Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study

<p>Abstract</p> <p>Background</p> <p>Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs.</p> <p>Methods</p> <p>Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure.</p> <p>Results</p> <p>While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis.</p> <p>Conclusions</p> <p>All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.</p

HIV Infection and Testing among Latino Men Who Have Sex with Men in the United States: The Role of Location of Birth and Other Social Determinants

BACKGROUND: In the United States, Latino men who have sex with men (MSM) are disproportionately affected by HIV. Latino MSM are a diverse group who differ culturally based on their countries or regions of birth and their time in the United States. We assessed differences in HIV prevalence and testing among Latino MSM by location of birth, time since arrival, and other social determinants of health. METHODS: For the 2008 National HIV Behavioral Surveillance System, a cross-sectional survey conducted in large US cities, MSM were interviewed and tested for HIV infection. We used generalized estimating equations to test associations between various factors and 1) prevalent HIV infection and 2) being tested for HIV infection in the past 12 months. RESULTS: Among 1734 Latino MSM, HIV prevalence was 19%. In multivariable analysis, increasing age, low income, and gay identity were associated with HIV infection. Moreover, men who were U.S.-born or who arrived ≥5 years ago had significantly higher HIV prevalence than recent immigrants. Among men not reporting a previous positive HIV test, 63% had been tested for HIV infection in the past 12 months; recent testing was most strongly associated with having seen a health care provider and disclosing male-male attraction/sexual behavior to a health care provider. CONCLUSIONS: We identified several social determinants of health associated with HIV infection and testing among Latino MSM. Lower HIV prevalence among recent immigrants contrasts with higher prevalence among established immigrants and suggests a critical window of opportunity for HIV prevention, which should prioritize those with low income, who are at particular risk for HIV infection. Expanding health care utilization and encouraging communication with health care providers about sexual orientation may increase testing

Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study

<p>Abstracts</p> <p>Background</p> <p>The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE)) and cluster-specific (i.e. random-effects logistic regression (RELR)) models for analyzing data from cluster randomized trials (CRTs) with missing binary responses.</p> <p>Methods</p> <p>In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI) and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE), and coverage probability.</p> <p>Results</p> <p>GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small) is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF) <3; within-cluster MI for CRTs with VIF≥3 and cluster size>50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied.</p> <p>Conclusion</p> <p>GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.</p