Purposes, methods, philosophies

Peter Armitage has enjoyed a long and distinguished career in biostatistics, and has also given considerable thought to the way in which statistics is taught to non-mathematicians, perhaps because, he says jokingly, he always found maths a bit difficult himself. Now in retirement, he is still communicating statistical ideas in his rôle as Editor of the Encyclopedia of Biostatistics .     

Randomization,balance, and the validity and efficiency of design-adaptive allocation methods

Few topics have stirred as much discussion and controversy as randomization. A reading of the literature suggests that clinical trialists generally feel randomization is necessary for valid inference, while biostatisticians using model-based inference often appear to prefer nearly optimal designs irrespective of any induced randomness. Dissection of the methods of treatment assignment shows that there are five basic approaches; pure randomizers, true randomizers, quasi-randomizers, permutation testers, and conventional modelers. Four of these have coherent design and analysis strategies, even though they are not mutually consistent, but the fifth and most prevalent approach (quasi-randomization) has little to recommend it. Design-adaptive allocation is defined, it is shown to provide valid inference, and a simulation indicates its efficiency advantage. In small studies, or large studies with many important prognostic covariates or analytic subgroups, design-adaptive allocation is an extremely attractive method of treatment assignment.     

Using simulation methods for bayesian econometric models: inference, development,and communication

This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the combination of models and the development of predictive distributions. Moving beyond conditioning on a fixed number of completely specified models, the paper introduces subjective Bayesian tools for formal comparison of these models with as yet incompletely specified models. The paper then shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators. A theme of the paper is the practicality of subjective Bayesian methods. To this end, the paper describes publicly available software for Bayesian inference, model development, and communication and provides illustrations using two simple econometric models.     

Semicompeting risks in aging research: methods,issues and needs

A semicompeting risks problem involves two-types of events: a nonterminal and a terminal event (death). Typically, the nonterminal event is the focus of the study, but the terminal event can preclude the occurrence of the nonterminal event. Semicompeting risks are ubiquitous in studies of aging. Examples of semicompeting risk dyads include: dementia and death, frailty syndrome and death, disability and death, and nursing home placement and death. Semicompeting risk models can be divided into two broad classes: models based only on observables quantities (class \(\mathcal {O}\) ) and those based on potential (latent) failure times (class \(\mathcal {L}\) ). The classical illness-death model belongs to class \(\mathcal {O}\) . This model is a special case of the multistate models, which has been an active area of methodology development. During the past decade and a half, there has also been a flurry of methodological activity on semicompeting risks based on latent failure times ( \(\mathcal {L}\) models). These advances notwithstanding, the semicompeting risks methodology has not penetrated biomedical research, in general, and gerontological research, in particular. Some possible reasons for this lack of uptake are: the methods are relatively new and sophisticated, conceptual problems associated with potential failure time models are difficult to overcome, paucity of expository articles aimed at educating practitioners, and non-availability of readily usable software. The main goals of this review article are: (i) to describe the major types of semicompeting risks problems arising in aging research, (ii) to provide a brief survey of the semicompeting risks methods, (iii) to suggest appropriate methods for addressing the problems in aging research, (iv) to highlight areas where more work is needed, and (v) to suggest ways to facilitate the uptake of the semicompeting risks methodology by the broader biomedical research community.