Clinician treatment preferences affect the ability to perform randomized clinical trials and the ability to analyze observational data for treatment effects. In clinical trials, clinician preferences that are based on a subjective analysis of the patient can make it difficult to define eligibility criteria for which clinicians would agree to randomize all patients who satisfy the criteria. In addition, since each clinician typically has some preference for the choice of treatment for a given patient, there are concerns about how strong that preference needs to be before it is inappropriate for him to randomize the choice of treatment. In observational studies, the fact that clinician preferences affect the choice of treatment is a major source of selection bias when estimating treatment effects. In this paper we review alternative designs that have been proposed in the literature for randomized clinical trials that utilize clinician preferences differently than the standard randomized trial design. We also examine the effects of clinician preferences on the ability to estimate causal treatment differences from observational data, and propose an alternative method of analysis for observational data that uses clinician preferences explicitly. We report on our experience to date in using our alternative randomized clinical trial design and our new method of observational analysis to compare two treatments at the orthodontic clinics at the University of California San Francisco and the University of the Pacific, San Francisco.

One of the most often quoted results from the original work of Rubin and Little on the classification of missing value processes is the validity of likelihood based inferences under missing at random (MAR) mechanisms. Although the sense in which this result holds was precisely defined by Rubin, and explored by him in later work, it appears to be now used by some authors in a general and rather imprecise way, particularly with respect to the use of frequentist modes of inference. In this paper an exposition is given of likelihood based frequentist inference under an MAR mechanism that shows in particular which aspects of such inference cannot be separated from consideration of the missing value mechanism. The development is illustrated with three simple setups: a bivariate binary outcome, a bivariate Gaussian outcome and a two-stage sequential procedure with Gaussian outcome and with real longitudinal examples, involving both categorical and continuous outcomes. In particular, it is shown that the classical expected information matrix is biased and the use of the observed information matrix is recommended.

Arguments are presented to support increased emphasis on logical aspects of formal methods of analysis, depending on probability in the sense of R. A. Fisher. Formulating probabilistic models that convey uncertain knowledge of objective phenomena and using such models for inductive reasoning are central activities of individuals that introduce limited but necessary subjectivity into science. Statistical models are classified into overlapping types called here empirical, stochastic and predictive, all drawing on a common mathematical theory of probability, and all facilitating statements with logical and epistemic content. Contexts in which these ideas are intended to apply are discussed via three major examples.

In 1993 the International Organization for Standardization (ISO), in cooperation with several other international organizations, issued Guide to the Expression of Uncertainty in Measurement in order to establish, and standardize for international use, a set of general rules for evaluation and expressing uncertainty in measurement. The ISO recommendation has been of concern to many statisticians because it appears to combine frequentist performance measures and indices of subjective distributions in a way that neither frequentists nor Bayesians can fully endorse. The purpose of this review of the ISO Guide is to describe the essential recommendations made in the Guide, then to show how these recommendations can be regarded as approximate solutions to certain frequentist and Bayesian inference problems. The framework thus provided will, hopefully, allow statisticians to develop improvements to the ISO recommendations (particularly in the approximations used), and also better communicate with the physical science researchers who will be following the ISO guidelines.

Shanti Gupta was born and raised in Saunasi, Mainpuri, India. He attended Delhi University and received B.A. Honours and M.S. degrees in mathematics. He then took a one-year diploma course in applied statistics at the Indian Council of Agricultural Research, New Delhi, and his distinguished career in statistics was launched. He came to the United States in 1953 and received his Ph.D. in mathematical statistics in 1956 from the University of North Carolina at Chapel Hill working under the guidance of Professor Raj Chandra Bose. His thesis, "On a decision rule for a problem in ranking means," began his prolific formulation of and investigation into a class of problems referred to as "subset approach to ranking and selection."

Shanti has held research and teaching positions at Delhi College, Bell Telephone Laboratories, University of Alberta, Courant Institute of Mathematical Science at New York University, Stanford University, and, most notably, Purdue University, where he has spent most of his professional career. At Purdue, he became Head of the newly created Department of Statistics in 1968 and served in that capacity until 1995 when he stepped down to devote more fully his time and energy to teaching and research. Under his leadership, Purdue's Department of Statistics grew into one of the premier departments in the country. Twenty-eight Ph.D. dissertations were directed by Shanti over this period of time.

Shanti has provided exemplary service to the profession. A partial list of his activities includes the following: Founding Editor of the IMS Lecture Notes--Monograph Series in Statistics and Probability (1979-1988); Chairman of the Joint Management Committee of the ASA and IMS for Current Index to Statistics (1981-1988); Member of the NRC Advisory Committee on U.S. Army Basic Scientific Research (1983-1988). He served as President of the IMS during 1989-1990. He has also been very active with many of the statistical journals, serving in both editorial and board-member capacities.

Shanti has received many honors in recognition of his valuable contributions. He is a Fellow of the ASA, AAAS and IMS and is an elected member of the International Statistical Institute. He has held special short-term visiting positions such as Special Chair, Institute of Mathematics, Academia Sinica, Taipei, Taiwan; and Erskine Fellow, University of Canterbury, Christchurch, New Zealand. He was one of the special invitees at the 1990 Taipei International Symposium in Statistics and was presented with the key to the city by the Mayor. A recent book, Advances in Statistical Decision Theory and Applications, published in honor of Shanti, contains many papers in the areas where his influence as a teacher and a researcher have been felt: Bayesian inference; decision theory; point and interval estimation; tests of hypotheses; ranking and selection; distributions theory; and industrial applications.

The following conversation took place at the Department of Statistics, Purdue University, on 22 September 1997.