The net benefit for time-to-event outcome in oncology clinical trials with treatment switching

审查(临床试验) 协变量 估计员 反概率 逆概率加权 加权 统计 临床试验 生存分析 医学 计量经济学 数学 内科学 后验概率 贝叶斯概率 放射科
作者
Musashi Fukuda,Kentaro Sakamaki,Koji Oba
出处
期刊:Clinical Trials [SAGE Publishing]
卷期号:20 (6): 670-680
标识
DOI:10.1177/17407745231186081
摘要

The net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework.The net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study.Our simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy.We proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring.
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
重要忆秋完成签到,获得积分10
刚刚
yy发布了新的文献求助10
刚刚
wwm98656完成签到,获得积分10
3秒前
3秒前
totpto完成签到,获得积分20
4秒前
6秒前
My完成签到,获得积分10
6秒前
孙颖莎粉丝完成签到,获得积分10
6秒前
尼古拉耶维奇完成签到,获得积分10
6秒前
阿卡宁发布了新的文献求助10
7秒前
8秒前
温柔翰完成签到,获得积分10
9秒前
文龙完成签到 ,获得积分10
9秒前
10秒前
Xiaopan完成签到,获得积分10
10秒前
xiaoming发布了新的文献求助200
11秒前
11秒前
QT完成签到,获得积分20
12秒前
朱华彪完成签到,获得积分10
12秒前
活在当下发布了新的文献求助10
12秒前
12秒前
haha发布了新的文献求助10
13秒前
aurora完成签到 ,获得积分10
14秒前
茉莉完成签到,获得积分10
14秒前
123完成签到,获得积分10
16秒前
16秒前
wwewew完成签到,获得积分10
17秒前
saying发布了新的文献求助10
17秒前
123123完成签到,获得积分10
17秒前
隐形曼青应助阿卡宁采纳,获得10
18秒前
负责紊完成签到,获得积分10
18秒前
善良的火发布了新的文献求助10
20秒前
haha完成签到,获得积分10
20秒前
22秒前
sugar完成签到,获得积分10
23秒前
活在当下完成签到,获得积分10
23秒前
25秒前
ssy发布了新的文献求助10
25秒前
小嘉贞完成签到,获得积分10
27秒前
鸡蛋黄完成签到,获得积分10
28秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
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 4038524
求助须知:如何正确求助?哪些是违规求助? 3576221
关于积分的说明 11374737
捐赠科研通 3305912
什么是DOI,文献DOI怎么找? 1819354
邀请新用户注册赠送积分活动 892688
科研通“疑难数据库(出版商)”最低求助积分说明 815048