Adjusting for Time- Varying Confounding in Survival Analysis Natalya Verbitsky Joint Work with...
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Transcript of Adjusting for Time- Varying Confounding in Survival Analysis Natalya Verbitsky Joint Work with...
Adjusting for Time-Varying Confounding in
Survival Analysis
Natalya Verbitsky
Joint Work with Jennifer BarberSusan MurphyUniversity of Michigan
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Outline
Introduction Problem Possible Solutions
Evaluation Data Simulation Statistical Models
Results “Path Analysis”
Rules Simulation Results
Conclusions Future Work
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Introduction We are concerned with cause-and-
effect relationship Q: If educational opportunities for
children in poor countries are increased, would couples limit their total family size via contraception?
Q: Does having a teenage birth curtail educational attainment?
Problem:
How do we assess the effect of an exposure on a response in the presence of confounders in a time-varying setting?Def: Confounders are variables that affect both response and predictors either directly or indirectly.
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Example:Q: If educational opportunities for
children in poor countries are increased, would couples limit their total family size via contraception?
Exposure: schooling children Response: limiting family size via contraception Some Possible Confounders: political power,
parents’ education, number of children in the family, proximity of school nearby
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Possible Solutions: Some Traditional Methods
Naïve approach: regular logistic regression of response on the exposure
Standard approach: regular logistic regression of response on exposure including confounders as covariates
New Method: Weighted logistic regression
)|expPr(
exp)Pr(
confConfExpos
ExposWeight
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Data Simulation Diagram
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Conf10 Conf11
Expos0 Resp0 Expos1 Resp1
Conf20 Conf21
Time 0 Time 1
UnmeasUnmeasured Confounder
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Variables: Unmeas=unmeasured confounder
Ex: political power Unmeas=2, high Unmeas=1, medium Unmeas=0, low
Conf1j = binary measured confounder 1 at time j, j=0 or 1 Ex: presence of school nearby
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Variables (Con’d)
Conf2j = binary measured confounder 2 at time j, j=0 or 1 Ex: having a small family
Exposj = binary exposure at time j, j=0 or 1 Ex: any child in the family has attended school
by time j
Respj = binary response at time j, j=0 or 1 initiating permanent contraception at time j
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Statistical Models: Model 1: Regular Logistic Regression (non-
parametric) Logit Pr(Resp0 = 1)=a0 + a2 Expos0 Logit Pr(Resp1 = 1)=a0 + a1 + a3 Expos0 + a4 Expos1
Model 2: Logistic Regression with Confounders Logit Pr(Resp0 = 1)=a0 + a2 Expos0 + a5 Conf10 + a6 Conf20
Logit Pr(Resp1 = 1)=a0 + a1 + a3 Expos0 + a4 Expos1 + a5 Conf11 + a6 Conf21
Model 3 and 4: Regular Logistic Regression with Weights (do not include confounders as covariates) Model 3 weights: both confounders included Model 4 weights: only Conf1 is included
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“Path Analysis” Rules Find all possible path between your
predictor and response To calculate the effect of a particular
path: Multiply the effect within the path If you conditioned on a variable and the
arrows of the path meet there, multiply the arrow effect by -1
Add the effect of all possible paths to get the total effect
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Path Analysis forExpos0 on Resp1 in Naïve Model:
Logit Pr(Resp0 = 1)=a0 + a2 Expos0
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Conf1 0 Conf1 1
Expos0 Resp 0 Expos 1 Resp 1
Conf2 0 Conf2 1
Time 0 Time 1
UnmeasUnmeasured Confounder
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Path Analysis for Expos0 on Resp1 in Naïve Model:Logit Pr(Resp1 = 1)=a0 + a1 + a3 Expos0 + a4 Expos1
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Conf1 0 Conf1 1
Expos0 Resp 0 Expos 1 Resp 1
Conf2 0 Conf2 1
Time 0 Time 1
UnmeasUnmeasured Confounder
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Results (general) The values inside the cells represent
1st line: average estimate and average std. error
2nd line: the proportion of the time you would reach the wrong conclusion
All results are based on 1,000 samples (unless otherwise specified) of 1,000 observations each
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Results (Table 1)Naive Standard Weighted
(Conf1, Conf2)Prt. Wtd(Conf1)
Effect of Expos0 on Resp0
0.31 (.15)0.56
-0.03 (.16)0.07
-.002 (.14)0.04
0.13 (.14)0.13
Effect of Expos0 on Resp1
0.22 (.20)0.24
-0.43 (.23)0.48
-.003 (.20)0.05
0.09 (.20)0.09
Effect of Expos1 on Resp1
0.33 (.30)0.22
-0.14 (.39)0.07
0.02 (.30)0.05
0.15 (.30)0.08
Note: intercepts are 0.0; alphas=etas=1.5; gammas=0.5
• biased estimates in regular logistic regression (Model 1)• biased estimates in Model 2 in the estimates of past values of predictors; no bias in the estimates of present values of predictors• According to “path analysis” rules expect positive bias in 1st and 3rd cells of Model 1; and a negative bias in the middle cell of Model 2
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Path Analysis forExpos0 on Resp1 in Standard Model:Logit Pr(Resp1 = 1)=a0 + a1 + a3 Expos0 + a4 Expos1 + a5 Conf11 + a6 Conf21
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Conf1 0 Conf1 1
Expos0 Resp 0 Expos 1 Resp 1
Conf2 0 Conf2 1
Time 0 Time 1
UnmeasUnmeasured Confounder
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Path Analysis for Weighted Model
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Conf10 Conf11
Expos0 Resp0 Expos1 Resp1
Conf20 Conf21
Time 0 Time 1
UnmeasUnmeasured Confounder
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Results (Table 2)
Naïve Standard Weighted Prt. Wtd
Effect of Expos0 on Resp0
-0.29 (.13)0.58
-.001 (.14)0.04
-0.01 (.12)0.02
-0.13 (.13)0.14
Effect of Expos0 on Resp1
-0.18 (.18)0.14
-1.17 (.24)0.99
-.0004 (.18)0.03
-0.08 (.18)0.06
Effect of Expos1 on Resp1
-0.31 (.25)0.24
0.10 (.31)0.06
0.002 (.25)0.04
-.13 (.25)0.08
Note: intercepts are 0.0; alphas=etas=-1.5; gammas=0.5
• According to “path analysis” rules of thumb, expect negative bias in the 1st and 3rd estimates of Model 1 and in the middle row of Model 2; on the other hand, expect unbiased estimates in Model 3
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Results (Table 3)
Naïve Standard Weighted Prt. Wtd.
Effect of Expos0 on Resp0
0.17 (.14)0.21
0.01 (.14)0.05
-0.01 (.14)0.05
0.08 (.14)0.09
Effect of Expos0 on Resp1
0.07 (.19)0.06
-.004 (.20)0.05
-0.01 (.20)0.05
0.03 (.19)0.06
Effect of Expos1 on Resp1
0.10 (.27)0.07
-.04 (.28)0.05
-.004 (.28)0.04
0.04 (.28)0.04
Note: intercepts are 0.0; alphas=etas=0.5; gammas=0.5
• Compare with Table 1, degree of bias depends on the values of alphas and etas• Including weights does not hurt your analysis
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Results (Table 4)
Naive (parsim.)
Standard (parsim.)
Weighted (parsim.)
Prt. Wtd. (parsim.)
Effect of Expos0 on Resp0
0.31 (.15)0.56
-0.09 (.16)0.10
-.002 (.14)0.04
0.13 (.14)0.13
Effect of Expos1 on Resp1
0.49 (.25)0.51
-0.45 (.34)0.30
0.02 (.25)0.04
0.22 (.26)0.14
Note: intercepts=0.0; alphas=etas=1.5; gammas=0.5
• Compare with Table 1, in parsimonious models 1 and 2 bias in the estimates of effect of Expos1 on Resp1 has increased• Model 3 has unbiased estimates
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Results (Table 5)Naïve Standard Weighted Prt. Wtd.
(Conf1)Prt. Wtd.(Conf2)
Effect of Expos0 on Resp0
.27 (.14)0.47
-.03 (.15)0.04
0.003 (.14)0.03
.09 (.14)0.08
.15 (.14)0.15
Effect of Expos0 on Resp1
.17 (.20)0.16
-.51 (.23)0.62
-0.03 (.20)0.06
.01 (.20)0.06
.11 (.20)0.10
Effect of Expos1 on Resp1
.30 (.29)0.18
-.07 (.36)0.05
-.002 (.29)0.06
.06 (.29)0.06
.20 (.30)0.10
Note: intercepts=0.0; alpha1j=2.25, alpha2j=0.75 j=0 or 1; etas=1.5Gammas=0.5
• Model 3 is weighted logistic regression, weights use Conf1 and Conf2• Model 4 is weighted logistic regression, weights use only Conf1 (3/4)• Model 5 is weighted logistic regression, weights use only Conf2 (1/4)
•Weighting Model is robust to missing confounders
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Conclusions Regular logistic regression (Model 1)—biased
estimates Regular logistic regression with confounders
(Model 2)—bias due to confounders affected by past exposure
The sign of the bias can be found by using “path analysis” type rules of thumb
Degree of the bias depends on the strength of correlations between Unmeas and Conf, Conf and Expos, and past Expos and Conf
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Conclusions (Con’d):
Parsimonious Models have biased estimates of effect of Expos on Resp
The use of weights incorporating all confounders eliminates this bias
Weighting method is robust to missing confounders
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Future Work
Diagnostics for situations with “bad” weights