Comparison of Population-averaged and Cluster-specific Models for the Analysis of Cluster Randomized Trials with Missing Binary Outcomes: a Simulation Study

Overview

Journal BMC Med Res Methodol

Publisher Biomed Central

Specialties General Medicine
Health Services

Date 2013 Jan 25

PMID 23343209

Citations 12

Authors

Jinhui Ma

Parminder Raina

Joseph Beyene

Lehana Thabane

Affiliations

Soon will be listed here.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Citing Articles

Demystifying estimands in cluster-randomised trials.

Kahan B, Blette B, Harhay M, Halpern S, Jairath V, Copas A Stat Methods Med Res. 2024; 33(7):1211-1232.

PMID: 38780480 PMC: 11348634. DOI: 10.1177/09622802241254197.

Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies.

Offorha B, Walters S, Jacques R BMC Med Res Methodol. 2023; 23(1):293.

PMID: 38093221 PMC: 10717070. DOI: 10.1186/s12874-023-02107-z.

How to Improve Healthcare for Patients with Multimorbidity and Polypharmacy in Primary Care: A Pragmatic Cluster-Randomized Clinical Trial of the MULTIPAP Intervention.

Del Cura-Gonzalez I, Lopez-Rodriguez J, Leiva-Fernandez F, Gimeno-Miguel A, Poblador-Plou B, Lopez-Verde F J Pers Med. 2022; 12(5).

PMID: 35629175 PMC: 9144280. DOI: 10.3390/jpm12050752.

A Pragmatic Cluster Randomized Trial of an Electronic Clinical Decision Support System to Improve Chronic Kidney Disease Management in Primary Care: Design, Rationale, and Implementation Experience.

Khoong E, Karliner L, Lo L, Stebbins M, Robinson A, Pathak S JMIR Res Protoc. 2019; 8(6):e14022.

PMID: 31199334 PMC: 6594214. DOI: 10.2196/14022.

Efficacy of motivating short interventions for smokers in primary care (COSMOS trial): study protocol for a cluster-RCT.

Grischott T, Senn O, Rosemann T, Frei A, Cornuz J, Martin-Diener E Trials. 2019; 20(1):81.

PMID: 30683155 PMC: 6347802. DOI: 10.1186/s13063-018-3071-z.

References

Brown E, Graham J, Hawkins J, Arthur M, Baldwin M, Oesterle S . Design and analysis of the Community Youth Development Study longitudinal cohort sample. Eval Rev. 2009; 33(4):311-34. PMC: 2714913. DOI: 10.1177/0193841X09337356. View

Bellamy S, Gibberd R, Hancock L, Howley P, Kennedy B, Klar N . Analysis of dichotomous outcome data for community intervention studies. Stat Methods Med Res. 2000; 9(2):135-59. DOI: 10.1177/096228020000900205. View

Hawkins J, Oesterle S, Brown E, Arthur M, Abbott R, Fagan A . Results of a type 2 translational research trial to prevent adolescent drug use and delinquency: a test of Communities That Care. Arch Pediatr Adolesc Med. 2009; 163(9):789-98. PMC: 2740999. DOI: 10.1001/archpediatrics.2009.141. View

Murray D, Varnell S, Blitstein J . Design and analysis of group-randomized trials: a review of recent methodological developments. Am J Public Health. 2004; 94(3):423-32. PMC: 1448268. DOI: 10.2105/ajph.94.3.423. View

Taljaard M, Donner A, Klar N . Imputation strategies for missing continuous outcomes in cluster randomized trials. Biom J. 2008; 50(3):329-45. DOI: 10.1002/bimj.200710423. View

Ganz P, Farmer M, Belman M, Garcia C, Streja L, Dietrich A . Results of a randomized controlled trial to increase colorectal cancer screening in a managed care health plan. Cancer. 2005; 104(10):2072-83. DOI: 10.1002/cncr.21434. View

Donner A . An empirical study of cluster randomization. Int J Epidemiol. 1982; 11(3):283-6. DOI: 10.1093/ije/11.3.283. View

Zeger S, Liang K, Albert P . Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988; 44(4):1049-60. View

Feng Z, Diehr P, Peterson A, McLerran D . Selected statistical issues in group randomized trials. Annu Rev Public Health. 2001; 22:167-87. DOI: 10.1146/annurev.publhealth.22.1.167. View

10.

Clark N, Shah S, Dodge J, Thomas L, Andridge R, Little R . An evaluation of asthma interventions for preteen students. J Sch Health. 2010; 80(2):80-7. PMC: 3582177. DOI: 10.1111/j.1746-1561.2009.00469.x. View

11.

Austin P . A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes. Stat Med. 2007; 26(19):3550-65. DOI: 10.1002/sim.2813. View

12.

French S, Story M, Fulkerson J, Himes J, Hannan P, Neumark-Sztainer D . 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. PMC: 1183238. DOI: 10.1186/1479-5868-2-8. View

13.

Pate R, Ward D, Saunders R, Felton G, Dishman R, Dowda M . Promotion of physical activity among high-school girls: a randomized controlled trial. Am J Public Health. 2005; 95(9):1582-7. PMC: 1449401. DOI: 10.2105/AJPH.2004.045807. View

14.

Donner A, Brown K, Brasher P . A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989. Int J Epidemiol. 1990; 19(4):795-800. DOI: 10.1093/ije/19.4.795. View

15.

Neuhaus J . Statistical methods for longitudinal and clustered designs with binary responses. Stat Methods Med Res. 1992; 1(3):249-73. DOI: 10.1177/096228029200100303. View

16.

Groenwold R, Donders A, Roes K, Harrell Jr F, Moons K . Dealing with missing outcome data in randomized trials and observational studies. Am J Epidemiol. 2012; 175(3):210-7. DOI: 10.1093/aje/kwr302. View

17.

Burton A, Altman D, Royston P, Holder R . The design of simulation studies in medical statistics. Stat Med. 2006; 25(24):4279-92. DOI: 10.1002/sim.2673. View

18.

Ma J, Akhtar-Danesh N, Dolovich L, Thabane L . Imputation strategies for missing binary outcomes in cluster randomized trials. BMC Med Res Methodol. 2011; 11:18. PMC: 3055218. DOI: 10.1186/1471-2288-11-18. View

19.

Eldridge S, Ashby D, Feder G, Rudnicka A, Ukoumunne O . Lessons for cluster randomized trials in the twenty-first century: a systematic review of trials in primary care. Clin Trials. 2005; 1(1):80-90. DOI: 10.1191/1740774504cn006rr. View

20.

Lee E, Dubin N . Estimation and sample size considerations for clustered binary responses. Stat Med. 1994; 13(12):1241-52. DOI: 10.1002/sim.4780131206. View