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The thresholds for statistical and clinical significance – a five-step procedure for evaluation of intervention effects in randomised clinical trials

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TLDR
Assessment of intervention effects in randomised clinical trials deserves more rigour in order to become more valid, and the proposed five-step procedure may increase the validity of assessments of interventions in randomising clinical trials.
Abstract
Thresholds for statistical significance are insufficiently demonstrated by 95% confidence intervals or P-values when assessing results from randomised clinical trials. First, a P-value only shows the probability of getting a result assuming that the null hypothesis is true and does not reflect the probability of getting a result assuming an alternative hypothesis to the null hypothesis is true. Second, a confidence interval or a P-value showing significance may be caused by multiplicity. Third, statistical significance does not necessarily result in clinical significance. Therefore, assessment of intervention effects in randomised clinical trials deserves more rigour in order to become more valid. Several methodologies for assessing the statistical and clinical significance of intervention effects in randomised clinical trials were considered. Balancing simplicity and comprehensiveness, a simple five-step procedure was developed. For a more valid assessment of results from a randomised clinical trial we propose the following five-steps: (1) report the confidence intervals and the exact P-values; (2) report Bayes factor for the primary outcome, being the ratio of the probability that a given trial result is compatible with a ‘null’ effect (corresponding to the P-value) divided by the probability that the trial result is compatible with the intervention effect hypothesised in the sample size calculation; (3) adjust the confidence intervals and the statistical significance threshold if the trial is stopped early or if interim analyses have been conducted; (4) adjust the confidence intervals and the P-values for multiplicity due to number of outcome comparisons; and (5) assess clinical significance of the trial results. If the proposed five-step procedure is followed, this may increase the validity of assessments of intervention effects in randomised clinical trials.

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Citations
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When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

TL;DR: This work considers how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommends analytical approaches which may prevent bias caused by unavoidable missing data.
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Trial Sequential Analysis in systematic reviews with meta-analysis

TL;DR: Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.
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Thresholds for statistical and clinical significance in systematic reviews with meta-analytic methods

TL;DR: An eight-step procedure for better validation of meta-analytic results in systematic reviews of randomised clinical trials is proposed, which will increase the validity of assessments of intervention effects in systematic Reviews of Randomised Clinical trials.
References
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Journal ArticleDOI

A Simple Sequentially Rejective Multiple Test Procedure

TL;DR: In this paper, a simple and widely accepted multiple test procedure of the sequentially rejective type is presented, i.e. hypotheses are rejected one at a time until no further rejections can be done.
Journal ArticleDOI

CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials

TL;DR: The Consort 2010 Statement as discussed by the authors has been used worldwide to improve the reporting of randomised controlled trials and has been updated by Schulz et al. in 2010, based on new methodological evidence and accumulating experience.
Journal ArticleDOI

Primary Prevention of Acute Coronary Events With Lovastatin in Men and Women With Average Cholesterol Levels: Results of AFCAPS/TexCAPS

TL;DR: Lovastatin reduces the risk for the first acute major coronary event in men and women with average TC and LDL-C levels and below-average HDL- C levels and supports the inclusion of HDL-C in risk-factor assessment and the need for reassessment of the National Cholesterol Program guidelines.
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