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26 April 2024 |
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Article overview
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Using the Prognostic Score to Reduce Heterogeneity in Observational Studies | | Rachael C. Aikens
; Dylan Greaves
; Michael Baiocchi
; | | Date: |
24 Aug 2019 | | Abstract: | In large sample observational studies, the control population often greatly
outnumbers the treatment population. Typical practice is to match several
control observations to a single treated observation, with the goal of reducing
variability of the resulting treatment effect estimate. However, increasing the
control to treated ratio yields diminishing returns in terms of variance
reduction and in practice leads to poorer quality matches. In line with
Rosenbaum’s argument on the importance of reducing heterogeneity to strengthen
causal inference against unobserved bias, we suggest first expending some of
the controls to fit a prognostic model, then matching with the resulting
prognostic score to produce matched sets with lower heterogeneity. We propose
methodological alternatives for fitting the prognostic model that help avoid
concerns of overfitting and extrapolation, then we demonstrate in a simulation
setting how this alternative use of the control observations can lead to gains
in terms of both treatment effect estimation and design sensitivity. | | Source: | arXiv, 1908.9077 | | Services: | Forum | Review | PDF | Favorites | |
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