In Week 3 of this course, you looked at cohort study by Maurice Zeegers et al, "Alcohol Consumption and Bladder Cancer Risk: Results from the Netherlands Cohort Study" American Journal of Epidemiology, Vol 153, No. 1, pp 38-41. We discussed potential effect modifiers vs. confounders at that time. Let's look at this study again to in terms of its design as a cohort study: The study design for the original cohort and selection of the case-cohort is detailed in van den Brandt, P.A. et al. "A large scale prospective cohort study on diet and cancer in the Netherlands." J Clinical Epidemiology (1990), Vol 43, No. 3, 285-295.
What evidence is there of the prospective nature of this cohort study?Subjects complete a questionnaire on baseline risk factors; 61% also provided toenail clippings (exposure data); follow-up for incident cancer ensues with record linkage to cancer and pathology registries.
Describe the type of cohort study...
The original cohort came from the general population of 55-69 year old men and women in the Netherlands, sampled from municipal population registries. Individuals with special dietary habits (e.g. vegetarians) were over-sampled. 120, 852 subjects are in the original cohort. These subjects completed the baseline questionnaire that was sent to 340, 439 subjects. A sub-cohort of 5000 was randomly selected immediately after identification of cohort members. A case-cohort approach was used. There was further random selection of 3500 members from the 5000 for processing questionnaires and toenail specimens; further selection for collecting and processing dietary questionnaires. See Figure 1 in Brandt et al.
Why didn't the researchers use a nested case-control study?
A nested case-control design would require waiting for cases to occur before efficiently matching controls to cases. This would cause a delay in processing questionnaires for cases and controls. The case-cohort approach allows data to be processed while cases are still being ascertained. In the case-cohort design, the person-year experience of the whole is estimated by the results of the sub-cohort, while cases are counted among the entire cohort.
A beauty of a well-run cohort study is the multiple outcomes that can be considered. A group well characterized and followed over a long period of time provides much useful information. For example, the Framingham study has studied 3 generations and added to our understanding of the roles of obesity, HDL lipids and hypertension in heart disease and stroke as well as contributing an algorithm for predicting CHD risk and identifying 8 genetic loci associated with hypertension. Use of sub-cohorts for specific purposes can minimize cost and the length of a study.