Statistical Considerations in the Analysis of Matched Case-Control Studies. With Applications in Nutritional Epidemiology

Abstract: The case-control study is one of the most frequently used study designs in analytical epidemiology. This thesis focuses on some methodological aspects in the analysis of the results from this kind of study.A population based case-control study was conducted in northern Norway and central Sweden in order to study the associations of several potential risk factors with thyroid cancer. Cases and controls were individually matched and the information on the factors under study was provided by means of a self-completed questionnaire. The analysis was conducted with logistic regression. No association was found with pregnancies, oral contraceptives and hormone replacement after menopause. Early pregnancy and artificial menopause were associated with an increased risk, and cigarette smoking with a decreased risk, of thyroid cancer (paper I). The relation with diet was also examined. High consumption with fat- and starch-rich diet was associated with an increased risk (paper II).Conditional and unconditional maximum likelihood estimations of the parameters in a logistic regression were compared through a simulation study. Conditional estimation had higher root mean square error but better model fit than unconditional, especially for 1:1 matching, with relatively little effect of the proportion of missing values (paper III). Two common approaches to handle partial non-response in a questionnaire when calculating nutrient intake from diet variables were compared. In many situations it is reasonable to interpret the omitted self-reports of food consumption as indication of "zero-consumption" (paper IV).The reproducibility of dietary reports was presented and problems for its measurements and analysis discussed. The most advisable approach to measure repeatability is to look at different correlation methods. Among factors affecting reproducibility frequency and homogeneity of consumption are presumably the most important ones (paper V). Nutrient variables can often have a mixed distribution form and therefore transformation to normality will be troublesome. When analysing nutrients we therefore recommend comparing the result from a parametric test with an analogous distribution-free test. Different methods to transform nutrient variables to achieve normality were discussed (paper VI).