Relative Risk Estimation in Randomized Controlled Trials: A Comparison of Methods for Independent Observations

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI:10.2202/1557-4679.1278

L. Yelland, A. Salter, Philip Ryan

引用次数: 38

Abstract

The relative risk is a clinically important measure of the effect of treatment on binary outcomes in randomized controlled trials (RCTs). An adjusted relative risk can be estimated using log binomial regression; however, convergence problems are common with this model. While alternative methods have been proposed for estimating relative risks, comparisons between methods have been limited, particularly in the context of RCTs. We compare ten different methods for estimating relative risks under a variety of scenarios relevant to RCTs with independent observations. Results of a large simulation study show that some methods may fail to overcome the convergence problems of log binomial regression, while others may substantially overestimate the treatment effect or produce inaccurate confidence intervals. Further, conclusions about the effectiveness of treatment may differ depending on the method used. We give recommendations for choosing a method for estimating relative risks in the context of RCTs with independent observations.

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随机对照试验的相对风险估计:独立观察方法的比较

在随机对照试验(rct)中，相对危险度是衡量治疗对二元结局影响的重要临床指标。调整后的相对风险可用对数二项回归估计;然而，该模型的收敛性问题是常见的。虽然已经提出了估算相对风险的替代方法，但方法之间的比较有限，特别是在随机对照试验的背景下。我们比较了十种不同的方法来估计相对风险在各种情况下与独立观察的随机对照试验相关。一项大型模拟研究的结果表明，一些方法可能无法克服对数二项回归的收敛问题，而另一些方法可能严重高估处理效果或产生不准确的置信区间。此外，关于治疗有效性的结论可能因使用的方法而异。我们建议在独立观察的随机对照试验中选择一种估计相对风险的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.