Rule-of-thumb adjustment of sample sizes to accommodate dropouts in a two-stage analysis of repeated measurements

Recent contributions to the statistical literature have provided elegant model‐based solutions to the problem of estimating sample sizes for testing the significance of differences in mean rates of change across repeated measures in controlled longitudinal studies with differentially correlated erro...

Full description

Saved in:
Bibliographic Details
Published in:International journal of methods in psychiatric research 2006-03, Vol.15 (1), p.1-11
Main Authors: Overall, John E., Tonidandel, Scott, Starbuck, Robert R.
Format: Article
Language:English
Subjects:
Analysis of Variance
Bias
constant power
Data Interpretation, Statistical
dropouts
Humans
mixed models
Monte Carlo Method
Patient Dropouts
repeated measures
Reproducibility of Results
Research Design
Sample Size
Sampling Studies
two-stage analysis
Weights and Measures
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recent contributions to the statistical literature have provided elegant model‐based solutions to the problem of estimating sample sizes for testing the significance of differences in mean rates of change across repeated measures in controlled longitudinal studies with differentially correlated error and missing data due to dropouts. However, the mathematical complexity and model specificity of these solutions make them generally inaccessible to most applied researchers who actually design and undertake treatment evaluation research in psychiatry. In contrast, this article relies on a simple two‐stage analysis in which dropout‐weighted slope coefficients fitted to the available repeated measurements for each subject separately serve as the dependent variable for a familiar ANCOVA test of significance for differences in mean rates of change. This article is about how a sample of size that is estimated or calculated to provide desired power for testing that hypothesis without considering dropouts can be adjusted appropriately to take dropouts into account. Empirical results support the conclusion that, whatever reasonable level of power would be provided by a given sample size in the absence of dropouts, essentially the same power can be realized in the presence of dropouts simply by adding to the original dropout‐free sample size the number of subjects who would be expected to drop from a sample of that original size under conditions of the proposed study. Copyright © 2006 John Wiley & Sons, Ltd.
ISSN:1049-8931
1557-0657
DOI:10.1002/mpr.23