Estimating causal effect of multi-valued treatment from observational survival dataEstimating causal effect of multi-valued treatment from observational survival data
- Other Titles
- Estimating causal effect of multi-valued treatment from observational survival data
- Authors
- 김봉성; 김지현
- Issue Date
- Nov-2020
- Publisher
- 한국통계학회
- Keywords
- generalized propensity score; inverse probability weighting; Cox' s proportional hazards model; marginal hazard ratio
- Citation
- Communications for Statistical Applications and Methods, v.27, no.6, pp.675 - 688
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 27
- Number
- 6
- Start Page
- 675
- End Page
- 688
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/40048
- DOI
- 10.29220/CSAM.2020.27.6.675
- ISSN
- 2287-7843
- Abstract
- In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment.
In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.
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