Scholarlink

<p>Clinical trials are essential for advancing medical knowledge and improving health care, with Randomized Clinical Trials (RCTs) considered the gold standard for minimizing bias and generating reliable evidence on treatment efficacy and safety. Stepped-wedge individual RCTs, which randomize participants into sequences transitioning from control to intervention at staggered time points, are increasingly adopted. To improve their design, we propose an information-theoretic framework based on D– and A–optimality criteria for participant allocation to sequences. Our approach leverages semidefinite programming for automated computation and is applicable across a range of settings, varying in: (i) number of sequences, (ii) attrition rates, (iii) optimality criteria, (iv) error correlation structures, and (v) multi-objective designs using the <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11222_2025_10690_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="10" /> </InlineMediaObject> <EquationSource Format="TEX">\(\epsilon \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>ϵ</mi> </math></EquationSource> </InlineEquation>-constraint method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Information-Theoretic Criteria for Optimizing Designs of Individually Randomized Stepped-Wedge Clinical Trials

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月11日

查看全文
  • Belmiro P. M. Duarte,
  • Anthony C. Atkinson,
  • Mirjam Moerbeek

摘要

Clinical trials are essential for advancing medical knowledge and improving health care, with Randomized Clinical Trials (RCTs) considered the gold standard for minimizing bias and generating reliable evidence on treatment efficacy and safety. Stepped-wedge individual RCTs, which randomize participants into sequences transitioning from control to intervention at staggered time points, are increasingly adopted. To improve their design, we propose an information-theoretic framework based on D– and A–optimality criteria for participant allocation to sequences. Our approach leverages semidefinite programming for automated computation and is applicable across a range of settings, varying in: (i) number of sequences, (ii) attrition rates, (iii) optimality criteria, (iv) error correlation structures, and (v) multi-objective designs using the \(\epsilon \) ϵ -constraint method.