Abstractobjectives Two common methods used to measure indicators for health programme monitoring and evaluation are the demographic and health surveys (DHS) and lot quality assurance sampling (LQAS); each one has different strengths. We report on both methods when utilised in comparable situations.methods We compared 24 indicators in south-west Uganda, where data for prevalence estimations were collected independently for the two methods in 2011 (LQAS: n = 8876; DHS: n = 1200). Data were stratified (e.g. gender and age) resulting in 37 comparisons. We used a two-sample two-sided Ztest of proportions to compare both methods.results The average difference between LQAS and DHS for 37 estimates was 0.062 (SD = 0.093; median = 0.039). The average difference among the 21 failures to reject equality of proportions was 0.010 (SD = 0.041; median = 0.009); among the 16 rejections, it was 0.130 (SD = 0.010, median = 0.118). Seven of the 16 rejections exhibited absolute differences of <0.10, which are clinically (or managerially) not significant; 5 had differences >0.10 and <0.20 (mean = 0.137, SD = 0.031) and four differences were >0.20 (mean = 0.261, SD = 0.083).conclusion There is 75.7% agreement across the two surveys. Both methods yield regional results, but only LQAS provides information at less granular levels (e.g. the district level) where managerial action is taken. The cost advantage and localisation make LQAS feasible to conduct more frequently, and provides the possibility for real-time health outcomes monitoring.keywords monitoring and evaluation, stratified sampling, cluster sampling, lot quality assurance sampling, demographic and health survey, Uganda
The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a group‐specific effect that differs from the population average. In fact, it is possible that there is underlying systematic effect heterogeneity that is obscured by focusing on the population mean estimand. In this context, understanding which covariates contribute to this treatment effect heterogeneity (TEH) and how these covariates determine the differential treatment effect (TE) is an important consideration. Towards such an understanding, this paper briefly reviews three approaches used in making causal inferences and conducts a simulation study to compare these approaches according to their performance in an exploratory evaluation of TEH when the heterogeneous subgroups are not known a priori. Performance metrics include the detection of any heterogeneity, the identification and characterization of heterogeneous subgroups, and unconfounded estimation of the TE within subgroups. The methods are then deployed in a comparative effectiveness evaluation of drug‐eluting versus bare‐metal stents among 54 099 Medicare beneficiaries in the continental United States admitted to a hospital with acute myocardial infarction in 2008.
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