n4meansMeta {CRTSize}

Empirical Power and Variance Reduction for an Updated Fixed Effects Meta-Analysis in Cluster Randomized Trials

Description

This function provides the empirical power/reduction in variance in an updated meta-analysis for a vector of number of clusters to randomize per group and a vector of estimates of the ICC.

Usage

n4meansMeta(data, model="fixed", k, ICC, ICCDistn="unif", lower=0, upper=0.25, 
varRed=FALSE, m, sdm, meanC, sdC, sdT=sdC, iter=1000, alpha=0.05)

Arguments

data

A matrix of individual studies (each row). The first column contains the estimate of the mean difference, second column contains the lower 95 % confidence limit and the third contains the upper 95 % confidence limit.

model

One of fixed or random, corresponding to the fixed or random effects meta-analysis models. Note that the random effects model is estimated according to the DerSimonian-Laird estimate of the between-study variance.

k

A vector of potential number of clusters to randomize to each of the treatment and control groups. Note that this function assumes an equal allocation to treatment and control group status.

ICC

A vector of potential values of the ICC, these can be obtained from the literature, pilot studies, etc.

ICCDistn

The hypothetical distribution of the ICC values. This can be set to "fixed" (note that only one ICC value is accepted for this option), "unif" on the range [lower, upper], "normal", corresponding to the truncated normal distribution (Turner et al, 2004), and "smooth" corresponding to the empirical smoothing option. (Rotondi and Donner, 2009)

lower

The lower bound for the smoothing or unif options. Default value is zero.

upper

The upper bound for the smoothing or unif options. Default value is 0.25.

varRed

Logical; If varRed is set to TRUE, the proportionate reduction of variance is displayed for the fixed effects meta-analysis.

m

The mean cluster size.

sdm

The standard deviation of the mean cluster size. This adds additional real-world variation in the simulated study.

meanC

The anticipated mean response level in the control group. The anticipated treatment mean is calculated from the simulated effect size of the preliminary meta-analysis.

sdC

The standard deviation of the control rate. This adds real-world variation in the simulated study and can be precise or imprecise depending on the investigators preference.

sdT

The standard deviation of the treatment rate. By default, this is set to the same sdC.

iter

The number of iterations for each value of k and the ICC. This has a large impact on computational time. Default is 1000.

alpha

The desired type I error rate for calculation of confidence limits for the meta-analysis model. Note that for simplicity, this function assumes that each of the inputed lower and upper limits are 95 % confidence limits and this cannot be changed.

Details

This function calculates the empirical power of an updated meta-analysis by a generalization of Sutton et al. (2007). The procedure is summarized in the accompanying manuscript (Rotondi and Donner, 2012). In short, a hypothetical new study of a given size is simulated, then added to the meta-analysis. The results are re-meta-analyzed and it is determined whether the result is statistically significant. Note that the proportion of variance reduction and power may not always strictly decrease with k, as the simulation exhibits individual-level variation. Moreover, the random effects model does not guarantee that future studies will result in higher power due to the presence of between-study heterogeneity.

Value

power

The power of the updated meta-analysis. Presented as a matrix of number of clusters by ICC values.

data

The data matrix is returned.

newMean

The preliminary fixed (or random) effects mean difference.

newVar

The variance of the preliminary fixed (or random) effects mean difference (MD).

lF

The 100(1 - \alpha) % lower limit of the MD in the original meta-analysis.

uF

The 100(1 - \alpha) % upper limit of the MD in the original meta-analysis.

Var

The variance of the effect measure.

Sig

Is the result statistically significant (Binary zero or one).

k

The number of clusters randomized per group (vector).

ICC

A vector of ICC values.

ICCDistn

The distributional assumption about the ICC.

varRed

Variance Reduction: Logical.

varianceReduction

The proportionate reduction in variance for the number of clusters in the fixed effects meta-analysis.

m

The mean cluster size.

sdm

The standard deviation of the mean cluster size.

meanC

The control mean.

sdC

The standard deviation of the control mean.

alpha

The desired type I error rate.

iter

The total number of iterations.

Author(s)

Michael Rotondi, [e-mail]

References

Matthews JNS. Introduction to Randomized Controlled Clinical Trials (2nd Ed.) Chapman & Hall: New York, 2006.

Donner A and Klar N. Design and Analysis of Cluster Randomization Trials in Health Research. Arnold: London, 2000.

Sutton AJ et al. (2007) Evidence-based sample size calculations based upon updated meta-analysis. Statistics in Medicine, 26(12):2479-2500.

Turner R et al. (2004) Allowing for imprecision in the intracluster correlation coefficient in the design of cluster randomized trials. Statistics in Medicine, 23(8):1195-1214.

Rotondi M and Donner A. (2009) Sample Size Estimation in Cluster Randomized Trials: An Empirical Bayes Approach. Journal of Educational and Behavioral Statistics. DOI: 10.3102/1076998609332756.

Rotondi M and Donner A. (2012) Sample Size Estimation in Cluster Randomized Trials: An Evidence-Based Perspective. Computational Statistics and Data Analysis 56:1174-1187.

See Also

n4propsMeta

Examples

## Not run: A brief example with 5 iterations.  
n4meansMeta(data=rbind(c(100, 50, 150), c(25, -100, 150), c(-90, -190, 10), 
c(-125, -200, -50)), model="fixed", k=c(10, 20), ICC=c(0.1, 0.15, 0.18), m=100, 
sdm=0, meanC=100, sdC=10, iter=5, alpha=0.05, varRed=TRUE, ICCDistn="smooth");