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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.
Analytic studies are increasingly important for understanding relationships between diseases and their causes. They usually take the form of observational investigations, but can also include randomized clinical trials. Risk factors are antecedents that are considered to be components of the disease pathway. Appropriate study design and consideration to systematic bias and confounding helps to establish association between the exposure and disease. The purpose for maintaining the principles of causal inference and eliminating chance, bias, and confounding is to establish validity. The most serious concern in analytic studies is maintaining validity. Confounders are extraneous factors that are related to the disease and to a risk factor or exposure related to the disease. The confounder usually predicts disease in the absence of any risk factor. Investigators have to consider cost and efficiency in their design as well as the potential public health impact of any observed association.
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