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find Keyword "Selection bias" 2 results
  • Analysis of the application of randomization methods in clinical trials in NEJM

    ObjectiveTo analyze the current status of the application of randomization methods in randomized controlled trials (RCTs) which have been published in New England Journal of Medicine (NEJM).MethodsRCTs published by NEJM in 2018 were searched and collected. The characteristics of clinical trial design elements and the application status of randomization methods were comprehensively analyzed to distinguish blind trial and non-blind trial, and characteristics of application of randomization methods and selection of allocation concealment mechanisms of non-blind trials were summarized.ResultsA total of 151 RCTs were published in NEJM in 2018, in which blinded trials and non-blinded trials accounted for 75 (49.67%) and 76 (50.33%), respectively. 34 (22.52%) RCTs did not report specific randomization methods, and the remaining 117 (77.48%) reported. Among the latter, stratified block randomization accounted for the main body (72.65%), followed by block randomization (11.11%), minimization method (9.40%), simple randomization method (4.27%) and the others. There was no significant difference in the proportion of reporting or using randomization methods between blind and non-blind trials (P>0.05). In 76 non-blind trials, 38 (50.00%) clearly reported the concealment method of random allocation, among which 37 used the central randomization (97.37%) and 1 used envelope method (2.63%).ConclusionsThe current RCTs published in NEJM still have problems in the selection of randomization methods to be optimized and the transparency of randomization reporting to be improved.

    Release date:2021-04-23 04:04 Export PDF Favorites Scan
  • Statistical methods in pragmatic randomized controlled trials (Ⅱ): Addressing missing outcome data

    Pragmatic randomized controlled trials can provide high-quality evidence. However, pragmatic trials need to frequently encounter the missing outcome data due to the challenges of quality assurance and control. The missing outcome could lead to bias which may misguide the conclusions. Thus, it is crucial to handle the missing outcome data appropriately. Our study initially summarized the bias structures and missingness mechanisms, and then reviewed important methods based on the assumption of missing at random. We referred to the multiple imputations and inverse probability of censoring weighting for dealing with missing outcomes. This paper aimed to provide insights on how to choose the statistical methods on missing outcome data.

    Release date:2021-07-22 06:18 Export PDF Favorites Scan
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