In medical and public health research, many studies are observational. In these studies, the treatment is not randomly assigned to participants. Therefore, the differences in outcomes between treatment groups could be due to imbalances of characteristics that are related to the outcome of interest prior to the treatment. Herein we investigate how we can use propensity scores, the conditional probability of receiving treatment given the observed information, to make valid causal inference in observational studies. Theoretical results for the bias are given for linear response models that use the propensity score as a linear covariate. The bias depends on the relationship between the propensity score, the treatment indicator and the functional form of the covariate. Various methods for estimation of the treatment effect are explored. We show that the bias is influenced by the overlap in the distributions and functional forms of the covariates. When the distributions of the covariates have substantial overlap between treated and control groups, matching does well in terms of bias.
In the second half of our work, we continue to investigate propensity score methods for causal inference in observational studies, however our focus turns to studies with multiple groups. These methods are motivated by an example from the Prematurity Prevention Clinic at The Ohio State University. Our innovation relies on matching triplets of patients, which includes one patient from each of our groups of interest (those treated on-time, those with delayed treatment, and those who never were treated with 17P). Within each of these triplets, we attempt to balance pre-treatment characteristics by two matching techniques. We investigate two matching algorithms via simulation. These simulation studies found that a sub-optimal matching approach will in most circumstances provide better overall matches, than a nearest-neighbor approach. We implement our sub-optimal triplet matching for our motivating data and provide some conclusions about these data and future work with these methods.
|Commitee:||Hong, Zhu, Jason, Hsu|
|School:||The Ohio State University|
|School Location:||United States -- Ohio|
|Source:||DAI-B 78/11(E), Dissertation Abstracts International|
|Subjects:||Biostatistics, Public health|
|Keywords:||Matching, Multiple group, Observational study, Propensity score|
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