Bayesian Analysis in Critical Care Medicine

A meta-analysis on the effectiveness of serotype O foot-and-mouth disease vaccines

Foot-and-mouth disease (FMD) is a highly contagious viral disease of domesticated animals that causes major economic losses globally. In this meta-analysis, 29 studies were evaluated using a random-effects model to analyze the efficacy of FMD vaccines. The quantifying heterogeneity between the groups was low (tau2 = 0.000, tau = 0.000, and I2 = 0.0% [0.0%; 24.6%]). The meta-analysis revealed that the inactivated vaccine provides the best protection among different vaccine types, with the following ranking from highest to lowest efficacy: inactivated vaccine > mRNA vaccine > E. coli vaccine > plant vaccine > recombinant virus vaccine > phage vaccine > synthesize vaccine > DNA vaccine > negative control. The findings revealed that the inactivated vaccine provides the best protection among the different types of vaccines. Based on these findings, we recommend using inactivated vaccines as controls in the development of novel vaccines, as they achieved the highest efficacy among all evaluated vaccine types.

Bayesian Reanalysis of Statistically Nonsignificant Outcomes in Plastic Surgery Clinical Trials

Background

Statistically nonsignificant randomized clinical trial (RCT) results are challenging to interpret, as they are unable to prove the absence of a difference between treatment groups. Bayesian analysis offers an alternative statistical framework capable of providing a comprehensive understanding of nonsignificant results.

Methods

This cross-sectional study conducted a post hoc Bayesian analysis of statistically nonsignificant outcomes from RCTs published in Plastic and Reconstructive Surgery from 2013 to 2022. Bayes factors representing the probability of the absence of a difference, or the null hypothesis of no difference, were calculated and examined. P values and Bayes factors of these outcomes were also compared with assessment of their association.

Results

In 73 studies with 176 statistically nonsignificant outcomes, 160 (91%) indicated evidence for the absence of a difference (Bayes factor > 1). For 110 (63%) of these, the Bayes factor was between 1 and 3, indicating weak evidence for the absence of a difference; 16 (9.1%) results supported the presence of a difference (Bayes factor < 1). A greater P value was independently associated with a larger Bayes factor (β = 2.6, P <0.001).

Conclusions

Nearly two-thirds of nonsignificant RCT outcomes provided only weak evidence supporting the absence of a difference. This uncertainty poses challenges for clinical decision-making and highlights the inefficiency in resource utilization. Integrating Bayesian statistics into future trial design and analysis could overcome these challenges, enhancing result interpretability and guiding medical practice and research.

Estimation of a Structural Equation Modeling of Quality of Life Mediated by Difficulty in Daily Life in Survivors of Breast Cancer

BACKGROUND

The purpose of this study was to clarify the structural relationship of quality of life (QOL) in survivors of breast cancer, including difficulty in daily life and negative experiences in daily activities, as health-related indicators.

METHODS

Participants were survivors of breast cancer for more than 2 years after primary breast cancer surgery and belonged to self-help groups. The assessment used FACT-B (QOL), HADS (anxiety and depression), SOC (sense of coherence), WHODAS 2.0 (difficulties in daily life), and CAOD (negative experiences in daily activities). Bayesian structural equation modeling (BSEM) was performed to analyze the hypothesized model. If the causal model was significant, multiplication of the path coefficient from emotional distress (anxiety and depression) to QOL, and from SOC to emotional distress, was considered a direct effect on QOL, and from SOC to difficulty in daily life, from difficulty in daily life to negative experiences in daily activities, and from negative experiences in daily activities to anxiety and depression were considered indirect effects on QOL.

RESULTS

The participants comprised 73 survivors of breast cancer. The goodness of fit of the model in the BSEM was satisfactory. The direct effect was 0.274, and the indirect effect was 0.164.

CONCLUSIONS

An additional finding of this study is that coping with difficulty in daily life and negative experiences in daily activities related to QOL may improve QOL.

Why are There not More Bayesian Clinical Trials? Ability to Interpret Bayesian and Conventional Statistics Among Medical Researchers

OBJECTIVE AND BACKGROUND

We assessed current understandings in interpretation of Bayesian and traditional statistical results within the clinical researcher (non-statistician) community.

METHODS

Within a 22-question survey, including demographics and experience and comfort levels with Bayesian analyses, we included questions on how to interpret both Bayesian and traditional statistical outputs. We also assessed whether Bayesian or traditional interpretations are considered more useful.

RESULTS

Among the 323 respondent clinicians, 42.4% and 36.5% chose the correct interpretations of the posterior probability and 95% credible interval, respectively. Only 11.5% of respondents interpreted the p-value correctly and 23.5% interpreted the 95% confidence interval correctly.

CONCLUSIONS

Based on these survey results, we conclude that most of these clinicians face uncertainty when attempting to interpret results from both Bayesian and traditional statistical outputs. When presented with accurate interpretations, clinicians generally conclude that Bayesian results are more useful than conventional ones. We believe there is a need for education of clinicians in statistical interpretation in ways that are customized to this audience.

Trial Design in Critical Care Nutrition: The Past, Present and Future

The specialty of nutrition in critical care is relatively modern, and accordingly, trial design has progressed over recent decades. In the past, small observational and physiological studies evolved to become small single-centre comparative trials, but these had significant limitations by today’s standards. Power calculations were often not undertaken, outcomes were not specified a priori, and blinding and randomisation were not always rigorous. These trials have been superseded by larger, more carefully designed and conducted multi-centre trials. Progress in trial conduct has been facilitated by a greater understanding of statistical concepts and methodological design. In addition, larger numbers of potential study participants and increased access to funding support trials able to detect smaller differences in outcomes. This narrative review outlines why critical care nutrition research is unique and includes a historical critique of trial design to provide readers with an understanding of how and why things have changed. This review focuses on study methodology, population group, intervention, and outcomes, with a discussion as to how these factors have evolved, and concludes with an insight into what we believe trial design may look like in the future. This will provide perspective on the translation of the critical care nutrition literature into clinical practice.