TITE‐gBOIN‐ET: Time‐to‐event generalized Bayesian optimal interval design to accelerate dose‐finding accounting for ordinal graded efficacy and toxicity outcomes

医学 临床试验 置信区间 最大耐受剂量 毒性 贝叶斯概率 肿瘤科 内科学 统计 数学
作者
Kazuhisa Takeda,Yusuke Yamaguchi,Masataka Taguri,Satoshi Morita
出处
期刊:Biometrical Journal [Wiley]
卷期号:65 (7) 被引量:1
标识
DOI:10.1002/bimj.202200265
摘要

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hakuna_matata完成签到 ,获得积分10
1秒前
caiia完成签到,获得积分10
2秒前
桑桑发布了新的文献求助10
2秒前
卿xx完成签到,获得积分10
2秒前
健康的忆寒完成签到,获得积分10
3秒前
3秒前
Hann完成签到,获得积分20
3秒前
huanhuan完成签到,获得积分10
3秒前
3秒前
MoleMed完成签到,获得积分10
3秒前
格物观微发布了新的文献求助10
4秒前
4秒前
大气凝云完成签到,获得积分10
5秒前
丘比特应助qxy采纳,获得10
5秒前
orixero应助MM采纳,获得10
5秒前
liuz完成签到,获得积分10
5秒前
花水木发布了新的文献求助10
5秒前
5秒前
zuolan完成签到,获得积分10
6秒前
尼克11完成签到,获得积分10
6秒前
7秒前
8秒前
种花兔完成签到,获得积分10
8秒前
可爱的函函应助陌路孤星采纳,获得10
8秒前
李健的小迷弟应助sunly采纳,获得10
9秒前
R_完成签到 ,获得积分10
9秒前
虚幻惜筠完成签到,获得积分10
9秒前
10秒前
李健的小迷弟应助阳光BOY采纳,获得10
10秒前
run完成签到,获得积分10
11秒前
11秒前
manman发布了新的文献求助10
11秒前
11秒前
易琚发布了新的文献求助10
12秒前
13秒前
13秒前
Si完成签到,获得积分10
14秒前
辻辰发布了新的文献求助20
15秒前
15秒前
16秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974358
求助须知:如何正确求助?哪些是违规求助? 3518706
关于积分的说明 11195521
捐赠科研通 3254897
什么是DOI,文献DOI怎么找? 1797614
邀请新用户注册赠送积分活动 877011
科研通“疑难数据库(出版商)”最低求助积分说明 806128