Fox Chase Cancer Center, Philadelphia, PA, USA.
Clin Trials. 2010 Jun;7(3):286-98. doi: 10.1177/1740774510367811. Epub 2010 Apr 27.
One problem with assessing effects of smoking cessation interventions on withdrawal symptoms is that symptoms are affected by whether participants abstain from smoking during trials. Those who enter a randomized trial but do not change smoking behavior might not experience withdrawal-related symptoms.
We present a tutorial of how one can use a principal stratification sensitivity analysis to account for abstinence in the estimation of smoking cessation intervention effects. The article is intended to introduce researchers to principal stratification and describe how they might implement the methods.
We provide a hypothetical example that demonstrates why estimating effects within observed abstention groups is problematic. We demonstrate how estimation of effects within groups defined by potential abstention that an individual would have in either arm of a study can provide meaningful inferences. We describe a sensitivity analysis method to estimate such effects, and use it to investigate effects of a combined behavioral and nicotine replacement therapy intervention on withdrawal symptoms in a female prisoner population.
Overall, the intervention was found to reduce withdrawal symptoms but the effect was not statistically significant in the group that was observed to abstain. More importantly, the intervention was found to be highly effective in the group that would abstain regardless of intervention assignment. The effectiveness of the intervention in other potential abstinence strata depends on the sensitivity analysis assumptions.
We make assumptions to narrow the range of our sensitivity analysis estimates. While appropriate in this situation, such assumptions might not be plausible in all situations.
A principal stratification sensitivity analysis provides a meaningful method of accounting for abstinence effects in the evaluation of smoking cessation interventions on withdrawal symptoms. Smoking researchers have previously recommended analyses in subgroups defined by observed abstention status in the evaluation of smoking cessation interventions. We believe that principal stratification analyses should replace such analyses as the preferred means of accounting for post-randomization abstinence effects in the evaluation of smoking cessation programs. Clinical Trials 2010; 7: 286-298. http://ctj.sagepub.com.
评估戒烟干预措施对戒断症状影响的一个问题是，症状受到参与者在试验期间是否戒烟的影响。那些参加随机试验但没有改变吸烟行为的人可能不会经历与戒断相关的症状。
我们介绍了一种如何使用主要分层敏感性分析来解释在估计戒烟干预效果时的戒烟情况的方法。本文旨在向研究人员介绍主要分层，并描述他们如何实施这些方法。
我们提供了一个假设的例子，说明了为什么在观察到的戒烟组内估计效果是有问题的。我们展示了如何在个体在研究的任何一组中潜在的戒烟组内估计效果，可以提供有意义的推论。我们描述了一种敏感性分析方法来估计这种效果，并使用它来研究一种联合行为和尼古丁替代疗法干预对女性囚犯群体戒断症状的影响。
总体而言，干预措施被发现可以减轻戒断症状，但在观察到的戒烟组中效果没有统计学意义。更重要的是，无论干预措施如何分配，干预措施在会戒烟的组中都被发现非常有效。干预措施在其他潜在的戒烟层中的有效性取决于敏感性分析的假设。
我们做出了一些假设来缩小敏感性分析估计的范围。虽然在这种情况下是合适的，但在所有情况下，这些假设可能都不切实际。
主要分层敏感性分析为评估戒烟干预措施对戒断症状的影响时，提供了一种有意义的方法来解释戒烟效果。吸烟研究人员此前曾建议在评估戒烟干预措施时，根据观察到的戒烟状态定义亚组进行分析。我们认为，在评估戒烟计划时，主要分层分析应该取代这种分析，作为解释随机化后戒烟效果的首选方法。临床试验 2010; 7: 286-298. http://ctj.sagepub.com.
