Study Design in Molecular Epidemiology of Cancer

1. Study Design in Molecular Epidemiology of Cancer Epi243 Zuo-Feng Zhang, MD, PhD

2. Objectives of Molecular Epidemiology To gain knowledge about the distribution and determinants of disease occurrence and outcome that may be applied to reduce the frequency and impact of disease in human populations.

6. Cross-Sectional Studies • Exposure (questionnaire or measured exposure markers) and a biomarker as an end-point among healthy population, e.g., ETS (exposure) and serum nicotine metabolites (cotinine levels) • Sometimes use biological markers to validate questionnaire data.

8. Case-Control Studies • Disease end-point as a major interest • Clinical (Hospital)-based or population-based case-control studies • Inclusion of both questionnaire data and biological specimens • Biological markers can be measured and compared between cases and controls when other variables can be used as either confounding factors or effect modifiers

9. Case-Control Studies • In population-based studies, the collection of biological material for such markers is feasible but logistically more complex. • For early biological marker, collection of materials (e.g., pre-cancerous lesions) is logistically feasible in a hospital setting, but become more difficult in the population setting

10. Case-Cohort Study Design • Collecting the specimens at the baseline for entire cohort and then collecting specimens from cases as they occur. • Measuring the biomarker using newly collected specimen and using the baseline cohort specimen as control. • Because the specimens for cases and controls are taken at the different times for cases and controls, bias will be introduced if sample degradation or lab drift occurs over time

11. Case-Control Study • 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)

12. Case-Control Study • 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.

13. Prospective Cohort Studies • 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 Cohort Studies • 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 Cohort 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 Cohort Studies • 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 Cohort Studies • 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 Cohort 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 Cohort Studies • 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. The Case-Case Design: Applications in Tumor Markers and Genetic Polymorphisms Studies

22. Case-Case Study Design • To identify etiological heterogeneity • To evaluate gene-environment interaction

23. Case-Case Study Design • Case-only, Case-series, etc. • Studies with cases without using controls • Can be employed to evaluate the etiological heterogeneity when studying tumor markers and exposure • May be used to assess the statistical gene-environment or gene-gene interactions

24. Interaction Assessment using Case-Control Study Genotype abnormal OR1 Genotype normal OR2 Interaction measure OR1/OR2 here OR2=OR01 OR1=OR11/OR10 OR Interaction= OR11/(OR10xOR01)

25. Comparison of Case-Control and Case-Case Study designs

26. Assumptions for Case-Case Study Design • Exposure and genotype occur independently in the population • The Risk of disease is small (or the disease is rare) at all level of the study variables

27. Smoking and TGF-alpha Polymorphism From Rothman & Greenland, p.615

28. OR int= OR11/(OR01 x OR10) = 5.5/(1.0 x 0.9)=6.1 OR CA=(A11 x A00)/(A10 x A01)= (13 x 36)/(13 x 7)=5.1

29. OR int=OR CA/OR CO=[OR 11/(OR01xOR10)] OR11=A11 B00/A00 B11 OR CA = [OR 11/(OR01xOR10)] x OR CO Assumption: OR CO=1, OR int = OR CA

30. Sample Size

31. Strengths of Case-Case Study Design • Case-Case study design offers greater precision for estimating gene-environment interaction than case-control study design • The power for detecting gene environment interactions in case-case study is comparable to the power for assessing a main effect in a classic case-control study. Which leads to reduced sample size for interaction assessment.

32. Strengths of Case-Case Study Design • Only cases are needed, thus avoiding the difficulties and often unsatisfying selection of appropriate controls (avoiding selection bias for controls)

33. Limitations of Case-Case Study Design • The main effects of susceptible genotype (G) and environment effect (E) cannot be estimated • The case-case study will miss gene-environment models with departures from additivity.

34. 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.

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

36. Family Studies • Does familial aggregation exist for a specific disease or characteristic? • Is the aggregation due to genetic factors or environmental factors, or both? • If a genetic component exists, how many genes are involved and what is their mode of inheritance? • What is the physical location of these genes and what is their function?

37. 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

38. Sample Size and Power Consideration EPI243: Molecular Epidemiology of Cancer

39. Sample Size and Power • False positive (alpha-level, or Type I error). The alpha-level used and accepted traditionally are 0.01 or 0.05. The smaller the level of alpha, the larger the sample size.

40. Sample Size and Power • False negative (beta-level, or Type II error). (1-beta) is called the power of the study. Investigator like to have a power of around 0.80 or 0.95 when planning a study, which means that there have a 80% or 95% chance of finding a statistically significant difference between study and control groups.

41. Sample Size and Power • The difference between study and control groups (delta). Two factors need to be considered here: one is what difference is clinically important, and the another is what is the difference reported by previous studies.

42. Sample Size and Power • Variability. The more the variability of the data, the bigger the sample size.

43. Power or Sample Size Estimate for Case-Control Studies • Alpha-level (false positive): 0.05 • Beta-level (false negative level; 1-beta=power): 0.20 • Delta-level: Proportion of exposure in controls and exposure in cases or expected odds ratio

44. Power Estimate