Interpreting the results of clinical trials, embracing uncertainty: A Bayesian approach

Abstract

Clinicians must interpret existing knowledge and new evidence as it arises, from well designed, conducted and reported clinical trials to guarantee the best quality of patient care. Clinical evidence is typically collected in an incremental and iterative process where new information is added to existing knowledge. However, the reporting of results from many trials often leads to uncertainty among clinicians on how to interpret a trial's outcomes with the translation of research into practice at times also challenged by prior established practices and beliefs. Traditionally, clinical trials are reported using P values and confidence intervals (CI) relevant to the study hypothesis (most commonly the null hypothesis of zero difference) and effect estimate (often the odds or risk ratio). This approach to inference of trial results is referred to as frequentist and uses data from a single trial in isolation and assigns the probability that the observed outcome has arisen by chance from a hypothetical number of repetitions of the trial (but not that the findings are erroneous or that the hypothesis is false). By convention, a threshold probability of P < .05 given the power of the trial is used as a compromise between type-1 (false positive) and type-2 (false negative) errors to reject the assumption of a chance finding. This approach furthermore leads to a dichotomisation of results that are reported as 'significant' or 'non-significant' purely based on frequentist statistical inference with the 'non-significant' result often receiving a connotation of a 'negative' trial or showing an 'absence' of effect. 1,2 It comes as no surprise that the P value has been criticised with calls made for an alternative approach to inference. [3][4][5][6][7] Using a Bayesian approach of inference, new trial results are considered in the context of existing information (please refer to the Appendix S1 for an explanation of Bayes theorem in an

Postoperative analgesia for upper gastrointestinal surgery: a retrospective cohort analysis

“…It is a different approach to the more commonly seen “Frequentist” or null hypothesis significance testing (NHST) framework that use a number of tools for interpretation such as p values, confidence intervals and the concepts of type I/II errors (Greenland et al 2016 ). In light of the recent concerns and controversies regarding mis-interpretations from null hypothesis significance testing approaches (Amrhein et al 2019 ; McShane et al 2009 ), and with advances in computation, Bayesian analysis has increasingly gained popularity (Bittl and He 2017 ; Frost et al 2021 ; Goligher et al 2018 ; Sidebotham et al 2021 ; Zampieri et al 2020 ). In short, with the model structure we developed from our understanding of the data generating process (the likelihood), such as that discussed above for morphine use across the postoperative time-period, a Bayesian approach can further incorporate what was already known (the prior distribution), combining with current evidence as support (the data we collect), to arrive in posterior probability distributions that we can then use to interpret the results.…”

Blockchain and COVID-19 Pandemic: Applications and Challenges

“…The private blockchain platform assures that the privacy of participants' data is preserved by hiding their identities. Thus, suitable use cases for private blockchain platforms in clinical trial management include consent management [67], health data sharing and monitoring in multi-site clinical trials [68], clinical trial results sharing [69], and rewards and incentives for effective coordination, management, and monitoring of clinical trial activities by the authorities. However, the participant's recruitment for clinical trial management services can be implemented through public blockchain platforms such as Ethereum.…”

Blockchain and COVID-19 pandemic: applications and challenges

“…The private blockchain platform assures that the privacy of participants’ data is preserved by hiding their identities. Thus, suitable use cases for private blockchain platforms in clinical trial management include consent management [ 67 ], health data sharing and monitoring in multi-site clinical trials [ 68 ], clinical trial results sharing [ 69 ], and rewards and incentives for effective coordination, management, and monitoring of clinical trial activities by the authorities. However, the participant’s recruitment for clinical trial management services can be implemented through public blockchain platforms such as Ethereum.…”