Using aggregate data to estimate the standard error of a treatment-covariate interaction in an individual patient data meta-analysis.
Subgroup analyses are important to medical research because they shed light on the heterogeneity of treatment effectts. A treatment-covariate interaction in an individual patient data (IPD) meta-analysis is the most reliable means to estimate how a subgroup factor modifies a treatment's effectiveness. However, owing to the challenges in collecting participant data, an approach based on aggregate data might be the only option. In these circumstances, it would be useful to assess the relative efficiency and power loss of a subgroup analysis without patient-level data. We present methods that use aggregate data to estimate the standard error of an IPD meta-analysis' treatment-covariate interaction for regression models of a continuous or dichotomous patient outcome. Numerical studies indicate that the estimators have good accuracy. An application to a previously published meta-regression illustrates the practical utility of the methodology.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20852, USA. kovalchiksa@mail.nih.gov
Stephanie A
SA
Kovalchik
William G
WG
Cumberland
D000319:Adrenergic beta-Antagonists / Q000494:pharmacology; D000704:Analysis of Variance; D003627:Data Interpretation, Statistical; D005260:Female; D005767:Gastrointestinal Diseases / Q000517:prevention & control; D006470:Hemorrhage / Q000517:prevention & control; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D008875:Middle Aged; D012044:Regression Analysis; D016896:Treatment Outcome; D035501:Uncertainty
D000319:Adrenergic beta-Antagonists