Two‐wave two‐phase outcome‐dependent sampling designs, with applications to longitudinal binary data

• Published in: Statistics in medicine Vol. 40; no. 8; pp. 1863 - 1876
• Main Authors: Tao, Ran, Mercaldo, Nathaniel D., Haneuse, Sebastien, Maronge, Jacob M., Rathouz, Paul J., Heagerty, Patrick J., Schildcrout, Jonathan S.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 15.04.2021
• Subjects: ascertainment corrected maximum likelihood; Cohort Studies; Humans; Longitudinal Studies; marginal model; marginalized model; Models, Statistical; multiple imputation; multiwave design; Research Design; Sample Size; time‐dependent covariate; multiple imputation; multiwave design; ascertainment corrected maximum likelihood; time-dependent covariate; marginalized model; marginal model
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8876

Two‐phase outcome‐dependent sampling (ODS) designs are useful when resource constraints prohibit expensive exposure ascertainment on all study subjects. One class of ODS designs for longitudinal binary data stratifies subjects into three strata according to those who experience the event at none, some, or all follow‐up times. For time‐varying covariate effects, exclusively selecting subjects with response variation can yield highly efficient estimates. However, if interest lies in the association of a time‐invariant covariate, or the joint associations of time‐varying and time‐invariant covariates with the outcome, then the optimal design is unknown. Therefore, we propose a class of two‐wave two‐phase ODS designs for longitudinal binary data. We split the second‐phase sample selection into two waves, between which an interim design evaluation analysis is conducted. The interim design evaluation analysis uses first‐wave data to conduct a simulation‐based search for the optimal second‐wave design that will improve the likelihood of study success. Although we focus on longitudinal binary response data, the proposed design is general and can be applied to other response distributions. We believe that the proposed designs can be useful in settings where (1) the expected second‐phase sample size is fixed and one must tailor stratum‐specific sampling probabilities to maximize estimation efficiency, or (2) relative sampling probabilities are fixed across sampling strata and one must tailor sample size to achieve a desired precision. We describe the class of designs, examine finite sampling operating characteristics, and apply the designs to an exemplar longitudinal cohort study, the Lung Health Study.