Report

Comparing high-dimensional propensity score versus lasso variable selection for confounding adjustment in a novel simulation framework Jessica Franklin Instructor in Medicine Division of Pharmacoepidemiology & Pharmacoeconomics Brigham and Women’s Hospital and Harvard Medical School QMC, Department of Quantitative Health Sciences University of Massachusetts Medical School April 15, 2014 Background • Administrative healthcare claims data are a popular data source for nonrandomized studies of interventions. • Because treatments are not randomized, addressing confounding is the primary methodological challenge. Claims Data • Comprehensive claims databases contain information on patient insurance enrollment and demographics, as well as every healthcare encounter, including: • • • • Diagnoses Procedures Hospitalizations Medications dispensed • Dates of encounters provide a complete longitudinal record of patients’ healthcare interactions. New user design • Potential confounders are measured prior to initiation of exposure. • Active treatment comparator group reduces biases associated with non-user comparators. Principles of variable selection • Brookhart et al. (2006) showed that the best PS model is the model that includes all predictors of outcome (regardless of whether they are associated with exposure). • Pearl (2010) and Myers et al. (2011) further noted that including instrumental varaibles (IVs) can increase bias from unmeasured confounding. • IVs are associated with exposure, but not associated with outcome except through exposure. hd-PS variable selection • The high-dimensional propensity score (hd-PS) algorithm screens thousands of diagnoses, medications, and procedure codes and ranks variables according to likelihood of confounding. • Relies on the idea that a large number of “proxy” variables can reduce bias from unmeasured confounding. • Empirical evidence has shown a reduction in bias. Shrinkage methods • Greenland (2008) suggested regularization methods as preferable to variable selection. • Shrinking coefficients allows for efficient estimation, even in models with many degrees of freedom. • Lasso regression provides both shrinkage and principled variable selection. • Shrinkage allows for direct modeling of the outcome even with many potential confounders • Some coefficients are shrunk all the way to 0. Objective • To compare the performance of • hd-PS variable selection • Ridge regression of the outcome on all potential confounders • Lasso regression of the outcome on all potential confounders • The goal is maximum reduction in confounding bias. Comparing high-dimensional methods • How can we answer this question? • Empirical studies are useful when we “know” the true treatment effect, but even then we can’t determine the contributions of bias and variance to overall error. • Ordinary simulation techniques with completely synthetic data cannot capture the complex correlation structure among covariates in claims data. Plasmode simulation • We start with a real empirical cohort study: • • • • 49,653 patients Exposed to either ns-NSAIDs or Cox-2 inhibitors (X) Followed for gastrointestinal events (Y) Pre-defined covariates include age, sex, race, and 16 diagnosis/medication/procedure variables (C1) • To get reasonable values for associations between covariates and outcome, we estimated a model with: • Y ~ X + all pre-defined covariates + interactions between age and binary covariates logit{Pr(Y =1)} = fˆ (C1 | bˆ )+ aˆ X Simulation setup • True outcome generation model: logit{Pr(Y =1)} = fˆ (C1 | bˆ )+ 0X • Estimated coefficient values from the observed outcome model • Except for the coefficient on exposure: a = 0. • To create simulated datasets: • Sample with replacement rows from (X, C) • Calculate pi = expit{ fˆ (C1i | bˆ )} for each patient in the sample. • Simulate outcome Yi* ~ Bernoulli(pi ) • We created 500 datasets, each of size 30,000, outcome prevalence set to 5%, exposure prevalence set to 40%. True causal diagram Any variables associated with exposure remain associated with exposure. C1 = True confounders, a subset of C = all measured covariates. Any correlations among covariates and true confounders remain intact. Associations with outcome are determined by chosen simulation model. Outcome generation Variable True OR Age 1.030928413 Black race 0.668385082 Male gender 1.418991333 Congestive heart failure 1.220575229 Coronary disease 1.184633001 Prior bleeding 10.62470195 Prior ulcer 0.777704249 Recent hospitalization 4.537106069 Recent nursing home admission 2.222756726 Warfarin 1.011494072 Gastrointestinal drugs 1.858528101 The mechanics of hd-PS • For each diagnosis, procedure, medication code, hdPS creates 3 potential variables: • Code observed ≥ 1 time during baseline period • Code observed ≥ median number of times • Code observed ≥ 75th percentile number of times • There are 2 potential ranking methods: • Exposure-based: A simple RR association measure between exposure and each variable. • Bias-based: Bross’s bias formula that considers the association of each varaible with exposure and outcome hd-PS Analyses • PSs were constructed using: • • • • The top 500 exposure-ranked variables + demographics The top 500 bias-ranked variables + demographics The top 30 exposure-ranked variables + demographics The top 30 bias-ranked variables + demographics • Logistic regression on exposure + deciles of each PS Shrinkage analyses • Regression of the outcome on all hdPS-screened variables (4800 – those that never occur) + exposure + demographics • Ridge regression • Lasso regression • We apply no shrinkage to the coefficient on exposure. • Calculate the crude estimate for comparison Combination approaches • Using the variables selected by the lasso regression: • Include them in a PS analysis • Include them in an ordinary logistic regression outcome model • Using the 500 variables chosen by bias-based hd-PS: • Include them in an ordinary logistic regression outcome model • Include them in a lasso outcome model • Include them in a ridge outcome model Results – Variable selection • Lasso selected 103 variables on average. • 66% were also selected by at least one hdPS algorithm • IQR: 62-70% • Age was selected in 100% of simulations. • Race was selected in 28%. Results - Bias Results - Bias Crude confounding bias of 0.19. Results - Bias Ridge and lasso regression with all variables reduces bias by 41% and 63%, respectively. Results - Bias Ridge and lasso do better when they start with prescreened variables. Bias is reduced by 70% and 83%, respectively. Results - Bias Ordinary regression and PS approaches performed better. Exposure-based hdPS with 500 variables completely eliminated bias. Results - Bias Bias-based hdPS varaible selection also performed well, with 93% and 91% bias reduction in the PS and ordinary regression models. Results - Bias PS and regular regression models performed well using lasso variable selection as well (95% and 96% bias reduction). Results - Bias When restricting variables to a very small set, bias-based hdPS was much preferred. Conclusion • The variable selection method had relatively little importance. • The estimation method mattered much more. • Shrinkage of coefficient estimates led to insufficient bias control. • Focus on including a large number of potential confounders or confounder proxies. Limitations • There are many “instruments” in current simulation setup. • Variables associated with exposure that are not included in the outcome simulation model are essentially IVs, which is unrealistic. • There is no unmeasured confounding in these data. • Variable selection is an easier task when all important confounders are measured. Future work • Enrich the outcome model • Non-linear associations, more interactions, more true confounders • Vary the true treatment effect • Modify the coefficient on treatment in the outcome generation model. • Vary exposure prevalence • Can be accomplished by sampling within exposure group. • Vary outcome prevalence • Modify the intercept in the outcome generation model. • Unmeasured confounding • Set aside one or more true confounders and don’t allow methods to utilize these variables. • Other base datasets Thanks! • Co-authors: • • • • Wesley Eddings Jeremy A Rassen Robert J Glynn Sebastian Schneeweiss • Contact: • [email protected] • www.drugepi.org/faculty-staff-trainees/faculty/jessicafranklin/