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

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

Introducing nonrandom error Phillips, 2003

How we deal with bias Jurek et al. 2006, Orsini )Ignore biases 2)Mention potential biases (in passing) 3)Qualitatively address the effect of bias 4)Quantitatively address the effect of bias (sensitivity analysis)

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

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

Deterministic SA Rothman et al. 2008

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

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!

An example of MCSA Observed OR = 3.0 Lung Cancer Asbestos

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...

1) Unmeasured confounding and MCSA What we need to generate: Prevalence of smoking among asbestos exposed (P e ) and asbestos non-exposed (P ne ) Association (OR s ) between smoking and case status Phillips 2003, Steenland et al. 2004

1) Unmeasured confounding and MCSA Start by generating uniform distributions for P e and P ne P e is bounded: [0.5, 0.8] P ne is bounded: [0.2, 0.5] Generate 10,000 random numbers for each

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

1) Unmeasured confounding and MCSA

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

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

Bringing it together... Algorithm uses formulas for external adjustment method (see Rothman book 3 rd 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

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)

How to implement it

On to the paper!

Socioeconomic Status and Mortality Pappas et al. 1993

Personality and Mortality Roberts et al Linked with mortality risk: Optimism Neuroticism Hostility Trust Conscientiousness Cynical distrust Rationality Extraversion Creativity Agreeableness Trait Anxiety

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: 1)SES and personality are clustered mortality risk factors 2)SES and personality are independent mortality risk factors

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

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

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

Methods: SES factor analysis INCOME TOTAL ASSETS EDUCATION OCCUPATIONAL PRESTIGE

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

Methods: Personality Midlife Development Inventory 30 Likert scale items Factor analysis used to separate: 1)Agreeableness 2)Openness 3)Neuroticism 4)Extraversion 5)Conscientiousness Lachman 1997

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

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

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

Results

SES effect is reduced: 20% Neuroticism effect is reduced: 8%

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

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