Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study.

ABSTRACT: BACKGROUND : In spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Commonly used methods to analyze such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an

SUBMITTER: Martins A 

PROVIDER: S-EPMC9741489 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature