The University of Tokyo · Medicine
Professor Yasuhiro Hagiwara's research lab specializes in health-related quality of life (HRQoL) measurement and statistical methodology in oncology and epidemiology. The lab focuses on developing and validating statistical models—such as mapping algorithms, modified Poisson regression, and risk difference estimation—for improving the analysis of clinical trial data and health outcomes. Key research directions include enhancing the validity of causal inference in trials with subsequent treatments, optimizing HRQoL assessment in cancer care, and addressing methodological challenges in binary outcome regression with small event counts. The lab's work bridges clinical oncology, biostatistics, and health economics to support evidence-based decision-making in healthcare.
Figures are computed from collected data and may differ slightly.
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
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