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  • Bayesians and frequentists.

    3 November 2018

  • Transforming data.

    3 November 2018

  • Blinding and exclusions after allocation in randomised controlled trials: survey of published parallel group trials in obstetrics and gynaecology.

    3 November 2018

    OBJECTIVE: To assess the methodological quality of approaches to blind ing and to handling of exclusions as reported in randomised trials from one medical specialty. DESIGN: Survey of published, parallel group randomised controlled trials. DATA SOURCES: A random sample of 110 reports in which allocation was described as randomised from 1990 and 1991 volumes of four journals of obstetrics and gynaecology. MAIN OUTCOME MEASURES: The adequacy of the descriptions of double blinding and exclusions after randomisation. RESULTS: Through 31 trials reported being double blind, about twice as many could have been. Of the 31 trials only eight (26%) provided information on the protection of the allocation schedule and only five (16%) provided some written assurance of successful implementation of double blinding. Of 38 trials in which the authors provided sufficient information for readers to infer that no exclusions after randomisation had occurred, six (16%) reported adequate allocation concealment and none stated that an intention to treat analysis had been performed. That compared with 14 (27%) and six (12%), respectively, for the 52 trials that reported exclusions. CONCLUSIONS: Investigators could have double blinded more often. When they did double blind, they reported poorly and rarely evaluated it. Paradoxically, trials that reported exclusions seemed generally of a higher methodological standard than those that had no apparent exclusions. Exclusions from analysis may have been made in some of the trials in which no exclusions were reported. Editors and readers of reports of randomised trials should understand that flawed reporting of exclusions may often provide a misleading impression of the quality of the trial.

  • Cronbach's alpha.

    3 November 2018

  • The relation between treatment benefit and underlying risk in meta-analysis.

    3 November 2018

    In meta-analyses of clinical trials comparing a treated group with a control group it has been common to ask whether the treatment benefit varies according to the underlying risk of the patients in the different trials, with the hope of defining which patients would benefit most and which least from medical interventions. The usual analysis used to investigate this issue, however, which uses the observed proportions of events in the control groups of the trials as a measure of the underlying risk, is flawed and produces seriously misleading results. This arises through a bias due to regression to the mean and will be particularly acute in meta-analyses which include some small trials or in which the variability in the true underlying risks across trials is small. Approaches which previously have been thought to be more appropriate are to substitute the average proportion of events in the control and treated groups as the measure of underlying risk or to plot the proportion of events in the treated group against that in the control group (L'Abbé plot). However, these are still subject to bias in most circumstances. Because of the potentially seriously flawed conclusions that can result from such analyses, they should be replaced either by statistically appropriate (but more complex) approaches or, preferably, by analyses which investigate the dependence of the treatment effect on measured baseline characteristics of the patients in each trial.

  • Measurement error.

    3 November 2018