Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study.
Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study.
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在存在非比例风险和未观察到异质性的情况下，分析复发性不良事件的统计方法比较：一项模拟研究。
DOI：
10.1186/s12874-021-01475-8
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发表时间：
2022-01-20
影响因子：
4
通讯作者：
Chirwa T
中科院分区：
文献类型：
--
作者：
Patson N;Mukaka M;Kazembe L;Eijkemans MJC;Mathanga D;Laufer MK;Chirwa T
在预防性药物试验中，如妊娠期间预防疟疾的间歇性预防性治疗（IPTp），如果重复治疗，预计会复发不良事件（AE）。AE风险建模的挑战包括解释至AE的时间和患者内相关性，超出了传统方法。相关性来自两个来源：（a）个体患者未观察到的异质性（即虚弱）和（B）以时间依赖性治疗效应为特征的AE之间的依赖性。潜在AE依赖性可通过时间依赖性治疗效应、事件特异性基线和事件特异性随机效应建模，而异质性可通过受试者特异性随机效应建模。可改善未观察到的异质性和治疗效应估计的方法可用于了解AE风险的演变，尤其是在预期治疗效应具有时间依赖性的预防性试验中。使用模拟研究和氯喹治疗妊娠期疟疾（NCT 01443130）证明模型应用的试验数据，我们研究了具有限制性三次样条和非比例风险的对数正态共享脆弱性模型（LSF-NPH）假设与传统的具有比例风险的逆高斯共享脆弱性模型相比，是否可以改善脆弱性方差和治疗效果的估计（ISF-PH），存在时间依赖性治疗效应和未观察到的患者异质性。我们评估了在不同的已知未观察到的异质性、样本量和时间依赖效应下，模型脆弱性方差估计值的偏倚、精度增益和95%置信区间的覆盖概率。与LSF-NPH模型相比，ISF-PH模型提供了更好的95%置信区间覆盖概率、更小的偏倚和更低精度的脆弱性方差估计值。与ISF-PH模型相比，LSF-NPH模型以不精确和高均方误差为代价获得了无偏风险比估计值。应根据研究目的选择复发性AE分析的共有虚弱模型。如果在存在时间依赖性治疗效应的情况下主要关注无偏风险比估计，则使用LSF-NPH模型是适当的。然而，ISF-PH模型是适当的，如果无偏脆弱的方差估计是主要的兴趣。ClinicalTrials.gov; NCT01443130
In preventive drug trials such as intermittent preventive treatment for malaria prevention during pregnancy (IPTp), where there is repeated treatment administration, recurrence of adverse events (AEs) is expected. Challenges in modelling the risk of the AEs include accounting for time-to-AE and within-patient-correlation, beyond the conventional methods. The correlation comes from two sources; (a) individual patient unobserved heterogeneity (i.e. frailty) and (b) the dependence between AEs characterised by time-dependent treatment effects. Potential AE-dependence can be modelled via time-dependent treatment effects, event-specific baseline and event-specific random effect, while heterogeneity can be modelled via subject-specific random effect. Methods that can improve the estimation of both the unobserved heterogeneity and treatment effects can be useful in understanding the evolution of risk of AEs, especially in preventive trials where time-dependent treatment effect is expected. Using both a simulation study and the Chloroquine for Malaria in Pregnancy (NCT01443130) trial data to demonstrate the application of the models, we investigated whether the lognormal shared frailty models with restricted cubic splines and non-proportional hazards (LSF-NPH) assumption can improve estimates for both frailty variance and treatment effect compared to the conventional inverse Gaussian shared frailty model with proportional hazard (ISF-PH), in the presence of time-dependent treatment effects and unobserved patient heterogeneity. We assessed the bias, precision gain and coverage probability of 95% confidence interval of the frailty variance estimates for the models under varying known unobserved heterogeneity, sample sizes and time-dependent effects. The ISF-PH model provided a better coverage probability of 95% confidence interval, less bias and less precise frailty variance estimates compared to the LSF-NPH models. The LSF-NPH models yielded unbiased hazard ratio estimates at the expense of imprecision and high mean square error compared to the ISF-PH model. The choice of the shared frailty model for the recurrent AEs analysis should be driven by the study objective. Using the LSF-NPH models is appropriate if unbiased hazard ratio estimation is of primary interest in the presence of time-dependent treatment effects. However, ISF-PH model is appropriate if unbiased frailty variance estimation is of primary interest. ClinicalTrials.gov; NCT01443130
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DOI：
10.1080/03610918.2019.1634201
发表时间：
2019-07-05
影响因子：
0.9
作者：
Bower, Hannah;Crowther, Michael J.;Lambert, Paul C.
通讯作者：
Lambert, Paul C.
影响因子：
2
作者：
Balan, Theodor Adrian;Putter, Hein
通讯作者：
Putter, Hein
影响因子：
2
作者：
Box-Steffensmeier, Janet M.;De Boef, Suzanna
通讯作者：
De Boef, Suzanna
影响因子：
2
作者：
Crowther, Michael J.;Look, Maxime P.;Riley, Richard D.
通讯作者：
Riley, Richard D.
影响因子：
2
作者：
Crowther, Michael J.;Lambert, Paul C.
通讯作者：
Lambert, Paul C.