Strategies for monitoring and evaluation of resource-limited national antiretroviral therapy programs: the two-phase design

Abstract

Background: In resource-limited settings, monitoring and evaluation (M&E) of antiretroviral treatment (ART) programs often relies on aggregated facility-level data. Such data are limited, however, because of the potential for ecological bias, although collecting detailed patient-level data is often prohibitively expensive. To resolve this dilemma, we propose the use of the two-phase design. Specifically, when the outcome of interest is binary, the two-phase design provides a framework within which researchers can resolve ecological bias through the collection of patient-level data on a sub-sample of individuals while making use of the routinely collected aggregated data to obtain potentially substantial efficiency gains. Methods: Between 2005–2007, the Malawian Ministry of Health conducted a one-time cross-sectional survey of 82,887 patients registered at 189 ART clinics. Using these patient data, an aggregated dataset is constructed to mimic the type of data that it routinely available. A hypothetical study of risk factors for patient outcomes at 6 months post-registration is considered. Analyses are conducted based on: (i) complete patient-level data; (ii) aggregated data; (iii) a hypothetical case–control study; (iv) a hypothetical two-phase study stratified on clinic type; and, (v) a hypothetical two-phase study stratified on clinic type and registration year. A simulation study is conducted to compare statistical power to detect an interaction between clinic type and year of registration across the designs. Results: Analyses and conclusions based solely on aggregated data may suffer from ecological bias. Collecting and analyzing patient data using either a case–control or two-phase design resolves ecological bias to provide valid conclusions. To detect the interaction between clinic type and year of registration, the case–control design would require a prohibitively large sample size. In contrast, a two-phase design that stratifies on clinic and year of registration achieves greater than 85% power with as few as 1,000 patient samples. Conclusions: Two-phase designs have the potential to augment current M&E efforts in resource-limited settings by providing a framework for the collection and analysis of patient data. The design is cost-efficient in the sense that it often requires far fewer patients to be sampled when compared to standard designs.