A Comparison of Matching and Weighting Methods for Causal Inference Based on Routine Health Insurance Data, or What to do If an RCT is Impossible

TY - JOUR

T1 - A Comparison of Matching and Weighting Methods for Causal Inference Based on Routine Health Insurance Data, or What to do If an RCT is Impossible

AU - Matschinger, Herbert

AU - Heider, Dirk

AU - König, Hans-Helmut

N1 - Eigentümer und Copyright ©Georg Thieme Verlag KG 2019.

PY - 2020/3

Y1 - 2020/3

N2 - Due to a multitude of reasons Randomized Control Trials on the basis of so-called "routine data" provided by insurance companies cannot be conducted. Therefore the estimation of "causal effects" for any kind of treatment is hampered since systematic bias due to specific selection processes must be suspected. The basic problem of counterfactual, which is to evaluate the difference between two potential outcomes for the same unit, is discussed. The focus lies on the comparison of the performance of different approaches to control for systematic differences between treatment and control group. These strategies are all based on propensity scores, namely matching or pruning, IPTW (inverse probability treatment weighting) and entropy balancing. Methods to evaluate these strategies are presented. A logit model is employed with 87 predictors to estimate the propensity score or to estimate the entropy balancing weights. All analyses are restricted to estimate the ATT (Average Treatment Effect for the Treated) Exemplary data come from a prospective controlled intervention-study with two measurement occasions. Data contain 35 857 chronically ill insurants with diabetes, congestive heart failure, arteriosclerosis, coronary heart disease or hypertension of one German sickness fund. The intervention group was offered an individual telephone coaching to improve health behavior and slow down disease progression while the control group received treatment as usual. Randomization took place before the insurants' consent to participate was obtained so assumptions of an RCT are violated. A weighted mixture model (difference-in-difference) as the causal model of interest is employed to estimate treatment effects in terms of costs distinguishing the categories outpatient costs, medication costs, and total costs. It is shown that entropy balancing performs best with respect to balancing treatment and control group at baseline for the first three moments of all 87 predictors. This will result in least biased estimates of the treatment effect.

AB - Due to a multitude of reasons Randomized Control Trials on the basis of so-called "routine data" provided by insurance companies cannot be conducted. Therefore the estimation of "causal effects" for any kind of treatment is hampered since systematic bias due to specific selection processes must be suspected. The basic problem of counterfactual, which is to evaluate the difference between two potential outcomes for the same unit, is discussed. The focus lies on the comparison of the performance of different approaches to control for systematic differences between treatment and control group. These strategies are all based on propensity scores, namely matching or pruning, IPTW (inverse probability treatment weighting) and entropy balancing. Methods to evaluate these strategies are presented. A logit model is employed with 87 predictors to estimate the propensity score or to estimate the entropy balancing weights. All analyses are restricted to estimate the ATT (Average Treatment Effect for the Treated) Exemplary data come from a prospective controlled intervention-study with two measurement occasions. Data contain 35 857 chronically ill insurants with diabetes, congestive heart failure, arteriosclerosis, coronary heart disease or hypertension of one German sickness fund. The intervention group was offered an individual telephone coaching to improve health behavior and slow down disease progression while the control group received treatment as usual. Randomization took place before the insurants' consent to participate was obtained so assumptions of an RCT are violated. A weighted mixture model (difference-in-difference) as the causal model of interest is employed to estimate treatment effects in terms of costs distinguishing the categories outpatient costs, medication costs, and total costs. It is shown that entropy balancing performs best with respect to balancing treatment and control group at baseline for the first three moments of all 87 predictors. This will result in least biased estimates of the treatment effect.

U2 - 10.1055/a-1009-6634

DO - 10.1055/a-1009-6634

M3 - SCORING: Journal article

C2 - 32066197

VL - 82

SP - S139-S150

JO - GESUNDHEITSWESEN

JF - GESUNDHEITSWESEN

SN - 0941-3790

IS - S 02

ER -