A causal approach to analysis of censored medical costs in the presence of time-varying treatment

A causal approach to analysis of censored medical costs in the presence of time-varying treatment. Spieker, A. J., Oganisian, A., Ko, E. M., Roy, J. A., & Mitra, N. arXiv:1705.08742 [stat], May, 2017.

Paper abstract bibtex

There has recently been a growing interest in the development of statistical methods to compare medical costs between treatment groups. When cumulative cost is the outcome of interest, right-censoring poses the challenge of informative missingness due to heterogeneity in the rates of cost accumulation across subjects. Existing approaches seeking to address the challenge of informative cost trajectories typically rely on inverse probability weighting and target a net “intent-to-treat” eﬀect. However, no approaches capable of handling time-dependent treatment and confounding in this setting have been developed to date. A method to estimate the joint causal eﬀect of a treatment regime on cost would be of value to inform public policy when comparing interventions. In this paper, we develop a nested g-computation approach to cost analysis in order to accommodate time-dependent treatment and repeated outcome measures. We demonstrate that our procedure is reasonably robust to departures from its distributional assumptions and can provide unique insights into fundamental diﬀerences in average cost across time-dependent treatment regimes.

@article{spieker_causal_2017-1,
	title = {A causal approach to analysis of censored medical costs in the presence of time-varying treatment},
	url = {http://arxiv.org/abs/1705.08742},
	abstract = {There has recently been a growing interest in the development of statistical methods to compare medical costs between treatment groups. When cumulative cost is the outcome of interest, right-censoring poses the challenge of informative missingness due to heterogeneity in the rates of cost accumulation across subjects. Existing approaches seeking to address the challenge of informative cost trajectories typically rely on inverse probability weighting and target a net “intent-to-treat” eﬀect. However, no approaches capable of handling time-dependent treatment and confounding in this setting have been developed to date. A method to estimate the joint causal eﬀect of a treatment regime on cost would be of value to inform public policy when comparing interventions. In this paper, we develop a nested g-computation approach to cost analysis in order to accommodate time-dependent treatment and repeated outcome measures. We demonstrate that our procedure is reasonably robust to departures from its distributional assumptions and can provide unique insights into fundamental diﬀerences in average cost across time-dependent treatment regimes.},
	language = {en},
	urldate = {2020-10-23},
	journal = {arXiv:1705.08742 [stat]},
	author = {Spieker, Andrew J. and Oganisian, Arman and Ko, Emily M. and Roy, Jason A. and Mitra, Nandita},
	month = may,
	year = {2017},
	keywords = {Statistics - Methodology},
	file = {Spieker et al. - 2017 - A causal approach to analysis of censored medical .pdf:/Users/neil.hawkins/Zotero/storage/P2Y8VMY4/Spieker et al. - 2017 - A causal approach to analysis of censored medical .pdf:application/pdf},
}

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