Showing papers by "Ross L. Prentice published in 2002"

Research strategies and the use of nutrient biomarkers in studies of diet and chronic disease.

Abstract: Objective To provide an account of the state of diet and chronic disease research designs and methods; to discuss the role and potential of aggregate and analytical observational studies and randomised controlled intervention trials; and to propose strategies for strengthening each type of study, with particular emphasis on the use of nutrient biomarkers in cohort study settings. Design Observations from diet and disease studies conducted over the past 25 years are used to identify the strengths and weaknesses of various study designs that have been used to associate nutrient consumption with chronic disease risk. It is argued that a varied research programme, employing multiple study designs, is needed in response to the widely different biases and constraints that attend aggregate and analytical epidemiological studies and controlled intervention trials. Study design modifications are considered that may be able to enhance the reliability of aggregate and analytical nutritional epidemiological studies. Specifically, the potential of nutrient biomarker measurements that provide an objective assessment of nutrient consumption to enhance analytical study reliability is emphasised. A statistical model for combining nutrient biomarker data with self-report nutrient consumption estimates is described, and related ongoing work on odds ratio parameter estimation is outlined briefly. Finally, a recently completed nutritional biomarker study among 102 postmenopausal women in Seattle is mentioned. The statistical model will be applied to biomarker data on energy expenditure, urinary nitrogen, selected blood fatty acid measurements and various blood micronutrient concentrations, and food frequency self-report data, to identify study subject characteristics, such as body mass, age or socio-economic status, that may be associated with the measurement properties of food frequency nutrient consumption estimates. This information will be crucial for the design of a potential larger nutrient biomarker study within the cohort study component of the Women's Health Initiative. Setting and subjects The methodology under study is expected to be pertinent to a wide variety of diet and chronic disease association studies in the general population. Ongoing work focuses on statistical methods developed using computer simulations motivated by studies of dietary fat in relation to breast and colon cancer among post-menopausal women, and ongoing pilot studies to be described in detail elsewhere, involving post-menopausal women living in the Seattle area. Results and conclusion A varied research programme appears to be needed to make progress in the challenging diet and chronic disease research area. Such progress may include aggregate studies of diet and chronic disease that include sample surveys in diverse population groups world-wide, analytical epidemiological studies that use nutrient biomarker data to calibrate self-report nutrient consumption estimates, and randomised controlled intervention trials that arise from an enhanced infrastructure for intervention development. New innovative designs, models and methodologies are needed for each such research setting.

Some further results on incorporating risk factor information in assessing the dependence between paired failure times arising from case-control family studies: an application to prostate cancer.

Abstract: In a typical case-control family study, detailed risk factor information is often collected on cases and controls, but not on their relatives for reasons of cost and logistical difficulty in locating the relatives The impact of missing risk factor information for relatives on estimation of the strength of dependence between the disease risk of pairs of relatives is largely unknown In this paper, we extend our earlier work on estimating the dependence of ages at onset between paired relatives from case-control family data to include covariates on cases and controls, and possibly relatives Using population-based case-control families as our basic data structure, we study the effect of missing covariates for relatives and/or cases and controls on the bias of certain dependence parameter estimators via a simulation study Finally we illustrate various analyses using a case-control family study of early onset prostate cancer Copyright © 2002 John Wiley & Sons, Ltd