“Personality, Socioeconomic Status, and All-Cause Mortality in the United States”

- Chapman BP et al.

Journal Club02/24/11

Monte Carlo Sensitivity Analysis(MCSA)

but first...

Introducing nonrandom error

Phillips, 2003

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

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

Asbestos150 80

50 80€

D

€

D

€

E

€

E

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

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 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

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. 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

INCOMETOTAL

ASSETS

EDUCATION OCCUPATIONALPRESTIGE

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

Results

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?