Journal of clinical epidemiology
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Statisticians have criticized the use of significance testing to compare the distribution of baseline covariates between treatment groups in randomized controlled trials (RCTs). Furthermore, some have advocated for the use of regression adjustment to estimate the effect of treatment after adjusting for potential imbalances in prognostically important baseline covariates between treatment groups. ⋯ Our findings suggest the need for greater editorial consistency across journals in the reporting of RCTs. Furthermore, there is a need for greater debate about the relative merits of unadjusted vs. adjusted estimates of treatment effect.
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Randomized controlled trials (RCTs) are considered the highest grade of research evidence, yet properly conducted trials investigating the same association often yield conflicting results. Our objective was to assess whether variability in treatment protocols of RCTs investigating the same topic could explain distinct patterns of outcomes. ⋯ Conflicting results from RCTs can represent a spectrum of "real" outcomes for specific treatments. Such trials are best evaluated by considering concurrently both the validity of study design as well as the generalizability of patients and interventions involved.
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To show how the bivariate random effects meta-analysis model can be used to study the relation between the explanatory variables and the performance of diagnostic tests as characterized by a summary receiver operating characteristic curve (SROCC). ⋯ The bivariate random effects meta-analysis model is an appropriate and convenient framework to investigate the effect of covariates on the performance of diagnostic tests as measured by SROCCs.