Bayesian vs. Frequentist Statements About Treatment Efficacy

The following examples are intended to show the advantages of Bayesian reporting of treatment efficacy analysis, as well as to provide examples contrasting with frequentist reporting. As detailed here, there are many problems with p-values, and some of those problems will be apparent in the examples below. Many of the advantages of Bayes are summarized here. As seen below, Bayesian posterior probabilities prevent one from concluding equivalence of two treatments on an outcome when the data do not support that (i.

EHRs and RCTs: Outcome Prediction vs. Optimal Treatment Selection

Frank Harrell Professor of Biostatistics Vanderbilt University School of Medicine Laura Lazzeroni Professor of Psychiatry and, by courtesy, of Medicine (Cardiovascular Medicine) and of Biomedical Data Science Stanford University School of Medicine Revised July 17, 2017 It is often said that randomized clinical trials (RCTs) are the gold standard for learning about therapeutic effectiveness. This is because the treatment is assigned at random so no variables, measured or unmeasured, will be truly related to treatment assignment.

Statistical Errors in the Medical Literature

Misinterpretation of P-values and Main Study Results Dichotomania Problems With Change Scores Improper Subgrouping Serial Data and Response Trajectories As Doug Altman famously wrote in his Scandal of Poor Medical Research in BMJ in 1994, the quality of how statistical principles and analysis methods are applied in medical research is quite poor. According to Doug and to many others such as Richard Smith, the problems have only gotten worse.

My Journey From Frequentist to Bayesian Statistics

The difference between Bayesian and frequentist inference in a nutshell: With Bayes you start with a prior distribution for θ and given your data make an inference about the θ-driven process generating your data (whatever that process may be), to quantify evidence for every possible value of θ. With frequentism, you make assumptions about the process that generated your data, and try to build evidence for what θ is not.

Randomized Clinical Trials Do Not Mimic Clinical Practice, Thank Goodness

Randomized clinical trials (RCT) have long been held as the gold standard for generating evidence about the effectiveness of medical and surgical treatments, and for good reason. But I commonly hear clinicians lament that the results of RCTs are not generalizable to medical practice, primarily for two reasons: Patients in clinical practice are different from those enrolled in RCTs Drug adherence in clinical practice is likely to be lower than that achieved in RCTs, resulting in lower efficacy.