Category: My First Bayes

Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist

Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons:

Possible Solution to Publication Bias Through Bayesian Statistics

The present paper argues that an important cause of publication bias resides in traditional frequentist statistics forcing binary decisions. An alternative approach through Bayesian statistics provides various degrees of support for any hypothesis allowing balanced decisions and proper null hypothesis testing, which may prevent publication bias.

Effectiveness, Mediators, and Effect Predictors of Internet Interventions for Chronic Cancer-Related Fatigue

This paper describes the design and analysis plan that will be used to study 2 Internet interventions aimed at reducing severe fatigue in cancer survivors: a mobile ambulant activity feedback therapy supported through a weekly email by a physiotherapist and a weekly Web- and mindfulness-based cognitive therapy supported online by a psychologist.

Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors

The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions.

Social Influence Interpretation of Interpersonal Processes and Team Performance Over Time Using Bayesian Model Selection

The team behavior literature is ambiguous about the relations between members’ interpersonal processes—task debate and task conflict—and team performance. From a social influence perspective, we show why members’ interpersonal processes determine team performance over time in small groups.

Reducing bias due to systematic attrition in longitudinal studies: The benefits of multiple imputation

Most longitudinal studies are plagued by drop-out related to variables at earlier assessments (systematic attrition). Although systematic attrition is often analysed in longitudinal studies, surprisingly few researchers attempt to reduce biases due to systematic attrition, even though this is possible and nowadays technically easy.