Choosing profile double-sampling designs for survival estimation with application to President's Emergency Plan for AIDS Relief evaluation

• Published in: Statistics in medicine Vol. 33; no. 12; pp. 2017 - 2029
• Main Authors: An, Ming-Wen, Frangakis, Constantine E., Yiannoutsos, Constantin T.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 30.05.2014; Wiley Subscription Services, Inc
• Subjects: Acquired immune deficiency syndrome; Acquired Immunodeficiency Syndrome - mortality; AIDS; Algorithms; covariates; Disaster relief; double sampling; dropouts; Estimating techniques; HIV; Human immunodeficiency virus; Humans; International Cooperation; Kenya - epidemiology; loss to follow-up; Mathematical functions; Medical treatment; PEPFAR; potential outcomes; profile sampling; Research Design; Sampling Studies; Sampling techniques; survival; Survival Analysis; United States; Kenya; United States; United States > US; double sampling; dropouts; potential outcomes; HIV; loss to follow-up; survival; covariates; PEPFAR; profile sampling
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6087

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample. In limited‐resource settings, however, generating accurate estimates using a minimum of resources is important. We propose using double‐sampling designs that oversample certain profiles of dropouts as more efficient alternatives to random designs. First, we develop a framework to estimate the survival function under these profile double‐sampling designs. We then derive the precision of these designs as a function of the rule for selecting different profiles, in order to identify more efficient designs. We illustrate using data from the United States President's Emergency Plan for AIDS Relief‐funded HIV care and treatment program in western Kenya. Our results show why and how more efficient designs should oversample patients with shorter dropout times. Further, our work suggests generalizable practice for more efficient double‐sampling designs, which can help maximize efficiency in resource‐limited settings. Copyright © 2014 John Wiley & Sons, Ltd.