Ganz David A, Greene Erich J, Latham Nancy K, Kane Michael, Min Lillian C, Gill Thomas M, Reuben David B, Peduzzi Peter, Esserman Denise
Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Geriatric Research, Education and Clinical Center, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Health Care Division, RAND, Santa Monica, CA, USA.
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
J Clin Epidemiol. 2025 May;181:111718. doi: 10.1016/j.jclinepi.2025.111718. Epub 2025 Feb 10.
Routinely collected data (RCD) from healthcare claims and encounters are increasingly used for outcomes in randomized trials; however, methods for estimating the validity and relative precision of RCD-derived outcomes compared to those from conventional outcome ascertainment are limited. We developed an approach to measuring validity and relative precision of RCD and quantifying uncertainty.
We reanalyzed data from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) cluster-randomized, controlled trial. Eighty-six primary care practices in 10 US healthcare systems were randomized to either a multifactorial intervention delivered by nurse falls care managers, or enhanced usual care, with 5451 persons age ≥ 70 at increased fall injury risk enrolled in the study. We estimated the hazard ratio (HR) and confidence interval (CI) for STRIDE's primary outcome (time to first serious fall injury) using original study data and RCD. The ratio of the RCD HR to original HR ("ratio of HRs") measured validity. The confidence limit ratio (CLR; upper divided by lower confidence limits of CI) measured precision, with the ratio of the CLR with RCD to the CLR from the original study data ("ratio of CLRs"), measuring relative precision. We estimated uncertainty around the ratio of HRs and ratio of CLRs using bootstrapped 95% CIs and performed sensitivity analyses to assess the effects of adaptations needed to use RCD.
Among the original sample of 5451 study participants, 5036 (92%) were linked to RCD. The intervention to control HR was 0.91 (95% CI: 0.78-1.07) in RCD, compared to 0.92 (95% CI: 0.80-1.06) in the original data. Using all RCD through STRIDE's administrative end date, the ratio of HRs was 1.00 (95% CI: 0.89-1.11) and ratio of CLRs was 1.03 (95% CI: 0.96-1.06). The CI around ratio of HRs was about three-fold wider for RCD than for the original STRIDE data in individuals who linked to RCD. Relative precision of RCD improved with increased length of follow-up.
Relying solely on RCD to ascertain the primary outcome in STRIDE would have resulted in similar point estimates and confidence limits for the treatment effect as in the original data. However, there was meaningful uncertainty around the estimate of validity. Efforts to validate RCD-derived outcomes for use as clinical trial endpoints should include measurement of uncertainty around validity estimates.
来自医疗保健理赔和诊疗记录的常规收集数据(RCD)越来越多地用于随机试验的结果分析;然而,与传统结果确定方法相比,估计RCD衍生结果的有效性和相对精度的方法有限。我们开发了一种测量RCD有效性和相对精度以及量化不确定性的方法。
我们重新分析了“减少老年人伤害并增强信心策略(STRIDE)”整群随机对照试验的数据。美国10个医疗保健系统中的86个初级保健机构被随机分配到由护士跌倒护理经理提供的多因素干预组或强化常规护理组,共有5451名年龄≥70岁且跌倒受伤风险增加的人参与了该研究。我们使用原始研究数据和RCD估计STRIDE主要结局(首次严重跌倒受伤时间)的风险比(HR)和置信区间(CI)。RCD的HR与原始HR的比值(“HR比值”)衡量有效性。置信限比值(CLR;CI的上限除以下限)衡量精度,RCD的CLR与原始研究数据的CLR的比值(“CLR比值”)衡量相对精度。我们使用自抽样95%CI估计HR比值和CLR比值周围的不确定性,并进行敏感性分析以评估使用RCD所需调整的影响。
在5451名研究参与者的原始样本中,5036人(占92%)与RCD相关联。RCD中控制HR的干预措施为0.91(95%CI:0.78 - 1.07),而原始数据中为0.92(95%CI:0.80 - 1.06)。使用STRIDE行政结束日期前的所有RCD,HR比值为1.00(95%CI:0.89 - 1.11),CLR比值为1.03(95%CI:0.96 - 1.06)。在与RCD相关联的个体中,RCD的HR比值周围的CI比原始STRIDE数据宽约三倍。RCD的相对精度随着随访时间的延长而提高。
仅依靠RCD来确定STRIDE的主要结局,对于治疗效果的点估计和置信限将与原始数据相似。然而,有效性估计存在显著的不确定性。验证RCD衍生结果以用作临床试验终点的努力应包括测量有效性估计周围的不确定性。