17 0 0 0 OA 反事実モデルに基づく直接効果と間接効果の推定
- 著者
- 矢田 真城 魚住 龍史 田栗 正隆
- 出版者
- 日本計量生物学会
- 雑誌
- 計量生物学 (ISSN:09184430)
- 巻号頁・発行日
- vol.40, no.2, pp.81-116, 2020-06-01 (Released:2020-07-21)
- 参考文献数
- 63
When a causal effect between treatment and outcome variables is observed, effects on the outcome are of interest to investigate the mechanisms among the outcome and treatment. Indirect effect is defined as the causal effect of the treatment on the outcome via the mediator. Direct effect is defined as the causal effect of the treatment on the outcome that is not through the mediator. In this paper, we discuss the estimation of direct and indirect effects based on the framework of potential response models focusing on the 4-way decomposition. Direct and indirect effect estimations are illustrated with two examples where the outcome, mediator, covariate variables are continuous and categorical data. Moreover, we discuss the estimation of clausal effects and the effect decomposition in the settings that include confounder of mediator and outcome affected by treatment, multiple mediators, or time-varying treatment in the presence of time-dependent confounder.
10 0 0 0 OA 臨床試験におけるヒストリカルコントロールデータの利用
- 著者
- 武田 健太朗 大庭 真梨 柿爪 智行 坂巻 顕太郎 田栗 正隆 森田 智視
- 出版者
- 日本計量生物学会
- 雑誌
- 計量生物学 (ISSN:09184430)
- 巻号頁・発行日
- vol.36, no.1, pp.25-50, 2015-07-20 (Released:2015-09-08)
- 参考文献数
- 47
- 被引用文献数
- 6 6
It is expected to develop new drug more efficiently by incorporating historical data into the current study data. Borrowing historical data which is sufficiently similar to the current data allows increasing power and improving the accuracy of the estimated treatment effect. On the other hand, if the historical data is not similar to the current data, there is a potential for bias and inflated type I error rate. Power prior and hierarchical model are widely known as the Bayesian approaches with borrowing strength from historical information. They have the advantage of deciding the amount of historical information continuously depending on the similarity between historical data and current data. Our goal is to introduce power prior and hierarchical model while showing some examples, and provide a review of points to keep in mind when these approaches are used in the clinical trials.
1 1 1 0 OA 疫学分野での計量生物学の発展
- 著者
- 田栗 正隆 高橋 邦彦 小向 翔 伊藤 ゆり 服部 聡 船渡川 伊久子 篠崎 智大 山本 倫生 林 賢一
- 出版者
- 日本計量生物学会
- 雑誌
- 計量生物学 (ISSN:09184430)
- 巻号頁・発行日
- vol.44, no.2, pp.129-200, 2024 (Released:2024-04-25)
- 参考文献数
- 167
Epidemiology is the study of health-related states or events in specific populations and their determinants, with the aim of controlling health problems. It encompasses various research fields, such as cancer epidemiology, infectious disease epidemiology, and social epidemiology, molecular epidemiology, environmental epidemiology, genetic epidemiology, clinical epidemiology, pharmacoepidemiology, spatial epidemiology, and theoretical epidemiology, among others, and is closely related to statistics and biometrics. In analytical epidemiological studies, data is collected from study populations using appropriate study designs, and statistical methods are applied to understand disease occurrence and its causes, particularly establishing causal relationships between interventions or exposures and disease outcomes. This paper focuses on five topics in epidemiology, including infectious disease control through spatial epidemiology, cancer epidemiology using cancer registry data, research about long-term health effects, targeted learning in observational studies, and that in randomized controlled trials. This paper provides the latest insights from experts in each field and offers a prospect for the future development of quantitative methods in epidemiology.