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“Personality, Socioeconomic Status, and All-Cause Mortality in the United States”
- Chapman BP et al.
Journal Club02/24/11
How we deal with bias
Jurek et al. 2006, Orsini 2007
1) 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
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
Let’s look at uncertainty arising from:
1) Unmeasured confounding due to smoking2) 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 (Pe) and asbestos non-exposed (Pne)
• Association (ORs) between smoking and case status
Phillips 2003, Steenland et al. 2004
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
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
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 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
Personality and Mortality
Roberts et al. 2007
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 25-74
• Random digit dialing starting in 1995
Study population
Study population
6,063 contacted
4,244complete
telephone interview
2,998 with
completedata
70%
3,692 returnmail
survey
71%
87% 81%
• Participants contacted for 10-year follow-up in 2004-2005
• Names of subjects lost to follow-up submitted to NDI
• All-cause mortality
Methods: Mortality status
Methods: SES factor analysis
INCOME
TOTALASSETS
OCCUPATIONALPRESTIGE
EDUCATIONContinuous
Factor Scores
Methods: Personality
• Midlife Development Inventory
• 30 Likert scale items
• Factor analysis used to separate:
1) Agreeableness2) Openness3) Neuroticism4) Extraversion5) 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
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