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>.
| Version: |
1.0.2 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
reshape2, pracma |
| Suggests: |
testthat (≥ 3.0.0), cubature (≥ 2.0.4), MASS (≥ 7.3-55) |
| Published: |
2023-08-28 |
| DOI: |
10.32614/CRAN.package.adace |
| 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 at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
adace results |
Documentation:
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