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Are we missing the importance of missing values in HIV prevention Randomized clinical trials? Review and Recommendations

机译:我们是否缺少艾滋病毒预防随机临床试验中缺失值的重要性?审查和建议

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摘要

Missing data in HIV prevention trials is a common complication to interpreting outcomes. Even a small proportion of missing values in randomized trials can cause bias, inefficiency and loss of power. We examined the extent of missing data and methods in which HIV prevention randomized clinical trials (RCT) have managed missing values. We used a database maintained by the HIV/AIDS Prevention Research Synthesis (PRS) Project at the Centers for Disease Control and Prevention (CDC) to identify related trials for our review. The PRS cumulative database was searched on June 15, 2010 and all citations that met the following criteria were retrieved: All RCTs which reported HIV/STD/HBV/HCV behavioral interventions with a biological outcome from 2005 to present.Out of the 57 intervention trials identified, all had some level of missing values. We found that the average missing values per study ranged between 3% to 97%. Averaging over all studies the percent of missing values was 26%. None of the studies reported any assumptions for managing missing data in their RCTs. Under some relaxed assumptions discussed below, we expect only 12% of studies to report unbiased results. There is a need for more detailed and thoughtful consideration of the missing data problem in HIV prevention trials. In the current state of managing missing data we risk major biases in interpretations. Several viable alternatives are available for improving the internal validity of RCTs by managing missing data.
机译:HIV预防试验中缺少数据是解释结果的常见并发症。在随机试验中,即使是一小部分的缺失值也可能导致偏差,效率低下和丧失动力。我们检查了艾滋病毒预防随机临床试验(RCT)处理缺失值的数据和方法的缺失程度。我们使用了疾病控制与预防中心(CDC)的HIV / AIDS预防研究综合(PRS)项目维护的数据库来确定相关试验供我们审查。在2010年6月15日检索了PRS累积数据库,并检索了所有符合以下条件的引文:从2005年至今,所有报告了HIV / STD / HBV / HCV行为干预且生物学结果的随机对照试验。确定,都具有某种程度的缺失值。我们发现,每项研究的平均缺失值介于3%至97%之间。在所有研究中,缺失值的百分比平均为26%。这些研究均未报告任何关于在RCT中管理缺失数据的假设。在下面讨论的一些宽松假设下,我们预计只有12%的研究报告无偏见的结果。有必要在艾滋病预防试验中对缺失的数据问题进行更详细,更周到的考虑。在管理丢失数据的当前状态下,我们可能会在解释上出现重大偏差。有几种可行的替代方法可用于通过管理丢失的数据来提高RCT的内部有效性。

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