Considerations of Multicenter Studies in Cancer Epidemiology

1. Considerations of Multicenter Studies in Cancer Epidemiology Yuan-Chin Amy Lee, PhD Epidemiology 295 Fall 2009

2. Motivation (I): statistical power • Meta-analysis can only rely on already calculated estimates with variable adjustments • Dose-response (?) • Stratified analysis (?) • Interaction (?) • Early onset of disease outcome • Non-smokers • Non-drinkers

3. Motivation (II)

4. Motivation (III)

5. Types of Multicenter Studies • Pooled analyses (e.g. International Head And Neck Cancer Epidemiology consortium–INHANCE; International Lung Cancer Consortium-ILCCO) • Multicenter case-control studies (e.g. Alcohol-Related Cancer And Genetic susceptibility in Europe-ARCAGE)

6. Definition of Pooled Analyses • Obtaining raw data from individual studies • Transforming these datasets into a common format • Merging the data together for analysis

7. The Difference between A Meta-Analysis and A Pooled Analysis • Meta-analysis • Using published risk estimates • Pooled analysis • Using individual level data • Inconsistency in terminology use

8. Steps for Pooled Analysis • Study selection • Inclusion and exclusion criteria development • Data request • Data validation • Data standardization • Data analysis • Pooled estimates • Heterogeneity test • Publication bias assessment • Subgroup and stratified analyses

9. Study Selection (I) • To collect a list of relevant studies • To tabulate the study design, laboratory methods, and analysis of the data • To set inclusion and exclusion criteria

10. Study Selection (II): study design • Cross-section • e.g. markers of exposure • Case-control • e.g. genetic markers • Cohort • Limit recall and selection bias

11. Study Selection (III) • Criteria • Appropriate source population • Sample size • Relevant variables • Appropriate measurement methods

12. Data Request (I) • Determine variables to be included • Send invitation letters • Make sure data are anonymous

13. Data Request (II): Questionnaire

14. Data Validity • Evaluate the reliability of the evidence from each study • Apply a quality scoring system

15. Data Standardization (I) • Standardization of variables of interest (both independent and dependent variables) • Possible solution: post-hoc data standardization, categorization of data within each study, application of statistical modeling for correlated data • Collection of a minimum set of epidemiological variables

16. Data Standardization (II): Questionnaire Wording

17. Data Standardization (III): Question comparability

18. Definition of Ever Smokers in Each Study

19. Data Analysis: Forest Plot (Hashibe 2007 JNCI)

20. Inverse Variance Weighting (1/SE2) More precise, narrower confidence interval, more weight given Less precise, wider confidence interval, less weight given Breast cancer & alcohol (Hamajima 2002 BJC)

21. Heterogeneity Test • Heterogeneity: there are differences in the risk estimates across certain strata • Heterogeneity due to the different distribution of risk factors vs. attributable to external variables • Use of univariate analysis to evaluate the possible source of heterogeneity • Removal of outliers with no obvious explanation • It is inappropriate to calculate the summary estimate if there is heterogeneity

22. Heterogeneity • It is important to assess & present characteristics of the individual study, to examine sources of heterogeneity • Examples of characteristics to assess: • Study design • Sample size • Study location • Study period • Subject eligibility criteria • Ascertainment methods • Matching of controls • Definition of disease (histology?) • Exposure assessment methods

23. Test for Heterogeneity • A test of the hypothesis θi = θ for all i is a test for true differences between studies • Small p-value  reject homogeneity

24. Heterogeneity

25. Expected value of a study estimate is modeled as a fixed function of measured study characteristics Disadvantages Assumption of a true effect fixed across all studies Within group homogeneity assumption not realistic Allows for heterogeneity between studies for unknown sources More conservative (usually the estimate does not change but CI widens), but not always Disadvantages Smaller studies are given more weight than in fixed model If there is substantial heterogeneity, it may be inappropriate to summarize RRs Fixed Effects Model vs. Random Effects Model

26. Fixed vs. Random Effects With limited heterogeneity, point estimates may be similar but the CIs are wider When there is heterogeneity, point estimates may differ

27. Publication Bias • Definition: a tendency of journals to accept preferentially papers reporting an association over papers reporting no association • Comparison of the frequency of relevant variables before pooling • Assessment of inclusion bias

28. Tests for Publication Bias • Funnel-plot assymetry • Rank correlation method (Begg & Mazumdar, 1994) • A direct statistical analogue of the visual funnel graph • Power for detecting bias is limited, publication bias cannot be ruled out if test is not significant • Tests for correlation between effect estimates and their variances • Weighted regression (Egger et al, 1997) • Suggests presence of publication bias more frequently than the Begg approach • Detects funnel plot assymetry by determining whether the intercept deviates significantly from zero in a regression of standardized effect estimates against their precision

29. Influence Analysis Dropping the Buch study results in a decrease in the summary estimate But the drop is from 1.38 to 1.30, and does not change the inference

30. Influence Analysis In this example, dropping the ISIS-4 study changes the inference from no association, to a protective association. Thus it would be inappropriate to conclude there is no association. Systematic Reviews in Health Care, 2nd edition

31. Examples of Pooled Analyses • INternational Head And Neck Cancer Epidemiology (INHANCE) consortium (inhance.iarc.fr) • International Lung Cancer COnsortium (ILCCO) (ilcco.iarc.fr) • International Liver Cancer Study (ILCS) (ilcs.iarc.fr)

33. Summary of studies included in the project on nonsmokers

34. Associations with involuntary smoking in the overall study population

35. Associations with involuntary smoking in the overall study population

36. Associations with involuntary smoking among nonsmokers

37. Associations with involuntary smoking among nonsmokers

38. Associatins with SNP in the overall study population

39. Associatins with SNP in the overall study population

40. Associatins with SNP in nonsmokers

41. Associatins with SNP in nonsmokers

42. Relevant References for Pooled Analysis • C. Wild, P. Vineis, S. Garte. Molecular Epidemiology of Chronic Diseases. (Chapter 15)

43. Multicenter Studies

44. Multicenter Studies • Definition: a clinical trial that is carried out at more than one medical institution

45. Advantages & Disadvantages • Advantages over pooled analyses • Designed for the same objectives • Same questionnaire • More complete adjustment variables • Disadvantages • More time and efforts to reach consensus among colloaborators (e.g. to agree on one set of questionniare, to analyze the data, etc.)

46. An Example of Multicenter Case-Control Study • The association between tobacco smoking and upper-aerodigestive-tract cancer risk in western Europe (ARCAGE study)

47. Variable Issues for Education

48. Tartu Oslo Edinburgh Newcastle Dublin Manchester Bremen Prague Aviano Padova Inserm Zagreb Barcelona Athens Alcohol-Related Cancers And Genetic Susceptibility in Europe (ARCAGE) • Study period 2002-2005 • 16 research centers • UADT cancer in Europe • 2103 cases/2221 controls • European Commission grant (QLK1-CT2001-00182)

49. Demographic Characteristics among SCC Cases and Controls.

50. Oral Cavity & Oropharynx p <0.001 p <0.001 p <0.001 p <0.001