Identifying Susceptibility in Epidemiology Studies: Implications for Risk Assessment. Joel Schwartz Harvard TH Chan School of Public Health

Transcription

1 Identifying Susceptibility in Epidemiology Studies: Implications for Risk Assessment Joel Schwartz Harvard TH Chan School of Public Health

2 Risk Assessment and Susceptibility Typically we do risk assessments by looking at mean changes in populations Assume small risk, large population exposed Sometimes enough cases to worry about No one at high risk If there is a lot of heterogeneity in response that may not be sufficient Some populations may have risks that are high and warrant attention

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4 But this assumes Susceptibility is independent of baseline risk and independent of exposure

5 Bind et al, EHP 2015

6 Bind et al, EHP 2015

7 How did we do that? Quantile Regression If instead of Minimizing the Sum of the Squares of the Errors, we Minimize the Sum of their Absolute Values We estimate how the Median depends on the Predictors If we weight positive and negative residuals differently, we can estimate other percentiles

8 If I know the potential susceptibility factor I can use interaction terms in standard regression

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10 Cold 1% Hot 99% Cold 1% Hot 99% Cold 1% Hot 99% Cold 1% Hot 99% Cold 1% Hot 99% Cold 1% Hot 99% Effect modification by medical condition and subject and area level characteristics of the effect of extreme hot and extreme cold temperature (1 th and 99 th percentiles of temperature) on total mortality: meta-analysis of 123 and 111 U.S. cities respectively during the period , among Medicare enrollees Alzheimer's Dementia Afib Non-white race High % poverty High % no High School

11 Genetic Susceptibility to Exposure Genome wide interaction studies Useful for finding the unexpected, but not powerful Pathway Analyses Useful for testing more expected pathways, more powerful

12 Oxidative Stress Defense Genes Modify the effects of Diesel PM on Fibrinogen

13 Interaction with TLR2 Methylation

14 Longitudinal Studies Separate Slopes for Separate Folks

15 Distribution of Personal Slopes for Effect of Black Carbon on SDNN Personal Slope

16 Again, I can now look to see Which people are more susceptible Percent Decrease in SDNN for IQR Change In BC

17 Causal Mediation Analysis A B C B is a mediator of the effect of A on C A can also have an effect on C by another pathway The effect of A on B may be heterogeneous across people Quantile can help here as well

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19 Case-Crossover Analyses Controls for all slowly varying covariates Estimates acute effects and identifies susceptibility factors

20 Percent increase in Risk of Death associated with a 10 g/m3 increase in PM10 concentration in Cook County Illinois, among population previously admitted to hospital for a diagnosis of heart or lung disease. Individuals Admitted to Hospital with Specific Condition Increase in Risk (per 10 g/m 3 ) 95% CI Relative Effect Modification * Myocardial Infarction 1.98 % (-0.25 %, 4.26 %) 2.67 Diabetes 1.49 % (-0.06 %, 3.07 %) 2.00 Congestive Heart Failure 1.28 % (-0.06 %, 2.64 %) 1.72 COPD 0.58 % (-0.82 %, 2.00 %) 0.78 Conduction Disorders 0.64 % (-0.61 %, 1.90 %) 0.85 None of the above conditions 0.74 % (-0.29 %, 1.79 %) Reference

21 Percent increase in Risk of Death associated with a 10 g/m3 increase in PM10 concentration in Cook County Illinois, among population previously admitted to hospital for a diagnosis of heart or lung disease. Individuals Admitted to Hospital with Specific Condition Increase in Risk (per 10 g/m 3 ) 95% CI Relative Effect Modification * Myocardial Infarction 1.98 % (-0.25 %, 4.26 %) 2.67 Diabetes 1.49 % (-0.06 %, 3.07 %) 2.00 Congestive Heart Failure 1.28 % (-0.06 %, 2.64 %) 1.72 COPD 0.58 % (-0.82 %, 2.00 %) 0.78 Conduction Disorders 0.64 % (-0.61 %, 1.90 %) 0.85 None of the above conditions 0.74 % (-0.29 %, 1.79 %) Reference

22 Case Only Analysis Suppose diabetics are more susceptible to the acute effects of air pollution on mortality risk Then if I only look at people who died I would expect a larger fraction of the deaths on high pollution days are diabetics So we don t model death (they are all dead) We model diabetes (yes/no) as a function of air pollution on the day of death Significant association susceptibility factor

23 Why Bother? Other Stuff (e.g. Seasonal Model, Weather Model, etc) Cancels Out Don t have to Model them Don t have to argue about them Focus on cases and get effect modification data for them Somewhat more power

24 Relative Odds [95% CI] of a Death having a specified condition on very hot or very cold days in Detroit: Condition Hot Hot3 Cold Cold3 Old 1.02 [0.89, 1.16] 0.97 [0.89, 1.06] Nonwhite 1.22 [1.09, 1.37] Female 0.94 [0.84, 1.05] Diabetes 1.17 [1.04, 1.32] MI 0.94 [0.78, 1.13] COPD [1.00, 1.26] Pneumonia 1.07 [0.95, 1.20] CHF 1.01 [0.91, 1.13] 1.18 [1.09, 1.27] 0.99 [0.92, 1.06] 1.09 [1.01, 1.18] 0.90 [0.79, 1.02] 0.99 [0.92, 1.07] 1.04 [0.99, 1.12] 1.00 [0.93, 1.07] 1.16 [1.03, 1.31] 1.25 [1.12, 1.40] 1.14 [1.02, 1.26] 0.98 [0.87, 1.10] 0.83 [0.69, 0.99] 1.19 [1.07, 1.33] 1.03 [0.92, 1.16] 1.00 [0.90, 1.11] 1.11 [1.00, 1.24] 1.23 [1.12, 1.35] 1.08 [0.99, 1.19] 1.04 [0.94, 1.15] 0.87 [0.75, 1.02] 1.16 [1.06, 1.28] 1.06 [0.96, 1.17] [0.91, 1.09]

25 Kernel Machine Regression Large number of SNPs or Methylation probes Large number of exposures (Exposome) How do we analyze such larger numbers of things Basic Idea of Kernel Machine Ignore small errors in fitting model Include nonlinearities and interactions Shrink coefficients so most of them are near zero

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27 Key Point Data Reduction! You can start with a large number of X s and their interactions. The approach shrinks the weights given the different X s and all possible interactions. Uses Ridge or Lasso penalty Gives you a global test of significance Can be used to identify important exposures, exposure times, susceptibility factors, etc.

28 Shrinkage We still have way to many variables, how do we manage that? Ridge Regression