Study Designs in Cancer Epidemiology

1. Study Designs in Cancer Epidemiology Zuo-Feng Zhang, MD, PhD Epi242, 2009

2. Study Design Prospective: • Cohort Studies: Observational studies • Intervention Studies, Clinical Trials • Nested Case-Control Studies Cross-sectional Studies Retrospective • Case-Control Studies

3. Prospective Cohort Study • Discussion of a study design for a prospective study in a near a nuclear plant

4. Study Population: 2 cohorts • General population cohort, the sampling process should be depended on the distance from the factory to their home, e.g., 1km, 2km, etc. • Occupational cohort. This is the most important cohort for radiation exposure. All individuals in the factory should be included in the occupational cohort.

5. Age Group • These cohorts should include all ages because radiation may cause children's leukemia and thyroid cancer • For occupational cohort, it would be better that worker's family are also included (workers may bring radiation exposure to home) • For general population cohort should include children of all ages.

6. Exposure Assessment • Occupational cohort should have personal radiation monitor as well as site monitor • General population cohort sample site should be corresponded to the site of radiation monitoring • Setting-up air pollution monitoring if possible.

7. Sample size justification • Estimate the power of your study

8. Questionnaire Design and Interview • IRB approval and Informed consent is needed for this type of study • Interviewers' training is very important: • Close monitoring the quality of questionnaire as well as the correspondent biological specimen collection • try to avoid missing data • Use double entry to avoid mistakes in data entry

9. Follow-up • Setting up the follow-up durations (2 years or 4 years, etc.) • Decide what need to be collected in the follow-up study (exposure status, end-point, biological specimens, additional exposure data.

10. Biological specimens • blood sample storage. At this point, -20 degree is a feasible way of the storage. It would be better to have two tubes of blood, EDTA and non-EDTA (or other chemicals). Minimum 10 ml blood • Urine: Consider to collect urine and then to get cells from urine and discard the fluid part, if there is any problem of storage • Buccal sample. For these who do not want to give blood, you should ask them to donate buccal cell sample • Hair sample?

11. Biological specimen transportation and storage • The transportation of biological samples need to be kept in low temperature, the best way is the have dry ice. Otherwise, to have blue ice for a short time. • The biological specimen be stored in two separate sites, so that when there is anything happened, we still have another set of sample to use

12. Disease Registry and Reporting System • Setting-up internet based disease registry and reporting system, including cancer, chronic and infectious disease as disease monitoring system which could be an important follow-up system for end-points.

13. Prospective Studies: Strengths • Exposure is measured before the outcome • The source population is defined • The participation rate is high if specimen are available for all subjects and follow-up is complete

14. Prospective Studies: Weaknesses • The usually small number of cases of each of many type of cancer • The lack of specimen if the biomarker requires large amounts of specimen or unusual specimens • Degradation of the biomarkers during long-term storage • The lack of details on other potentially confounding or interacting exposures

15. Prospective Studies • The major concern of cohort studies of the short duration (as in case-control studies) is the possibility that the disease process has influenced the biomarker level among cases diagnosed within 1 to 2 years of the specimen being collected.

16. Prospective Studies: Misclassification • In prospective studies in longer duration, there may be considerable misclassification of the etiologically relevant exposures if the specimens have been collected only at baseline. • This misclassification occurs when individual’s exposure level may change systematically over time and there may be intra-individual variation in biomarker level.

17. Prospective Studies: Intra-Individual Variation • The intra-individual misclassification may be reduced by taking multiple samples, but this will generally increase expenses of sample collection and storage and the burden on study subjects • Similar approaches apply to taking sample at several points in time in an attempt to estimate time-integrated exposures or exposure change.

18. Prospective Studies • An alternative approach is to estimate the extent of intra-individual variation, and the misclassification involved in taking single specimens, by taking multiple specimens in a sample of the cohort. • This information can be used to correct for bias to the null introduced if the misclassification is non-differential, and therefore de-attenuate observed relative risks

19. Prospective Studies: Ethical Issues • Repeated contact of subjects • Informing the cohort members of their biomarker level is problematic if the biomarker is not considered to be sufficiently predictive of disease and if there is no preventive steps cohort members can take to reduce their risk of the disease

20. Nested Case-Control Study • The biomarker can be measured in specimens matched on storage duration • The case-control set can be analyzed in the same laboratory batch, reducing the potential for bias introduced by sample degradation and laboratory drift

21. Intervention Studies • In studies of smoking cessation intervention, we can measure either serum cotinine or protein or DNA adducts (exposure) or p53 mutation, dysplasia and cell proliferation (intermediate markers for disease) • Measure compliance with the intervention such as assaying serum b-carotene in a randomized trial of b-carotene.

22. Intervention Studies Susceptibility markers (GSTM1) can also be used to determine whether the randomization is successful (comparable intervention and control arms)

23. Case-Control Study Design • For genetic susceptibility markers, case-control study design is highly appropriate • Clinic-based case-control studies are particularly suitable for studies of intermediate endpoints, as these end-point can be systematically measured. • Clinic-based case-control studies are excellent for studying etiology of precancerous lesions (e.g., CIN)

24. Case-Control Study Design • Biomarkers of internal dose (e.g., carrier status for infectious agents, such as HBsAg) or effective dose (PAH DNA adducts) are appropriate when they are stable over a long period of time or when the exposures have been constant over exposure period. However, it is essential that you are not affected by the disease process, diagnosis, or treatment.

25. Case-Control Study Design: ETS or Air Pollution and Lung Cancer • 1. Hypothesis: • Environmental Tobacco Smoking and other Environmental exposures may be associated with lung cancer among non-smoking women. 

26. Case-Control Study Design: Hypothesis • Women have less tobacco smokers and prevalence of male smoking is relatively high • Women’s lung cancer is different from men’s lung cancer. • High proportion of female lung cancer is adenocarcinoma of the lung • The RR for women’s lung cancer is higher than male lung cancer, which indicates that women may be susceptible to low dose tobacco smoking exposure • ETS have very similar carcinogens as active tobacco smoking, but ETS may have some carcinogens with high concentration.

27. Case-Control Study Design • A population-based case-control study (population-based versus hospital-base case-control studies) • Inclusion (women, non-smokers. Definition of non-smokers: lifetime cigarette smoking of less than 100 cigarettes)

28. Consideration of Controlling for Confounding Factors • There are several ways of controlling for potential confounding factors. In the designing stage, we can design a study which can control for potential confounding effects, including: (1) randomization (assigned subjects into treatment and control groups; (2) matching; (3) exclusion/inclusion. In the data analysis phase, we can use (4) stratified analysis such as M-H methods and (5) multivariate analysis such as proportional hazards model and logistic regression model to control for potential confounding effects.

29. Study Population: Case Selection • Age: 35-75 • Gender: female • All new cases if possible (not a random sample of new cases) • Newly diagnosed or incident cases (not prevalent cases, why?) • Pathological diagnosis of lung cancer • In a stable mental and physical status

30. Study Population: Control Selection • Controls should be selected from the population where the cases from and be a representative sample of the source population. • Matching variables: (age, gender, race. the residence should not be matched for this hypotheses, why?) • Frequency versus individual matching • Random sample of the general population (stratified sampling by matching variables) • Random digital dialing, DMV registration, etc.

31. Data Collection: Questionnaire • ETS exposures: exposure as child at home, exposure from spouse and other family members, and exposure from co-workers at working environment • Other potential confounding factors: Other indoor air-pollution, cooking oil fume, coal smoke, etc. occupation history and exposure, family history of cancer, alcohol drinking, etc.

32. Data Collection: Biological Specimens • Blood samples, urine samples, tissue specimens • ETS measurements (urine or blood levels of cotinine, hemoglobin protein adducts, PM2.5, etc.) • DNA adducts at lung tissue (only for cases with surgery)

33. Sample Size, Statistical power, and Data Analysis • Sample size: • Case-control ratio • Alpha level (0.05) • Beta level (0.20 or power 0.80) • OR=1.5 • Consideration of interactions and confounding effects • Data analysis: M-H methods, Logistic Regression Models.

34. Issues in Study Design and Analysis • Relating a particular disease (or marker of early effect); to a particular exposure; while minimizing bias; controlling for confounding; assessing and minimizing random error; and assessing interactions