Indiana University-Purdue University Indianapolis (IUPUI) / Observational data are frequently used for causal inference of treatment effects
on prespecified outcomes. Several widely used causal inference methods have adopted
the method of inverse propensity score weighting (IPW) to alleviate the in
uence of
confounding. However, the IPW-type methods, including the doubly robust methods,
are prone to large variation in the estimation of causal e ects due to possible extreme
weights. In this research, we developed an ordinary least-squares (OLS)-based causal
inference method, which does not involve the inverse weighting of the individual
propensity scores.
We first considered the scenario of homogeneous treatment effect. We proposed
a two-stage estimation procedure, which leads to a model-free estimator of
average treatment effect (ATE). At the first stage, two summary scores, the propensity
and mean scores, are estimated nonparametrically using regression splines. The
targeted ATE is obtained as a plug-in estimator that has a closed form expression.
Our simulation studies showed that this model-free estimator of ATE is consistent,
asymptotically normal and has superior operational characteristics in comparison to
the widely used IPW-type methods. We then extended our method to the scenario
of heterogeneous treatment effects, by adding in an additional stage of modeling
the covariate-specific treatment effect function nonparametrically while maintaining
the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE
and thus can be utilized to study the treatment effect heterogeneity.
We discussed ways of using advanced machine learning techniques in the proposed
method to accommodate high dimensional covariates. We applied the proposed
method to a case study evaluating the effect of early combination of biologic &
non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up
treatment plan in children with newly onset of juvenile idiopathic arthritis disease
(JIA). The proposed method gives strong evidence of significant effect of early combination
at 0:05 level. On average early aggressive use of biologic DMARDs leads to
around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month
than the step-up plan for treating JIA.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/20225 |
| Date | 07 1900 |
| Creators | Xu, Yuanfang |
| Contributors | Zhang, Ying, Huang, Bin, Tu, Wanzhu, Bakoyannis, Giorgos, Song, Yiqing |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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