Pseudo-likelihood & split-sample methods in small & large trials
Design and Analysis for Dependent Data Theme
Pseudo-likelihood and split-sample methods in small and very large trials
Professor Geert Molenberghs, Hasselt University and KU Leuven, Belgium
Abstract: Hierarchical and otherwise clustered data can easily be analyzed using maximum likelihood or Bayesian methods in moderate to large samples. This may well be different in very small populations (e.g., clinical trials in rare diseases) or, on the other side of the spectrum, when samples become very large. We explore a few techniques that can be used in such settings. One method is pseudo-likelihood (or composite likelihood), where a cumbersome likelihood is replaced by a simpler function, which is easier to maximize and still produces consistent and asymptotically normal estimates.
On the other hand, the sample can be split into sub-samples, each of which is analyzed separately (ideally in parallel), after which the results are funneled into a single set of inferences using appropriate combination rules. These techniques can also be combined. Using a set of examples, we illustrate how the methods work, and what the computational gains are.
Followed by a drinks reception in the Manson Foyer