### Raymond J. Carroll

##### Department of Statistics

Texas A&M University

Raymond J. Carroll is Distinguished Professor of Statistics, Nutrition and Toxicology, and former Head of the Statistics Department, at Texas A&M University. He received his PhD degree in Statistics in 1974 from Purdue University, and was on the faculty of the Statistics Department at the University of North Carolina from 1974 to 1987. Among his many honors, Professor Carroll was elected Fellow of the American Statistical Association in 1982, Fellow of the Institute of Mathematical Statistics in 1984, and Ordinary Member of the International Statistical Institute in 1991. Professor Carroll served as editor of Biometrics and the Journal of Statistical Planning and Inference, and in several other editorial capacities. He has had extensive NIH funding, including the prestigious MERIT award for basic research. He is a consultant in high demand from corporations and government agencies. Professor Carroll is co-author of four well-known authoritative texts on modern regression and modeling. He is author or co-author of neary 300 articles in statistical and other journals, and has made almost an equal number of invited lectures.

#### Abstracts

##### General Lecture

(November 13, 2006)

**Measuring Dietary Intake**

Newspaper articles routinely report the results of epidemiological studies of the relationship between what we eat and disease outcomes such as heart disease and various forms of cancer. One of the most-quoted studies is the Nurses Health Study, which follows the health outcomes 100,000 nurses and asks them questions about their dietary intakes. While there are exceptions, for the most part one can find a relationship between

heart disease and diet, e.g., less fat, more fruits, etc. On the other hand, it is rare that prospective epidemiological studies of human populations find links between cancer and dietary intakes. Perhaps the most controversial of all is the question of the relationship between dietary fat intake and breast cancer. Countries with higher fat intakes tend to have higher rates of breast cancer, and yet no epidemiological study has shown such a

link. The puzzle of course is to understand the discrepancy.

Obviously, the etiology of disease may explain why heart disease, with its intermediate endpoints such as serum cholesterol, has confirmed links to nutrition while the evidence is mixed with cancer. I will focus instead on a basic question of study design: how do we measure what we eat? Try this out: how many days per year do you eat apples? I am going to review the accumulating evidence that suggests that with complex, subtle disease such as cancer, with no good intermediate endpoints such as serum cholesterol for heart disease, finding links between disease and nutrient intakes will be the exception rather than the rule, simply because of the way diet is measured. I will close with remarks about the Women’s Health Initiative Dietary Intervention Trial and two new cohort studies, along with my own views of the subject.

##### Technical Lecture

(November 14, 2006)

**General Semiparametric Analysis of Repeated Measures Data**

This talk considers the general problem where the data for an individual are repeated measures in the most general sense, with a parametric component and a nonparametric component. It is easy, although not well-known, to handle the problem in the case that the nonparametric component of the likelihood function is evaluated exactly once, e.g., when a baseline variable is modeled nonparametrically. Far more difficult, and non-intuitive, is the case where the nonparametric component is evaluated more than once in the likelihood function. Examples include repeated

measures studies, variance component models when the random effect is related to the predictors, matched case-control studies with a nonparametric component, fixed-effects models in econometrics, etc. I will present a constructive (i.e., computable), semiparametric efficient method

for this general problem. The constructive part is important: like most semiparametric efficient methods, there is an integral equation lurking in the background, but unlike most such methods, in our approach the integral equation can be avoided. An example involving caloric intake and income

in China is used to illustrate the methodology, as a means of contrasting a random effects analysis and a fixed effects analysis.