Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.
Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.
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DOI：
10.1371/journal.pone.0006624
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发表时间：
2009-08-13
期刊：
影响因子：
3.7
通讯作者：
Allison DB
中科院分区：
文献类型：
--
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
在肥胖随机对照试验中，脱落和缺失数据几乎无处不在，威胁着结论的有效性和普遍性。在此，我们通过荟萃分析评估缺失数据的程度、采用各种分析方法以适应脱落的频率以及多种统计方法的性能。我们检索了PubMed和科克伦数据库（2000-2006）中以英文发表的文章，并手动检索了参考书目。纳入以体重减轻或体重增加预防为主要终点的药物随机对照试验的文章。两名作者独立审查了每篇文献的纳入情况。121篇文章符合纳入标准。两位作者独立提取了治疗、样本量、脱落率、研究持续时间和用于处理所有文章缺失数据的统计方法，并通过共识解决了分歧。在荟萃分析中，脱落率很高，生存率（非脱落率）近似于指数衰减曲线（e−λt），其中λ估计为0.0088（95% bootstrap置信区间：0.0076至0.0100），t代表时间（周）。1年时的估计辍学率为37%。大多数研究使用末次观测值结转作为处理缺失数据的主要分析方法。我们还获得了12个原始的肥胖随机对照试验数据集进行实证分析。对原始随机对照试验数据的分析表明，混合模型和多重插补均表现良好，但当缺失数据广泛时，多重插补可能更稳健。我们的分析提供了一个预测辍学率的方程，对未来的学习规划有用。我们的原始数据分析表明，多重插补比其他方法更好地处理肥胖随机对照试验中的缺失数据，其次是混合模型。我们建议用这些方法代替最后一次观察值作为主要的分析方法。
Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. We searched PubMed and Cochrane databases (2000–2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e−λt) where λ was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.
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影响因子：
29
作者：
Erondu, Ngozi;Gantz, Ira;Heymsfield, Steven B.
通讯作者：
Heymsfield, Steven B.
影响因子：
120.7
作者：
Fontaine, KR;Redden, DT;Allison, DB
通讯作者：
Allison, DB
影响因子：
4.9
作者：
Coffey, CS;Steiner, D;Allison, DB
通讯作者：
Allison, DB
影响因子：
4.9
作者：
Gadde, KM;Yonish, GM;Allison, DB
通讯作者：
Allison, DB
作为一种潜在的抗肥胖剂的藤黄属植物（羟基柠檬酸）--： 随机对照试验
影响因子：
120.7
作者：
Heymsfield, SB;Allison, DB;Nunez, C
通讯作者：
Nunez, C