{"title":"Using Limited Trial Evidence to Credibly Choose Treatment Dosage when Efficacy and Adverse Effects Weakly Increase with Dose.","authors":"Charles F Manski","doi":"10.1097/EDE.0000000000001793","DOIUrl":null,"url":null,"abstract":"<p><p>It has become standard in medical treatment to base dosage on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to compare some dose of a new drug with an established therapy or placebo. Standard trial analysis views each trial arm as qualitatively different, but it may be credible to assume that efficacy and adverse effects weakly increase with dosage. Optimization of patient care requires joint attention to both, as well as to treatment cost. This paper develops methodology to use limited trial evidence to choose dosage when efficacy and adverse effects weakly increase with dose. I suppose that dosage is an integer t ∊ (0,1, . ,T), T being a specified maximum dose. I study dosage choice when trial evidence on outcomes is available for only K dose levels, where K < T+1. Then the population distribution of dose response is partially identified. I show that the identification region is a convex polygon. I characterize clinical and population decision making using the minimax-regret criterion. A simple analytical solution exists when T = 2. Computation is tractable when T is larger.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/EDE.0000000000001793","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
It has become standard in medical treatment to base dosage on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to compare some dose of a new drug with an established therapy or placebo. Standard trial analysis views each trial arm as qualitatively different, but it may be credible to assume that efficacy and adverse effects weakly increase with dosage. Optimization of patient care requires joint attention to both, as well as to treatment cost. This paper develops methodology to use limited trial evidence to choose dosage when efficacy and adverse effects weakly increase with dose. I suppose that dosage is an integer t ∊ (0,1, . ,T), T being a specified maximum dose. I study dosage choice when trial evidence on outcomes is available for only K dose levels, where K < T+1. Then the population distribution of dose response is partially identified. I show that the identification region is a convex polygon. I characterize clinical and population decision making using the minimax-regret criterion. A simple analytical solution exists when T = 2. Computation is tractable when T is larger.
根据随机试验的证据确定用药剂量已成为医学治疗的标准。然而,研究结果如何随剂量的变化而变化却很少见。在为获得药物批准而进行的试验中,通常是将某种剂量的新药与既有疗法或安慰剂进行比较。标准的试验分析认为每个试验组都有质的不同,但假设疗效和不良反应随剂量的增加而微弱增加可能是可信的。优化患者护理需要同时关注这两方面以及治疗成本。本文提出了当疗效和不良反应随剂量增加而微弱增加时,利用有限的试验证据选择剂量的方法。我假设剂量为整数 t ∊ (0,1, . ,T),T 是指定的最大剂量。我研究的是当只有 K 个剂量水平的试验结果证据时的剂量选择,其中 K < T+1。然后,剂量反应的总体分布被部分识别出来。我证明了识别区域是一个凸多边形。我用最小遗憾准则描述了临床和人群决策的特点。当 T = 2 时,存在一个简单的解析解。当 T 较大时,计算很容易。
期刊介绍:
Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.