Spineli Loukia M, Fleming Padhraig S, Pandis Nikolaos
Institut für Biometrie, Medizinische Hochschule Hannover, Hannover, Germany.
Barts and the London School of Medicine and Dentistry, Institute of Dentistry, Queen Mary University, London, United Kingdom.
J Dent. 2015 Jun;43(6):605-18. doi: 10.1016/j.jdent.2015.03.007. Epub 2015 Mar 31.
Missing outcome data are common in clinical trials and despite a well-designed study protocol, some of the randomized participants may leave the trial early without providing any or all of the data, or may be excluded after randomization. Premature discontinuation causes loss of information, potentially resulting in attrition bias leading to problems during interpretation of trial findings. The causes of information loss in a trial, known as mechanisms of missingness, may influence the credibility of the trial results. Analysis of trials with missing outcome data should ideally be handled with intention to treat (ITT) rather than per protocol (PP) analysis. However, true ITT analysis requires appropriate assumptions and imputation of missing data. Using a worked example from a published dental study, we highlight the key issues associated with missing outcome data in clinical trials, describe the most recognized approaches to handling missing outcome data, and explain the principles of ITT and PP analysis.
在临床试验中，缺失结局数据的情况很常见。尽管研究方案设计完善，但一些随机分组的参与者可能会提前退出试验，未提供任何或全部数据，或者在随机分组后被排除。过早停药会导致信息丢失，可能会产生失访偏倚，进而在解释试验结果时引发问题。试验中信息丢失的原因，即缺失机制，可能会影响试验结果的可信度。对于存在缺失结局数据的试验，理想情况下应采用意向性分析（ITT）而非符合方案分析（PP）。然而，真正的ITT分析需要适当的假设和对缺失数据的插补。通过一个已发表的牙科研究中的实例，我们突出了临床试验中与缺失结局数据相关的关键问题，描述了处理缺失结局数据最常用的方法，并解释了ITT和PP分析的原则。
