The University of Tokyo · 의학
이 교수의 연구실은 임상 연구에서의 생존 분석과 질병 관련 삶의 질(HRQOL) 평가를 중심으로, 특히 암 치료에서의 효과적이고 신뢰할 수 있는 결과 측정 방법을 개발하고 있습니다. 주요 연구 방향은 EORTC QLQ-C30나 FACT-G와 같은 환자 중심 평가 도구를 기반으로 한 EQ-5D-5L 인덱스 추정 알고리즘 개발, 그리고 후속 치료의 영향을 고려한 인과적 분석 기법의 개선에 초점이 맞춰져 있습니다. 특히, 위험차이 추정, 수정 푸아송 회귀, 모의 실험 기반의 유의미한 표본 크기 기준 설정 등 통계적 방법론의 정교화를 통해 임상 시험의 해석 타당성을 높이는 데 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The developed mapping algorithms can be used to generate the EQ-5D-5L index from EORTC QLQ-C30 or FACT-G in cost-effectiveness analyses, whose predictive performance would be similar to or better than those of previous algorithms.
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GS achieved better HRQOL than gemcitabine alone, resulting a good balance between overall survival and HRQOL benefits. S-1 alone provides HRQOL similar to that provided by gemcitabine alone. Preventing fatigue and anorexia and maintaining better response would improve HRQOL.
Risk difference is a relevant effect measure in epidemiologic research. Although it is well known that when there are few events per confounder, logistic regression is not suitable for confounding control, it is not clear how many events per confounder are required for valid estimation of risk difference using linear binomial models. Because the maximum likelihood method has a convergence problem, we investigated the number of events per confounder necessary to validly estimate risk difference u
Modified Poisson regression, which estimates the regression parameters in the log-binomial regression model using the Poisson quasi-likelihood estimating equation and robust variance, is a useful tool for estimating the adjusted risk and prevalence ratio in binary outcome analysis. Although several goodness-of-fit tests have been developed for other binary regressions, few goodness-of-fit tests are available for modified Poisson regression. In this study, we proposed several goodness-of-fit test
Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention-to-treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per-protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how
Weight change since age 20 is a significant risk factor for MASLD development in non-obese populations, but its impact varies widely among individuals. Men and individuals with abdominal obesity, dyslipidemia, hyperuricemia, and high ALT levels are particularly susceptible to the effects of weight gain.
No recommendation or guidance is available for the sample size to develop and select a mapping algorithm from a health-related quality-of-life measure onto the score of a preference-based measure.This research proposes using a framework for calculating the sample size for clinical prediction models in sample size consideration for mapping algorithms using linear regression.A survey showed that the information required to calculate the sample size could be successfully extracted from previous map
