Getting carried away: a note showing baseline observation carried forward (BOCF) results can be calculated from published complete-cases results.
Getting carried away: a note showing baseline observation carried forward (BOCF) results can be calculated from published complete-cases results.
复制标题
DOI：
10.1038/ijo.2011.25
复制
发表时间：
2012-06
影响因子：
4.9
通讯作者：
Allison, D. B.
中科院分区：
文献类型：
--
作者：
Kaiser, K. A.;Affuso, O.;Beasley, T. M.;Allison, D. B.
关键词：
肥胖的随机对照试验（RCT）由于参与者退出而缺失数据。大多数方法学家和监管机构都认为，此类RCT的主要分析应基于意向治疗（ITT）原则，以便将所有随机化受试者纳入分析，即使是那些退出的受试者。不幸的是，一些作者在其发表的报告中没有包括ITT分析。在这里，我们展示了ITT分析的一种形式，基线观察结转（BOCF），可以只利用已发表的完整病例（CC）分析中的信息进行分析，允许读者，编辑，荟萃分析师和监管机构在原始作者没有报告的情况下轻松进行自己的ITT分析。我们从数学上推导出一种简单的方法，使用BOCF估计和测试治疗效果，以便在临床试验中出现脱落时对治疗效果进行更保守的比较。我们提供了两个例子，这种方法使用现有的CC分析数据报告的肥胖试验，以说明应用程序的读者谁希望确定一系列的治疗效果的基础上公布的汇总统计。常用的CC分析可能导致I类错误率和/或治疗效果估计值夸大。本文所述的方法可用于希望基于有限的报告数据估计合理治疗效果的保守范围的研究人员。这种方法的局限性进行了讨论。
Randomized controlled trials (RCTs) in obesity are plagued by missing data due to participant drop-outs. Most methodologists and regulatory bodies agree that the primary analysis of such RCTs should be based on the intent-to-treat (ITT) principle, such that all randomized subjects are included in the analysis, even those who dropped out. Unfortunately, some authors do not include an ITT analysis in their published reports. Here we show that one form of ITT analysis, baseline observation carried forward (BOCF), can be performed utilizing only information available in a published complete case (CC) analysis, permitting readers, editors, meta-analysts, and regulators to easily conduct their own ITT analyses when the original authors do not report one. We mathematically derive a simple method for estimating and testing treatment effects using the BOCF to allow a more conservative comparison of treatment effects when there are drop outs in a clinical trial. We provide two examples of this method using available CC analysis data from reported obesity trials to illustrate the application for readers who wish to determine a range of treatment effects based on published summary statistics. Commonly used CC analyses may lead to inflated Type I error rates and/or treatment effect estimates. The method described herein can be useful for researchers who wish to estimate a conservative range of plausible treatment effects based on limited reported data. Limitations of this method are discussed.
登录
查看更多内容
影响因子：
7.1
作者：
Keogh, Jennifer B.;Brinkworth, Grant D.;Clifton, Peter M.
通讯作者：
Clifton, Peter M.
DOI：
10.1046/j.1467-789x.2003.00109.x
发表时间：
2003-08-01
期刊：
Obesity reviews : an official journal of the International Association for the Study of Obesity
影响因子：
--
作者：
Gadbury, G L;Coffey, C S;Allison, D B
通讯作者：
Allison, D B
影响因子：
3.6
作者：
Gaullier, Jean-Michel;Halse, Johan;Gudmundsen, Ola
通讯作者：
Gudmundsen, Ola
影响因子：
3.7
作者：
Elobeid MA;Padilla MA;McVie T;Thomas O;Brock DW;Musser B;Lu K;Coffey CS;Desmond RA;St-Onge MP;Gadde KM;Heymsfield SB;Allison DB
通讯作者：
Allison DB
影响因子：
1.1
作者：
Shao, Jun;Jordan, David C.;Pritchett, Yili L.
通讯作者：
Pritchett, Yili L.