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adace: Estimator of the Adherer Average Causal Effect
Estimate the causal treatment effect for subjects that can adhere to one or both of the treatments. Given longitudinal data with missing observations, consistent causal effects are calculated. Unobserved potential outcomes are estimated through direct integration as described in: Qu et al., (2019) <doi:10.1080/19466315.2019.1700157> and Zhang et. al., (2021) <doi:10.1080/19466315.2021.1891965>.
Getting started
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Package details |
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| Author | Jiaxun Chen [aut], Rui Jin [aut], Yongming Qu [aut], Run Zhuang [aut, cre], Ying Zhang [aut], Eli Lilly and Company [cph] |
| Maintainer | Run Zhuang <capecod0321@gmail.com> |
| License | GPL (>= 3) |
| Version | 1.0.2 |
| Package repository | View on CRAN |
| Installation |
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