Edwards Jessie K, Breger Tiffany L, Cole Stephen R, Zivich Paul N, Shook-Sa Bonnie E, Sadinski Leah M, Westreich Daniel, Edmonds Andrew, Ramirez Catalina, Ofotokun Igho, Kassaye Seble G, Brown Todd T, Konkle-Parker Deborah, Stosor Valentina, Bolan Robert, Krier Sarah, Jones Deborah L, D'Souza Gypsyamber, Cohen Mardge, Tien Phyllis C, Taylor Tonya, Anastos Kathryn, Drummond M Bradley, Floris-Moore Michelle
From the Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA.
Epidemiology. 2025 Jul 1;36(4):511-519. doi: 10.1097/EDE.0000000000001852. Epub 2025 Mar 24.
Epidemiologists frequently employ right censoring to handle missing outcome, covariate, or exposure data incurred when participants have large gaps between study visits or stop attending study visits entirely. But, if participants who are censored are more or less likely to experience outcomes of interest than those not censored, such censoring could introduce bias in estimated measures.
We examined how censoring after two consecutive missed visits may affect mortality results from the Multicenter AIDS Cohort Study (MACS) and Women's Interagency HIV Study (WIHS). MACS and WIHS provide linkages to vital statistics registries, such that mortality data were available for all participants, regardless of whether they attended study visits.
In a gold standard analysis that did not censor after two consecutive missed visits, 10-year mortality was 23% (95% CI: 22, 24) in MACS and 21% (95% CI: 20, 23) in WIHS. Estimated mortality was modestly reduced by 0%-5% across subgroups when censoring at missed visits. Applying inverse probability of censoring weights partially removed this attenuation.
While mortality was slightly elevated after two consecutive missed visits in MACS and WIHS, censoring at two consecutive missed visits did not substantially alter estimated mortality, particularly after applying inverse probability of censoring weights.
流行病学家经常采用右删失法来处理当参与者在研究访视之间有较大间隔或完全停止参加研究访视时出现的缺失结局、协变量或暴露数据。但是,如果被删失的参与者比未被删失的参与者经历感兴趣结局的可能性更高或更低,那么这种删失可能会在估计指标中引入偏差。
我们研究了连续两次错过访视后的删失如何影响多中心艾滋病队列研究(MACS)和女性机构间HIV研究(WIHS)的死亡率结果。MACS和WIHS提供了与人口动态统计登记处的关联,因此所有参与者都可获得死亡率数据,无论他们是否参加研究访视。
在一项未在连续两次错过访视后进行删失的金标准分析中,MACS的10年死亡率为23%(95%CI:22,24),WIHS为21%(95%CI:20,23)。在错过访视时进行删失时,各亚组的估计死亡率适度降低了0%-5%。应用删失权重的逆概率部分消除了这种衰减。
虽然在MACS和WIHS中连续两次错过访视后的死亡率略有升高,但连续两次错过访视时进行删失并没有实质性改变估计的死亡率,特别是在应用删失权重的逆概率之后。