Journal Club 02/24/11

1. Journal Club 02/24/11 “Personality, Socioeconomic Status, and All-Cause Mortality in the United States”- Chapman BP et al.

2. Monte Carlo Sensitivity Analysis(MCSA) but first...

3. Introducing nonrandom error Phillips, 2003

4. How we deal with bias • Ignore biases • Mention potential biases (in passing) • Qualitatively address the effect of bias • Quantitatively address the effect of bias • (sensitivity analysis) Jurek et al. 2006, Orsini 2007

5. Why should we care? • Participation rates in epidemiologic studies are falling (selection bias?) • Reviewers almost certainly ask about different forms of bias • Better understand uncertainty behind your findings Galea et al. 2007, Curtin et al. 2005

6. Types of quantitative sensitivity analysis 1) Deterministic • e.g. externally-adjusted estimates 2) Probabilistic • e.g. MCSA

7. Deterministic SA Rothman et al. 2008

8. Problems with Deterministic SA • Fail to discriminate among the different scenarios in terms of their likelihood • Difficult to summarize results • Difficult to examine effects of multiple biases Orsini 2007

9. An example of MCSA Analysis of a case-control study to determine whether case status (lung cancer) is associated with exposure to asbestos. However, there is no measure of smoking duration!

10. An example of MCSA Observed OR = 3.0

11. An example of MCSA Let’s look at uncertainty arising from: 1) Unmeasured confounding due to smoking 2) Exposure misclassification With MCSA we can simultaneously examine bias arising from these sources...

12. 1) Unmeasured confounding and MCSA What we need to generate: • Prevalence of smoking among asbestos exposed (Pe) and asbestos non-exposed (Pne) • Association (ORs) between smoking and case status Phillips 2003, Steenland et al. 2004

13. 1) Unmeasured confounding and MCSA • Start by generating uniform distributions for Pe and Pne • Pe is bounded: [0.5, 0.8] • Pne is bounded: [0.2, 0.5] • Generate 10,000 random numbers for each

14. 1) Unmeasured confounding and MCSA

15. 1) Unmeasured confounding and MCSA • Generate a normal distribution of odds ratios betweens smoking (confounder) and lung cancer (outcome) • Consult literature for distribution parameters • Generate 10,000 random numbers

16. 1) Unmeasured confounding and MCSA

17. 2) Exposure misclassification and MCSA What we need to generate: • Sensitivity and specificity distributions for cases and controls • Let’s assume recall bias has occurred (differential misclassification) • Cases remember true exposure more than controls Greenland et al. 2008

18. 2) Exposure misclassification and MCSA • Generate normal distributions • Cases: • Mean sensitivity = 0.95 • Mean specificity = 0.75 • Controls: • Mean sensitivity = 0.75 • Mean specificity = 0.75 • Bound values: [0,1] • Generate 10,000 random numbers for each

19. Bringing it together... • Algorithm uses formulas for external adjustment method (see Rothman book 3rd ed) • Correct for biases in reverse order of data generation process • Pick a random number from each of the above distributions and back-calculate a new OR • Repeat this 250,000 times Phillips 2003, Steenland et al. 2004, Greenland et al. 2008

20. Median OR: 1.55 Middle 5% OR’s: (1.52 – 1.57) Middle 90% OR’s: (1.13 – 2.06) Range: (0.70 – 3.26)

21. How to implement it

22. On to the paper!

23. Socioeconomic Status and Mortality Pappas et al. 1993

24. Personality and Mortality • Linked with mortality risk: Roberts et al. 2007

25. Purpose of the study • To examine degree to which SES and personality are mutually confounded risks in predicting all-cause mortality among US adults • Two possibilities: • SES and personality are clustered mortality risk factors • SES and personality are independent mortality risk factors

26. Study population • Midlife Development in the United States (MIDUS) study • English-speaking adults aged 25-74 • Random digit dialing starting in 1995

27. Study population 70% 87% 81% 2,998 with complete data 4,244 complete telephone interview 6,063 contacted 3,692 return mail survey 71%

28. Methods: Mortality status • Participants contacted for 10-year follow-up in 2004-2005 • Names of subjects lost to follow-up submitted to NDI • All-cause mortality

29. Methods: SES factor analysis INCOME TOTAL ASSETS EDUCATION OCCUPATIONAL PRESTIGE

30. Methods: SES factor analysis TOTAL ASSETS OCCUPATIONAL PRESTIGE INCOME EDUCATION Continuous Factor Scores

31. Methods: Personality • Midlife Development Inventory • 30 Likert scale items • Factor analysis used to separate: • Agreeableness • Openness • Neuroticism • Extraversion • Conscientiousness Lachman 1997

32. Methods: Covariates Demographic factors: • Age • Sex • Ethnicity Behavioral risk factors: • Smoking • Heavy drinking • BMI • Physical activity

33. Methods: Analysis Primary analysis: • Stepwise logistic regression • Adjusted population attributable fractions Secondary analysis: • Interactions among personality domains • Mortality risk associated with “traits” within each personality domain

34. Methods: Analysis Sensitivity/Error analysis: • Change in estimate in SES for all 32 combos of personality domains • Multiple imputation: missing data bias • Simulation extrapolation: random measurement error in health behaviors • MCSA: unmeasured confounding, selection bias, nonrandom error in personality or SES measurement

35. Results

36. Results • SES effect is reduced: 20% • Neuroticism effect is reduced: 8%

37. Discussion: Key findings • Support for both correlated risk model and independent risk models • Low SES is mortality risk factor (no surprise) • High Neuroticism is mortality risk factor (no surprise) • Agreeableness X Conscientiousness interaction • Health behaviors explain substantial amount of SES and personality effects

38. Thoughts on the strengths and weaknesses of this study? Significance of this study?