datamethods.org is a discussion site where data methodologists meet each other and subject matter experts including clinical trialists and clinical researchers. Its development is documented here. Datamethods is provided by the Department of Biostatistics, Vanderbilt University School of Medicine.
I have written some short articles on the site, listed below.
Responder analysis: Loser x 4 Problems with NNT Should we ignore covariate imbalance and stop presenting a stratified ‘table one’ for randomized trials?
This article discusses issues related to alpha spending, effect sizes used in power calculations, multiple endpoints in RCTs, and endpoint labeling. Changes in endpoint priority is addressed. Included in the the discussion is how Bayesian probabilities more naturally allow one to answer multiple questions without all-too-arbitrary designations of endpoints as “primary” and “secondary”. And we should not quit trying to learn.
What are the major elements of learning from data that should inform the research process? How can we prevent having false confidence from statistical analysis? Does a Bayesian approach result in more honest answers to research questions? Is learning inherently subjective anyway, so we need to stop criticizing Bayesians’ subjectivity? How important and possible is pre-specification? When should replication be required? These and other questions are discussed.
(In a Bayesian analysis) It is entirely appropriate to collect data until a point has been proven or disproven, or until the data collector runs out of time, money, or patience.
— Edwards, Lindman, Savage (1963) Introduction Bayesian inference, which follows the likelihood principle, is not affected by the experimental design or intentions of the investigator. P-values can only be computed if both of these are known, and as been described by Berry (1987) and others, it is almost never the case that the computation of the p-value at the end of a study takes into account all the changes in design that were necessitated when pure experimental designs encounter the real world.
To avoid “false positives” do away with “positive”.
A good poker player plays the odds by thinking to herself “The probability I can win with this hand is 0.91” and not “I’m going to win this game” when deciding the next move.
State conclusions honestly, completely deferring judgments and actions to the ultimate decision makers. Just as it is better to make predictions than classifications in prognosis and diagnosis, use the word “probably” liberally, and avoid thinking “the evidence against the null hypothesis is strong, so we conclude the treatment works” which creates the opportunity of a false positive.
Misinterpretation of P-values and Main Study Results Dichotomania Problems With Change Scores Improper Subgrouping Serial Data and Response Trajectories Cluster Analysis 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.
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 happened to be), to quantify evidence for every possible value of θ. With frequentism, you make assumptions about the process that generated your data and infinitely many replications of them, and try to build evidence for what θ is not.
With the many problems that p-values have, and the temptation to “bless” research when the p-value falls below an arbitrary threshold such as 0.05 or 0.005, researchers using p-values should at least be fully aware of what they are getting. They need to know exactly what a p-value means and what are the assumptions required for it to have that meaning. ♦ A p-value is the probability of getting, in another study, a test statistic that is more extreme than the one obtained in your study if a series of assumptions hold.
Optimum decision making in the presence of uncertainty comes from probabilistic thinking. The relevant probabilities are of a predictive nature: P(the unknown given the known). Thresholds are not helpful and are completely dependent on the utility/cost/loss function.
Corollary: Since p-values are P(someone else’s data are more extreme than mine if H0 is true) and we don’t know whether H0 is true, it is a non-predictive probability that is not useful for decision making.