Simulation data for the analysis of Bayesian posterior significance and effect size indices for the two-sample t-test to support reproducible medical research

Identifying the Influencing Factors for the BMI by Bayesian and Frequentist Multiple Linear Regression Models: A Comparative Study

Background

In this article, we attempt to demonstrate the superiority of the Bayesian approach over the frequentist approaches of the multiple linear regression model in identifying the influencing factors for the response variable.

Methods and Material

A survey was conducted among the 310 respondents from the Kathirkamam area in Puducherry. We have considered the response variable, body mass index (BMI), and the predictors such as age, weight, gender, nature of the job, and marital status of individuals were collected with the personal interview method. Jeffreys's Amazing Statistics Program (JASP) software was used to analyze the dataset. In the conventional multiple linear regression model, the single value of regression coefficients is determined, while in the Bayesian linear regression model, the regression coefficient of each predictor follows a specific posterior distribution. Furthermore, it would be most useful to identify the best models from the list of possible models with posterior probability values. An inclusion probability for all the predictors will give a superior idea of whether the predictors are included in the model with probability.

Results and Conclusions

The Bayesian framework offers a wide range of results for the regression coefficients instead of the single value of regression coefficients in the frequentist test. Such advantages of the Bayesian approach will catapult the quality of investigation outputs by giving more reliability to solutions of scientific problems.

The evidence interval and the Bayesian evidence value: On a unified theory for Bayesian hypothesis testing and interval estimation

Interval estimation is one of the most frequently used methods in statistical science, employed to provide a range of credible values a parameter is located in after taking into account the uncertainty in the data. However, while this interpretation only holds for Bayesian interval estimates, these suffer from two problems. First, Bayesian interval estimates can include values which have not been corroborated by observing the data. Second, Bayesian interval estimates and hypothesis tests can yield contradictory conclusions. In this paper a new theory for Bayesian hypothesis testing and interval estimation is presented. A new interval estimate is proposed, the Bayesian evidence interval, which is inspired by the Pereira-Stern theory of the full Bayesian significance test (FBST). It is shown that the evidence interval is a generalization of existing Bayesian interval estimates, that it solves the problems of standard Bayesian interval estimates and that it unifies Bayesian hypothesis testing and parameter estimation. The Bayesian evidence value is introduced, which quantifies the evidence for the (interval) null and alternative hypothesis. Based on the evidence interval and the evidence value, the (full) Bayesian evidence test (FBET) is proposed as a new, model-independent Bayesian hypothesis test. Additionally, a decision rule for hypothesis testing is derived which shows the relationship to a widely used decision rule based on the region of practical equivalence and Bayesian highest posterior density intervals and to the e-value in the FBST. In summary, the proposed method is universally applicable, computationally efficient, and while the evidence interval can be seen as an extension of existing Bayesian interval estimates, the FBET is a generalization of the FBST and contains it as a special case. Together, the theory developed provides a unification of Bayesian hypothesis testing and interval estimation and is made available in the R package fbst.

Carbapenem-resistant Klebsiella pneumoniae outbreak in a COVID-19 intensive care unit; a case-control study

We analysed a carbapenem-resistant Klebsiella pneumoniae (CRKP) outbreak in the coronavirus disease (COVID) ICU. We retrospectively collected data from ICU records. We identified 25 cases between 12 November 2020 and 19 December 2020, and compared them to 42 controls present in the ICU during the same period. The presence of a femoral haemodialysis catheter was strongly associated with invasive CRKP infections (cases, 9 [36%]; controls, 0 [0%]; odds ratio [OR] 95% confidence intervals [CIs], 21 (5; 89)). We found a significant association between old age and CRKP infection with adverse outcomes. Sequence analysis revealed three distinct carbapenemase genes: bla_NDM-1, bla_OXA-48 and bla_KPC-2. We launched rectal swab sampling upon admission to the ICU, cohorted colonized patients and cases and conducted an intensive training programme for newly employed staff. This study revealed that the emergence and dissemination of CRKP in COVID ICUs were associated with increased adverse outcomes. The presence of a femoral haemodialysis catheter was a significant risk factor for CRKP infections.