The online version of this article (doi:10.1186/1471-2288-13-9) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
JM, PR, JB, and LT conceived the research question. JM conducted literature review, designed and implemented the simulation study, composed the initial draft of the manuscript, and revised the manuscript. LT oversaw the design and implementation of the study, revised the manuscript. PR and JB provided assistance with design of the simulation study. All authors read and approved the final manuscript.
The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses.
In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE), and coverage probability.
GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small) is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF) <3; within-cluster MI for CRTs with VIF≥3 and cluster size>50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied.
GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.
Authors’ original file for figure 112874_2012_865_MOESM1_ESM.pdf
Authors’ original file for figure 212874_2012_865_MOESM2_ESM.tiff
Authors’ original file for figure 312874_2012_865_MOESM3_ESM.tiff
Authors’ original file for figure 412874_2012_865_MOESM4_ESM.tiff
Authors’ original file for figure 512874_2012_865_MOESM5_ESM.tiff
Donner A, Klar N: Design and Analysis of Cluster Randomization Trials in Health Research. 2000, New York: John Wiley & Sons
Little RJA, Rubin DB: Statistical analysis with missing data. 2002, New-York: John Wiley & Sons, 2
Ma J, Raina P, Beyene J, Thabane L: Comparing the performance of different multiple imputation strategies for missing binary outcomes in cluster randomized trials: a simulation study. J Open Access Med Stat. 2012, 2: 93-103.
Liang K, Zeger S: Longitudinal data analysis using generalized linear models. Biometrika. 1986, 73 (1): 13-22. 10.1093/biomet/73.1.13. CrossRef
McCulloch CE, Searle SR: Generalized, Linear and Mixed Models. 2001, New York: John Wiley & Sons Inc
Rubin DB: Multiple imputation after 18+ years. J Am Stat Assoc. 1996, 91: 473-489. 10.1080/01621459.1996.10476908. CrossRef
Barnard J, Rubin DB: Small-sample degrees of freedom with multiple imputation. Biometrika. 1999, 86: 949-955. CrossRef
French SA, Story M, Fulkerson JA, Himes JH, Hannan P, Neumark-Sztainer D, Ensrud K: Increasing weight-bearing physical activity and calcium-rich foods to promote bone mass gains among 9–11 year old girls: outcomes of the cal-girls study. Int J Behav Nutr Phys Act. 2005, 2: 8-10.1186/1479-5868-2-8. CrossRefPubMedPubMedCentral
- Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study
- BioMed Central
Neu im Fachgebiet AINS
Meistgelesene Bücher aus dem Fachgebiet AINS
Mail Icon II