存在测量误差和区间 censoring 时，时变协变量模型与生存时间联合模型的比较：在肾移植中的应用。
Comparison of a time-varying covariate model and a joint model of time-to-event outcomes in the presence of measurement error and interval censoring: application to kidney transplantation.
机构信息
Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, 80045, Colorado, USA.
Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, 80045, Colorado, USA.
出版信息
BMC Med Res Methodol. 2019 Jun 26;19(1):130. doi: 10.1186/s12874-019-0773-1.
BACKGROUND
Tacrolimus (TAC) is an immunosuppressant drug given to kidney transplant recipients post-transplant to prevent antibody formation and kidney rejection. The optimal therapeutic dose for TAC is poorly defined and therapy requires frequent monitoring of drug trough levels. Analyzing the association between TAC levels over time and the development of potentially harmful de novo donor specific antibodies (dnDSA) is complex because TAC levels are subject to measurement error and dnDSA is assessed at discrete times, so it is an interval censored time-to-event outcome.
METHODS
Using data from the University of Colorado Transplant Center, we investigated the association between TAC and dnDSA using a shared random effects (intercept and slope) model with longitudinal and interval censored survival sub-models (JM) and compared it with the more traditional interval censored survival model with a time-varying covariate (TVC). We carried out simulations to compare bias, level and power for the association parameter in the TVC and JM under varying conditions of measurement error and interval censoring. In addition, using Markov Chain Monte Carlo (MCMC) methods allowed us to calculate clinically relevant quantities along with credible intervals (CrI).
RESULTS
The shared random effects model was a better fit and showed both the average TAC and the slope of TAC were associated with risk of dnDSA. The simulation studies demonstrated that, in the presence of heavy interval censoring and high measurement error, the TVC survival model underestimates the association between the survival and longitudinal measurement and has inflated type I error and considerably less power to detect associations.
CONCLUSIONS
To avoid underestimating associations, shared random effects models should be used in analyses of data with interval censoring and measurement error.
背景
他克莫司（TAC）是一种免疫抑制剂药物，用于移植后预防抗体形成和肾排斥反应。TAC 的最佳治疗剂量定义不佳，需要频繁监测药物谷浓度。分析 TAC 水平随时间的变化与潜在有害的新供体特异性抗体（dnDSA）发展之间的关系是复杂的，因为 TAC 水平存在测量误差，dnDSA 是在离散时间点评估的，因此它是区间删失的时事件结果。
方法
利用科罗拉多大学移植中心的数据，我们使用具有纵向和区间删失生存子模型的共享随机效应（截距和斜率）模型（JM）来研究 TAC 与 dnDSA 之间的关系，并将其与具有时变协变量的更传统的区间删失生存模型（TVC）进行了比较。我们进行了模拟，以比较在不同的测量误差和区间删失条件下，TVC 和 JM 中关联参数的偏差、水平和功效。此外，使用马尔可夫链蒙特卡罗（MCMC）方法使我们能够计算出具有可信区间（CrI）的临床相关数量。
结果
共享随机效应模型拟合得更好，表明平均 TAC 和 TAC 斜率都与 dnDSA 风险相关。模拟研究表明，在存在大量区间删失和高测量误差的情况下，TVC 生存模型低估了生存和纵向测量之间的关联，并且存在较大的Ⅰ型错误和检测关联的功效大大降低。
结论
为了避免低估关联，应在具有区间删失和测量误差的数据分析中使用共享随机效应模型。
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