Journal of clinical epidemiology
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There are numerous potential barriers to conducting randomized controlled trials (RCTs) in children. The purpose of this study was to compare the quantity, trends over time, characteristics, and quality of pediatric RCTs published in general medical journals (GMJs) with adult RCTs. ⋯ There may be significant barriers to the publication of high-quality pediatric RCTs in GMJs.
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The objective of this study is to outline explicit criteria for assessing the contribution of qualitative empirical studies in health and medicine, leading to a hierarchy of evidence specific to qualitative methods. ⋯ A hierarchy of evidence-for-practice specific to qualitative methods provides a useful guide for the critical appraisal of papers using these methods and for defining the strength of evidence as a basis for decision making and policy generation.
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To evaluate the methodological robustness of patient-reported outcomes (PROs) evaluation in complementary and alternative medicine (CAM) randomized controlled trials (RCTs) in oncology. ⋯ To facilitate the interpretation of results from such CAM RCTs, investigators are encouraged to pay greater attention to key methodological issues as identified in this study.
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Review Meta Analysis
A systematic review finds that methodological quality is better than its reputation but can be improved in physiotherapy trials in childhood cerebral palsy.
To identify critical issues in performing randomized controlled trials (RCTs) on complex interventions such as physiotherapy in multifaceted disabilities like cerebral palsy (CP); to systematically assess how well trials handled patient characteristics, key components of complex interventions, and outcome assessments; to make suggestions for improving the effectiveness of physiotherapy research. ⋯ We found good to fair methodological quality in a considerable number of RCTs on physiotherapy in CP children. Nevertheless, improvement is indicated for certain areas in design and performance of future studies. This review shows that informative RCTs on complex interventions for multifaceted disabilities are feasible.
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In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced by a new randomly chosen subject from the same source population. Imputation of missing data on a variable is replacing that missing by a value that is drawn from an estimate of the distribution of this variable. ⋯ But single imputation results in too small estimated standard errors, whereas multiple imputation results in correctly estimated standard errors and confidence intervals. In this article we explain why all this is the case, and use a simple simulation study to demonstrate our explanations. We also explain and illustrate why two frequently used methods to handle missing data, i.e., overall mean imputation and the missing-indicator method, almost always result in biased estimates.