著者

五所 正彦 丸尾 和司

出版者

応用統計学会

雑誌

応用統計学 (ISSN:02850370)

巻号頁・発行日

vol.46, no.2, pp.53-65, 2017 (Released:2017-12-27)

参考文献数

46

被引用文献数

1

---

データの欠測は,臨床試験の結果をゆがめ,解釈を困難にする重大な問題である.mixed-effects models for repeated measures(MMRM)は,線形混合効果モデルの一種で,不完全な経時測定データを解析するために利用される統計モデルである.特に医生物学の分野で急速に普及しており,臨床試験においては主要な解析に採用されることも多い.本論文では,MMRMに基づく解析を取り上げ,この方法の原理や性質,固定効果パラメータの推定ならびに統計的推測の方法を概観する.また,実際のデータにMMRMを適用する際の具体的な指定方法や注意点を紹介する.

著者

高橋 健一 石井 亮太 丸尾 和司 五所 正彦

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.43, no.1, pp.37-62, 2022 (Released:2023-03-24)

参考文献数

72

---

The seamless phase II/III design combining phases II and III into a single trial has been shown growing interest for improving the efficiency of drug development, becoming the most frequent adaptive design type. It typically consists of two stages, the trial objectives being often different in each stage. The primary objectives are to select optimal experimental treatment group(s) in the first stage and compare the efficacy between the selected treatment and control groups in the second stage. In the final analysis, appropriate statistical methods should be applied to avoid increasing the type I error rate and selection bias. This paper reviews several statistical methods that can be applied to adaptive seamless phase II/III designs. Especially, the most representative methods such as the group sequential design approach, combination test approach, and conditional error function approach, are organized in a unified framework and the characteristics of the methods are compared. As related topics, statistical methods for the case where endpoints are different between stages 1 and 2, and the method of sample size calculation will also be discussed. Examples of actual applications are also presented.

著者

Takebayashi Takashi Takahashi Kayoko Amano Satoru Uchiyama Yuki Gosho Masahiko Domen Kazuhisa Hachisuka Kenji 五所 正彦

出版者

Frontiers

雑誌

Frontiers in Neurology (ISSN:16642295)

巻号頁・発行日

no.9, 2018-08

---

Background: Stroke patients experience chronic hemiparesis in their upper extremities leaving negative effects on quality of life. Robotic therapy is one method to recover arm function, but its research is still in its infancy. Research questions of this study is to investigate how to maximize the benefit of robotic therapy using ReoGo-J for arm hemiplegia in chronic stroke patients.Methods: Design of this study is a multi-center parallel group trial following the prospective, randomized, open-label, blinded endpoint (PROBE) study model. Participants and setting will be 120 chronic stroke patients (over 6 months post-stroke) will be randomly allocated to three different rehabilitation protocols. In this study, the control group will receive 20 min of standard rehabilitation (conventional occupational therapy) and 40 min of self-training (i.e., sanding, placing and stretching). The robotic therapy group will receive 20 min of standard rehabilitation and 40 min of robotic therapy using ReoGo®-J device. The combined therapy group will receive 40 min of robotic therapy and 20 min of constraint-induced movement therapy (protocol to improve upper-limb use in ADL suggests). This study employs the Fugl-Meyer Assessment upper-limb score (primary outcome), other arm function measures and the Stroke Impact Scale score will be measured at baseline, 5 and 10 weeks of the treatment phase. In analysis of this study, we use the mixed effects model for repeated measures to compare changes in outcomes between groups at 5 and 10 Weeks. The registration number of this study is UMIN000022509.Conclusions: This study is a feasible, multi-site randomized controlled trial to examine our hypothesis that combined training protocol could maximize the benefit of robotic therapy and best effective therapeutic strategy for patients with upper-limb hemiparesis.