Analyzing Observational Data: Focus on Propensity Scores

Report
Analyzing Observational Data: Focus
on Propensity Scores
Arlene Ash
QMC - Third Tuesday
September 21, 2010
(as amended, Sept 23)
1
The Problem
• Those with the intervention and those without have
markedly different values for important measured
risk factors &
• Outcome is related to the risk factors that are
imbalanced between the groups &
• It is not clear how the risk factors and outcome are
related
• Why may standard analyses be misleading?
2
True and Modeled Relationship
Between Risk and Outcome
1.0
Outcome
0.8
0.6
0.4
0.2
0
0
0.5
1.0
Risk
1.5
2.0
3
Is Imbalance in Risk a Problem?
• If we correctly model the relationship between
risk factors and outcome, we correctly estimate
effect of the intervention
• With many risk factors, hard to know if the
relationship between risk factors and outcome
is correctly modeled
• Propensity score - a way to reduce the effect of
imbalance in measured risk when models may
be inadequate
4
Propensity Score Method (Key Idea)
• The propensity score (PS) for an observation is
the probability that the observation is “exposed”
or “got the intervention”
• Use the PS model in pre-processing the data
– To draw a sub-sample where the exposed and nonexposed groups are fairly balanced on risk factors.
Then
– Use standard techniques to analyze the sub-sample5
Simple Propensity Score Approach
• Estimate a model to predict the “probability of
intervention/exposure”
– This is “the propensity score”
• Divide the population into PS quintiles
• Create a subsample by taking equal numbers of
exposed and unexposed observations from each quintile
• Use a subsequent regression model to estimate the
effect of the intervention in the subsample
6
Propensity Score Sampling Example
PS Quintile
# Cases
# Controls
# Sampled
Lowest
12
81
24
2nd
30
67
60
Middle
44
38
76
4th
53
15
30
Highest
78
8
16
217
209
206
Total
7
Propensity Score Sampling Example:
Treatments for Drug Abusers
• Patients seeking substance abuse detoxification in
Boston receive either
Residential detoxification
Lasts ~ one week + encouragement for postdetox treatment, or
Acupuncture
Acute (daily) detox + 3-6 months of maintenance
with acupuncture and motivational counseling
8
Data
• From Boston’s publicly-funded substance
abuse treatment system
• All cases discharged from residential detox or
acupuncture between 1/93 and 9/94
• Client classified (only once) as residential or
acupuncture based on the modality of first
discharge
9
Outcome
• Is client re-admitted to detox within 6
months? (Y/N)
• Study question: Are acupuncture clients
more likely to be re-admitted than
residential detox clients?
– Exposure = assigned to accupuncture
10
Client Characteristics Available At
Time Of Admission
•
•
•
•
•
•
•
Gender
Race/ethnicity
Age
Education
Employment status
Income
Health insurance status
• Living situation
• Prior mental health
treatment
• Primary drug
• Substance abuse
treatment history
11
Residential Detox & Acupuncture Cases:
% with Various Characteristics
Characteristic
Gender: female
Race/ethnicity: black
Hispanic
Residential
Acupuncture
(n = 6,907)
29
(n = 1,104)
33
46
46
12
White
41
10
43
Education: HS grad
College graduate
56
4
59
13
12
Characteristics of Residential Detox &
Acupuncture Clients (2)
Characteristic
Residential
Acupuncture
(n = 6,907)
(n = 1,104)
Employment: unemployed
Insurance: uninsured
Medicaid
Private insurance
Lives: with child
In shelter
86.8
43.2
65.4
52.3
28.2
21.2
3.0
15.4
9.5
19.3
30.3
2.9
13
Characteristics of Residential Detox &
Acupuncture Clients (3)
Residential Acupuncture
Characteristic
(n = 6,907)
(n = 1,104)
Prior mental health treatment
12.3
27.8
Primary drug: alcohol
42.3
32.4
Cocaine
16.2
16.6
Crack
15.9
20.2
Heroin
24.6
19.0
14
Characteristics of Residential Detox &
Acupuncture Clients (4)
Residential Acupuncture
Characteristic
(n = 6,907)
Substance abuse admits in the last year
Residential detox: 0
1
2+
Short-term residential: 0
Long-term residential: 0
Outpatient: None
Acupuncture: None
56.7
20.2
23.1
76.2
80.5
80.6
95.9
(n = 1,104)
81.0
12.1
7.0
94.8
93.5
54.3
90.1
15
Results Of Standard Analysis
Percentage of clients re-admitted to detox within 6 months
• Among 1,104 acupuncture cases, 18% re-admitted
• Among 6,907 residential detox cases, 36% re-admitted
• Raw odds ratio = 0.40
From a multivariable stepwise logistic regression model:
• Odds ratio for acupuncture:
0.71 (CI = 0.53-0.95)
16
What’s the Worry? How Do We
Address It?
• Given how different the two groups are, can we trust a
model to correctly estimate the effect of acupuncture?
• PS methods generalize (long-standing) matching-withinstrata methods that work well with 1 or 2 predictors
• PS can address imbalances in many important
predictors simultaneously
• Both traditional and PS matching allow for
– A pooled estimate (across all strata) or
– When N is large enough, stratum-specific estimates
17
Propensity Score Application
• Use stepwise logistic regression to build a model
to predict whether a client “is exposed”(i.e.,
receives acupuncture)
• Select sub-samples of exposed and non-exposed
with similar distributions of the “propensity score”
(predicted probability of being exposed)
• Model (as before) on the sub-sample
18
Sampling Results
• Able to match
740 who received acupuncture (out of 1,104)
with
740 people who did not (out of 6,907)
• The risk factors in this subsample of 1480 are
much more balanced between the two groups
19
Characteristics of Clients in Subsample
(vs. Full Sample)
Characteristic
College graduate
Employed
Private Insurance
Lives with child or adult
Lives in shelter
Prior mental health Rx
Residential
7%
41%
9%
72%
5%
21%
(4%)
(13%)
(3%)
(55%)
(30%)
(12%)
Acupuncture
7%
42%
6%
77%
4%
21%
(13%)
(57%)
(15%)
(76%)
(3%)
(28%)
20
Comparing Standard and Propensity
Score Findings
From the multivariable model fit to all cases:
Odds Ratio for acupuncture:
0.71
95% Confidence Interval:
0.53-0.95
From multivariable model fit to more comparable subsample:
OR for acupuncture:
0.61
95% CI:
0.39-0.94
21
Summary
• In this case, results were similar - Why?
Original model was very good (C-statistic = 0.96)
• What we learned from the PS analysis:
– Could find a subset of (about 10% of) patients
who got residential detox who look very similar
to those who got acupuncture
– Skeptics were more receptive to findings from the
PS analysis
22
Which X’s Belong in the PS Model?
The goal is to estimate the effect of exposure E
on outcome Y
• Confounders (Brookhart’s X1 variables)?
– Directly affect both E and Y
• Simple predictors (X2 s)?
– Affect Y but not E
• Simple selectors (X3 s)?
– Affect E but not Y
23
Example
The goal is to estimate the effect of
E = CABG surgery on
Y = 30-day mortality following admission for a
heart attack
– Confounder (e.g., disease severity)
– Simple predictors (e.g., home support)
– Simple selectors, aka “instrumental variables”
(e.g., random assignment)
24
Variable type
Belongs in
Directly affects
which model
Outcome Exposure
Subsequent
(Y)
(E) PS Regression
X1 Confounder
1
1
Yes
Yes
X2 Predictor
1
0
?
Yes
X3 Selector
0
1
No
?
? = inclusion should neither harm nor help
25
Discussion
• The “pre-processing” that occurs when subsampling to create “PS-balanced”
comparison groups protects against bias from
confounding variables
• Putting selector variables in the PS model will
hurt accuracy (by reducing the numbers of
good matches) without making the groups
more comparable
• Subsequent regression improves accuracy 26

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