Systematic missing-at-random (SMAR) design and analysis for translational research studies.
Translational research studies often involve a central study (e.g. clinical trial, cohort of patients, etc.) and multiple investigators who are each interested in addressing different research questions using the same patient population. However, it is often impossible for the investigators to include all patients in all of the ancillary translational research substudies that are part of the main study. This arises due to time and budgetary constraints and other logistical considerations. In this paper, we propose a prospective Systematic Missing-At-Random study design (SMAR) with planned partially missing covariates collected using a nested random sampling scheme that allows an integrated statistical analysis across all domains of data. We propose an algorithm for data analysis that incorporates the features of the design. We show that the SMAR design is computationally and statistically efficient as well as cost effective using simulation studies and a published data example. An extension to a two-stage prospective-retrospective design is discussed.
New York University School ofMedicine, New York, USA.
Ilana
I
Belitskaya-Levy
Yongzhao
Y
Shao
Judith D
JD
Goldberg
D000465:Algorithms; D056808:Biostatistics / Q000379:methods*; D016022:Case-Control Studies; D015331:Cohort Studies; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D011446:Prospective Studies; D012189:Retrospective Studies; D018401:Sample Size; D057170:Translational Research, Biomedical / Q000191:economics / Q000706:statistics & numerical data*