials: Difference...

In clusterPower: Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials

Description

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This set of functions utilize iterative simulations to determine approximate power for difference in difference cluster-randomized controlled trials. Users can modify a variety of parameters to suit the simulations to their desired experimental situation.

Runs the power simulation for difference in difference (DID) cluster-randomized controlled trial.

Users must specify the desired number of simulations, number of subjects per cluster, number of clusters per arm, expected absolute difference between arms, two of the following: ICC, within-cluster variance, or between-cluster variance; significance level, analytic method, progress updates, and simulated data set output may also be specified.

Usage

cps.did.normal(
  nsim = NULL,
  nsubjects = NULL,
  nclusters = NULL,
  delta_mu = 0,
  delta_mu2 = NULL,
  sigma_sq = NULL,
  sigma_b_sq0 = NULL,
  sigma_b_sq1 = 0,
  alpha = 0.05,
  method = "glmm",
  poorFitOverride = FALSE,
  lowPowerOverride = FALSE,
  timelimitOverride = TRUE,
  quiet = FALSE,
  allSimData = FALSE,
  seed = NA,
  nofit = FALSE
)

Arguments

nsim	Number of datasets to simulate; accepts integer (required).
nsubjects	Number of subjects per arm; accepts either a scalar (equal cluster sizes, both groups), a vector of length two (equal cluster sizes within groups), or a vector of length sum(nclusters) (unequal cluster sizes within groups) (required).
nclusters	Number of clusters per group; accepts integer scalar or vector of length 2 for unequal number of clusters per arm (required)
delta_mu	Default = 0. Reference arm expected change between from baseline to followup.
delta_mu2	Expected change in tretament arm at follow-up; accepts numeric (required).
sigma_sq	Within-cluster variance; accepts numeric scalar (indicating equal within-cluster variances for both arms at both time points) or vector of length 4 specifying within-cluster variance for each arm at each time point.
sigma_b_sq0	Pre-treatment (time == 0) between-cluster variance; accepts numeric scalar (indicating equal between-cluster variances for both arms) or a vector of length 2 specifying treatment-specific between-cluster variances
sigma_b_sq1	Post-treatment (time == 1) between-cluster variance; accepts numeric scalar (indicating equal between-cluster variances for both arm) or a vector of length 2 specifying treatment-specific between-cluster variances. For data simulation, sigma_b_sq1 is added to sigma_b_sq0, such that if sigma_b_sq0 = 5 and sigma_b_sq1 = 2, the between-cluster variance at time == 1 equals 7. Default = 0.
alpha	Significance level. Default = 0.05.
method	Analytical method, either Generalized Linear Mixed Effects Model (GLMM) or Generalized Estimating Equation (GEE). Accepts c('glmm', 'gee') (required); default = 'glmm'.
poorFitOverride	Option to override stop() if more than 25% of fits fail to converge; default = FALSE.
lowPowerOverride	Option to override stop() if the power is less than 0.5 after the first 50 simulations and every ten simulations thereafter. On function execution stop, the actual power is printed in the stop message. Default = FALSE. When TRUE, this check is ignored and the calculated power is returned regardless of value.
timelimitOverride	Logical. When FALSE, stops execution if the estimated completion time is more than 2 minutes. Defaults to TRUE.
quiet	When set to FALSE, displays simulation progress and estimated completion time; default is FALSE.
allSimData	Option to output list of all simulated datasets; default = FALSE.
seed	Option to set the seed. Default is NA.
nofit	Option to skip model fitting and analysis and only return the simulated data. Default = FALSE.

Value

A list with the following components:

• Character string indicating total number of simulations and simulation type

• Number of simulations

• Data frame with columns "Power" (Estimated statistical power), "lower.95.ci" (Lower 95 "upper.95.ci" (Upper 95

• Analytic method used for power estimation

• Significance level

• Vector containing user-defined cluster sizes

• Vector containing user-defined number of clusters

• Data frame reporting ICC, within & between cluster variances for both arms at each time point

• Vector containing expected difference between groups based on user inputs

• Data frame with columns: "Period" (Pre/Post-treatment indicator), "Arm.2" (arm indicator), "Value" (Mean response value)

• Data frame with columns: "Estimate" (Estimate of treatment effect for a given simulation), "Std.err" (Standard error for treatment effect estimate), "Test.statistic" (z-value (for GLMM) or Wald statistic (for GEE)), "p.value", "sig.val" (Is p-value less than alpha?)

• If allSimData = TRUE, a list of data frames, each containing: "y" (Simulated response value), "trt" (Indicator for arm), "clust" (Indicator for cluster), "period" (Indicator for time point)

If nofit = T, a data frame of the simulated data sets, containing:

• "arm" (Indicator for treatment arm)

• "cluster" (Indicator for cluster)

• "y1" ... "yn" (Simulated response value for each of the nsim data sets).

Author(s)

Alexander R. Bogdan

Alexandria C. Sakrejda ([e-mail])

Ken Kleinman ([e-mail])

Examples

# Estimate power for a trial with 6 clusters in arm 1 and 6 clusters in arm 2,
# those clusters having 120 subjects each, with sigma_sq = 1. Estimated
# arm mean changes are 0 and 0.48 in the first and second arms, respectively, and we use
# 100 simulated data sets analyzed by the GLMM method. The resulting estimated
# power (for seed = 123) should be 0.81.

## Not run: 
normal.did.rct = cps.did.normal(nsim = 100, nsubjects = 120, nclusters = 6,
                                delta_mu = 0, delta_mu2 = 0.48, sigma_sq = 1, alpha = 0.05,
                                sigma_b_sq0 = 0.1, method = 'glmm', seed = 123)

## End(Not run)

clusterPower documentation built on Jan. 29, 2021, 1:06 a.m.