Brownstein Naomi C, Cai Jianwen, Slade Gary D, Bair Eric
Ion Cyclotron Resonance Facility, National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, U.S.A.
Department of Statistics, Florida State University, Tallahassee, FL, U.S.A.
Stat Med. 2015 Dec 30;34(30):3984-96. doi: 10.1002/sim.6604. Epub 2015 Aug 4.
In a prospective cohort study, examining all participants for incidence of the condition of interest may be prohibitively expensive. For example, the "gold standard" for diagnosing temporomandibular disorder (TMD) is a physical examination by a trained clinician. In large studies, examining all participants in this manner is infeasible. Instead, it is common to use questionnaires to screen for incidence of TMD and perform the "gold standard" examination only on participants who screen positively. Unfortunately, some participants may leave the study before receiving the "gold standard" examination. Within the framework of survival analysis, this results in missing failure indicators. Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large cohort study of TMD, we propose a method for parameter estimation in survival models with missing failure indicators. We estimate the probability of being an incident case for those lacking a "gold standard" examination using logistic regression. These estimated probabilities are used to generate multiple imputations of case status for each missing examination that are combined with observed data in appropriate regression models. The variance introduced by the procedure is estimated using multiple imputation. The method can be used to estimate both regression coefficients in Cox proportional hazard models as well as incidence rates using Poisson regression. We simulate data with missing failure indicators and show that our method performs as well as or better than competing methods. Finally, we apply the proposed method to data from the OPPERA study.
在一项前瞻性队列研究中，对所有参与者检查感兴趣疾病的发病率可能成本过高。例如，诊断颞下颌关节紊乱病（TMD）的“金标准”是由经过培训的临床医生进行体格检查。在大型研究中，以这种方式检查所有参与者是不可行的。相反，通常使用问卷来筛查TMD的发病率，并且仅对筛查呈阳性的参与者进行“金标准”检查。不幸的是，一些参与者可能在接受“金标准”检查之前就退出了研究。在生存分析的框架内，这会导致缺失失败指标。受口腔面部疼痛：前瞻性评估与风险评估（OPPERA）研究（一项关于TMD的大型队列研究）的启发，我们提出了一种在存在缺失失败指标的生存模型中进行参数估计的方法。我们使用逻辑回归估计那些缺乏“金标准”检查的人成为发病病例的概率。这些估计概率用于为每次缺失检查生成病例状态的多重填补值，并在适当的回归模型中与观察数据相结合。通过多重填补估计该过程引入的方差。该方法可用于估计Cox比例风险模型中的回归系数以及使用泊松回归估计发病率。我们模拟了带有缺失失败指标的数据，并表明我们的方法与其他竞争方法表现相当或更好。最后，我们将所提出的方法应用于OPPERA研究的数据。
