activity
20202026
most citedAn interpretable neural network-based non-proportional odds model for ordinal regression

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing stat.MEShow all

5 papers · 1 filter

stat.ME2026

Prognosis-equivalent mapping of clinical measurements via survival analysis

Kazuharu Harada, Mitsunori Ogawa

A continuous clinical measurement recorded in fixed physical units may have different prognostic meaning across patients when its effect depends on a patient-level modifier. For ex…

stat.ME2025

Efficient estimation of weighted treatment effects under two-phase sampling

Kazuharu Harada, Masataka Taguri

Two-phase sampling offers a practical way to collect costly confounders only in a subsample while retaining inexpensive information for a larger cohort. In observational causal stu…

stat.ME2023

Simultaneous Modeling of Disease Screening and Severity Prediction: A Multi-task and Sparse Regularization Approach

Kazuharu Harada, Shuichi Kawano, Masataka Taguri

Identifying clinically relevant biomarkers and developing predictive models are central challenges in biomedical research. Biomarkers are commonly used for disease screening, and s…

stat.ME2023★ 1 cited

An interpretable neural network-based non-proportional odds model for ordinal regression

Akifumi Okuno, Kazuharu Harada

This study proposes an interpretable neural network-based non-proportional odds model (NPOM) for ordinal regression. NPOM is different from conventional approaches to ordin…

stat.ME2021

Outlier-Resistant Estimators for Average Treatment Effect in Causal Inference

Kazuharu Harada, Hironori Fujisawa

The inverse probability (IPW) and doubly robust (DR) estimators are often used to estimate the average causal effect (ATE), but are vulnerable to outliers. The IPW/DR median can be…