15 papers
Estimating the average treatment effect under limited overlap via Polynomial Approximation and Extrapolation
Shunichiro Orihara, Sho Komukai, Fan Li
Estimating the average treatment effect (ATE) remains a fundamental challenge in observational studies in the presence of poor or limited covariate overlap. Although the inverse pr…
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…
General Bayesian inference for causal effects using covariate balancing procedure
Shunichiro Orihara, Tomotaka Momozaki, Tomoyuki Nakagawa
In observational studies, the propensity score plays a central role in estimating causal effects of interest. The inverse probability weighting (IPW) estimator is commonly used for…
Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models
Shunichiro Orihara, Shonosuke Sugasawa, Tomohiro Ohigashi +2
In observational studies, unmeasured confounders present a crucial challenge in accurately estimating desired causal effects. To calculate the hazard ratio (HR) in Cox proportional…
Evaluating the Conservativeness of Robust Sandwich Variance Estimator in Weighted Average Treatment Effects
Shunichiro Orihara
In causal inference, the Inverse Probability Weighting (IPW) estimator is commonly used to estimate causal effects for estimands within the class of Weighted Average Treatment Effe…
Frailty Model with Change Point for Survival Analysis
Masahiro Kojima, Shunichiro Orihara
We propose a novel frailty model with change points applying random effects to a Cox proportional hazard model to adjust the heterogeneity between clusters. Because the frailty mod…
Likelihood-based Instrumental Variable Methods for Cox Proportional Hazard Models
Shunichiro Orihara
In biometrics and related fields, the Cox proportional hazards model are widely used to analyze with covariate adjustment. However, when some covariates are not observed, an unbias…
Robust Estimating Method for Propensity Score Models and its Application to Some Causal Estimands: A review and proposal
Shunichiro Orihara
In observational study, the propensity score has the central role to estimate causal effects. Since the propensity score is usually unknown, estimating by appropriate procedures is…
Limited-Information Maximum Likelihood based Model Selection Procedures for Binary Outcomes
Shunichiro Orihara
Unmeasured covariates constitute one of the important problems in causal inference. Even if there are some unmeasured covariates, some instrumental variable methods such as a two-s…
Valid Instrumental Variables Selection Methods using Negative Control Outcomes and Constructing Efficient Estimator
Shunichiro Orihara
In observational studies, instrumental variable (IV) methods are commonly applied when there exists some unmeasured covariates. In Mendelian Randomization (MR), constructing an all…