Our goal is to improve the estimation of the average treatment effect among treated (ATT) from longitudinal data. When the ATT is estimated at one time point (or separately at each), outcome-regression (OR), inverse probability weighting and doubly robust estimators can be used. These methods involve estimating the relationships that the covariates have with the outcome and/or propensity score, in different regression models. Assuming these relationships do not vary drastically between close-by time points, we can improve estimation by also using information from neighboring points. We use local regression to smooth the coefficient estimates in the outcome- and propensity score-model over time. Our simulation study shows that when the true coefficients are constant over time, the performance of all estimators is improved by smoothing. Especially in terms of precision, the improvement is greater the more the coefficient estimates are smoothed. We also evaluate the OR-estimator in more complex scenarios where the true regression coefficients vary linearly and non-linearly over time. Here we find that larger degrees of smoothing have a negative effect on the estimators’ accuracy, but continue to improve their precision. This is especially prominent in the non-linear scenario.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-149428 |
| Date | January 2018 |
| Creators | Ecker, Kreske |
| Publisher | Umeå universitet, Statistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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