Statistical perspective on adjusting for center effect in a 2 x 2 crossover design with application to clinical trials.
Patients for large-scale clinical trials are typically recruited from multiple centers to ensure adequate patient population and timely enrollment. In this note, carryover, treatment, and period effects for continuous data in multicenter studies with a 2 x 2 crossover design are examined using two types of covariate analysis adjusted for center effect proposed by Castellana and Patel (1). The correspondence among F-tests for various sources of variation in the two approaches is derived in detail. An example from a clinical study with complete and incomplete data is given to illustrate the use of the two types of covariate analysis. Also, the impact of complete and incomplete data on the covariate analyses is discussed along with the weighted squares of means analysis used in SAS procedure PROC GLM and residual maximum likelihood (REML) estimation employed in SAS procedure PROC MIXED.
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US Food and Drug Administration, Center for Devices and Radiological Health, Division of Biostatistics, Rockville, Maryland 20850, USA.
D000465:Algorithms; D000704:Analysis of Variance; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000188:drug therapy; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000627:therapeutic use; D016013:Likelihood Functions; D015233:Models, Statistical; D011446:Prospective Studies
D007004:Hypoglycemic Agents; D007328:Insulin