Overview Maximum likelihood estimation (MLE) is a gold standard estimation procedure in non-Bayesian statistics, and the likelihood function is central to Bayesian statistics (even though it is not maximized in the Bayesian paradigm). MLE may be unpenalized (the standard approach) or various penalty functions such as L1 (lasso, absolute value penalty), and L2 (ridge regression; quadratic) penalties may be added to the log-likelihood to achieve shrinkage (aka regularization). I have been doing...| Statistical Thinking
Event: Consilium Scientific Slides Video| Statistical Thinking
Event: International Chinese Statistical Association Applied Statistics Symposium, Nashville, Tennessee USA Slides| Statistical Thinking
Background As explained here, the power for a group comparison can be greatly increased over that provided by a binary endpoint, with greater increase when an ordinal endpoint has several well-populated categories or has a great many categories, in which it becomes a standard continuous variable. When a randomized clinical trial (RCT) is undertaken and deaths can occur, there are disadvantages to excluding the death and analyzing responses only on survivors using death as a competing risk, wh...| Statistical Thinking
Background A binary endpoint in a clinical trial is a minimum-information endpoint that yields the lowest power for treatment comparisons. A time-to-event outcome, when only a minority of subjects suffer the event, has little power gain over a pure binary endpoint, since its power comes from the number of events (number of uncensored observations). The highest power endpoint would be from a continuous variable that is measured precisely and reflects the clinical outcome situation. An ordinal ...| Statistical Thinking
Slides Elaborations Video| Statistical Thinking