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The Original Study
The study evaluates the effect of shifting the task of antiretroviral therapy (ART) from doctors to nurses. The study analyses the effect of Streamlining Tasks and Roles to Expand Treatment and Care for HIV (STRETCH), a programme that trains nurses on ART and health outcomes. To evaluate the programme effects, a cluster-randomised trial was implemented between January 2008 and June 2010 in South Africa. 31 primary-care centres were randomly allocated to the control group (15 clinics) and the intervention group (16 clinics) in which the STRETCH programme was implemented. Two cohorts of patients were eligible: (i) adults not receiving ART, who had CD4 counts of 350 cells per μL or less and (ii) adults who had received ART for at least 6 months. The researchers found that the intervention did not reduce mortality. On the second cohort, the time of death did not differ across control and treatment groups, and 19 per cent of patients died in the control group while 20 per cent died in the intervention group. The research also found that the intervention improved the quality of care and health outcomes.
The first objective is to replicate the original statistical analyses from Fairall et al.(2012) to verify their conclusions. The replication researchers will assess whether the published findings can be reproduced using the study’s data and methods. The second objective is to conduct a measurement and estimation analysis (MEA) to investigate the robustness of the findings through additional analysis methodology. Specifically, the researchers will (i) assess the validity of original models by checking all the assumptions of the statistical methods in this paper, and propose and apply alternative measures when any of these assumptions are violated; (2) apply advanced clustered-data analysis methods such as frailty models and the generalised mixed-effects models to account for correlations/heterogeneity of patients in the same clinic; (3) account for incomplete data to conduct statistical analysis to verify and generate new findings based on the original analyses. The researchers will use multiple imputation method to handle nonrandom missing data.