6 papers
Efficient Bayesian Inference in the Cox Model via Rank-Ordered Likelihood
Tomohiro Ohigashi, Shunichiro Orihara, Shonosuke Sugasawa
In Bayesian inference for the Cox proportional hazards model, modeling the baseline hazard function is challenging. Recently, direct Bayesian inference using the partial likelihood…
On the Conservativeness of Robust Variance Estimators in Propensity Score Weighted Cox Models
Hiroya Morita, Shunichiro Orihara, Fumitaka Shimizu +1
In propensity score weighted analysis, robust variance that does not account for weight estimation is commonly used. In propensity score weighted Cox models (CoxPSW), the robust va…
Sample size re-estimation in blinded hybrid-control design using inverse probability weighting
Masahiro Kojima, Shunichiro Orihara, Keisuke Hanada +1
With the increasing availability of data from historical studies and real-world data sources, hybrid control designs that incorporate external data into the evaluation of current s…
Bayesian Doubly Robust Causal Inference via Posterior Coupling
Shunichiro Orihara, Tomotaka Momozaki, Shonosuke Sugasawa
Bayesian doubly robust (DR) causal inference faces a fundamental dilemma: joint modeling of outcome and propensity score suffers from the feedback problem where outcome information…
Robust Estimation and Model Selection for the Controlled Directed Effect with Unmeasured Mediator-Outcome Confounders
Shunichiro Orihara, Shinpei Imori, Kosuke Morikawa +2
Controlled Direct Effect (CDE) is one of the causal estimands used to evaluate both exposure and mediation effects on an outcome. When there are unmeasured confounders existing bet…
Bayesian-based Propensity Score Subclassification Estimator
Shunichiro Orihara, Tomotaka Momozaki
Subclassification estimators are one of the methods used to estimate causal effects of interest using the propensity score. This method is more stable compared to other weighting m…