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An empirical comparison of tree-based methods for propensity score estimation.

ABSTRACT: To illustrate the use of ensemble tree-based methods (random forest classification [RFC] and bagging) for propensity score estimation and to compare these methods with logistic regression, in the context of evaluating the effect of physical and occupational therapy on preschool motor ability among very low birth weight (VLBW) children.We used secondary data from the Early Childhood Longitudinal Study Birth Cohort (ECLS-B) between 2001 and 2006.We estimated the predicted probability of treatment using tree-based methods and logistic regression (LR). We then modeled the exposure-outcome relation using weighted LR models while considering covariate balance and precision for each propensity score estimation method.Among approximately 500 VLBW children, therapy receipt was associated with moderately improved preschool motor ability. Overall, ensemble methods produced the best covariate balance (Mean Squared Difference: 0.03-0.07) and the most precise effect estimates compared to LR (Mean Squared Difference: 0.11). The overall magnitude of the effect estimates was similar between RFC and LR estimation methods.Propensity score estimation using RFC and bagging produced better covariate balance with increased precision compared to LR. Ensemble methods are a useful alterative to logistic regression to control confounding in observational studies.

SUBMITTER: Watkins S

PROVIDER: S-EPMC3796115 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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An empirical comparison of tree-based methods for propensity score estimation.

Watkins Stephanie S Jonsson-Funk Michele M Brookhart M Alan MA Rosenberg Steven A SA O'Shea T Michael TM Daniels Julie J

Health services research 20130523 5

<h4>Objective</h4>To illustrate the use of ensemble tree-based methods (random forest classification [RFC] and bagging) for propensity score estimation and to compare these methods with logistic regression, in the context of evaluating the effect of physical and occupational therapy on preschool motor ability among very low birth weight (VLBW) children.<h4>Data source</h4>We used secondary data from the Early Childhood Longitudinal Study Birth Cohort (ECLS-B) between 2001 and 2006.<h4>Study desi ...[more]

PMID: 23701015

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Project description:Classical methods used for signal detection in pharmacovigilance rely on disproportionality analysis of counts aggregating spontaneous reports of a given adverse drug reaction. In recent years, alternative methods have been proposed to analyze individual spontaneous reports such as penalized multiple logistic regression approaches. These approaches address some well-known biases resulting from disproportionality methods. However, while penalization accounts for computational constraints due to high-dimensional data, it raises the issue of determining the regularization parameter and eventually that of an error-controlling decision rule. We present a new automated signal detection strategy for pharmacovigilance systems, based on propensity scores (PS) in high dimension. PSs are increasingly used to assess a given association with high-dimensional observational healthcare databases in accounting for confusion bias. Our main aim was to develop a method having the same advantages as multiple regression approaches in dealing with bias, while relying on the statistical multiple comparison framework as regards decision thresholds, by considering false discovery rate (FDR)-based decision rules. We investigate four PS estimation methods in high dimension: a gradient tree boosting (GTB) algorithm from machine-learning and three variable selection algorithms. For each (drug, adverse event) pair, the PS is then applied as adjustment covariate or by using two kinds of weighting: inverse proportional treatment weighting and matching weights. The different versions of the new approach were compared to a univariate approach, which is a disproportionality method, and to two penalized multiple logistic regression approaches, directly applied on spontaneous reporting data. Performance was assessed through an empirical comparative study conducted on a reference signal set in the French national pharmacovigilance database (2000-2016) that was recently proposed for drug-induced liver injury. Multiple regression approaches performed better in detecting true positives and false positives. Nonetheless, the performances of the PS-based methods using matching weights was very similar to that of multiple regression and better than with the univariate approach. In addition to being able to control FDR statistical errors, the proposed PS-based strategy is an interesting alternative to multiple regression approaches.

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An empirical comparison of tree-based methods for propensity score estimation.

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An empirical comparison of tree-based methods for propensity score estimation.

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