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 a United States President’s Emergency Plan for AIDS Relief (PEPFAR)-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.
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