Analysis of the Single Ventricle Reconstruction Trial Using Restricted Mean Survival Time and Shunt Type Received.
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ABSTRACT:
SUBMITTER: Dolgner SJ
PROVIDER: S-EPMC9683638 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Publications
Analysis of the Single Ventricle Reconstruction Trial Using Restricted Mean Survival Time and Shunt Type Received.
Dolgner Stephen J SJ Tjoeng Yuen L YL Chan Titus T
Journal of the American Heart Association 20220908 18
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Project description:In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.
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Project description:Restricted mean survival time (RMST) is a clinically interpretable and meaningful survival metric that has gained popularity in recent years. Several methods are available for regression modeling of RMST, most based on pseudo-observations or what is essentially an inverse-weighted complete-case analysis. No existing RMST regression method allows for the covariate effects to be expressed as functions over time. This is a considerable limitation, in light of the many hazard regression methods that do accommodate such effects. To address this void in the literature, we propose RMST methods that permit estimating time-varying effects. In particular, we propose an inference framework for directly modeling RMST as a continuous function of L. Large-sample properties are derived. Simulation studies are performed to evaluate the performance of the methods in finite sample sizes. The proposed framework is applied to kidney transplant data obtained from the Scientific Registry of Transplant Recipients.
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Project description:Restricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose generalized estimating equation methods to model RMST as a function of baseline covariates. The proposed methods avoid potentially problematic distributional assumptions pertaining to restricted survival time. Unlike existing methods, we allow censoring to depend on both baseline and time-dependent factors. Large sample properties of the proposed estimators are derived and simulation studies are conducted to assess their finite sample performance. We apply the proposed methods to model RMST in the absence of liver transplantation among end-stage liver disease patients. This analysis requires accommodation for dependent censoring since pre-transplant mortality is dependently censored by the receipt of a liver transplant.
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Project description:BackgroundIn children with single right ventricular (RV) anomalies, changes in RV size and function may be influenced by shunt type chosen at the time of the Norwood procedure.ObjectivesThe study sought to identify shunt-related differences in echocardiographic findings at 14 months and ≤6 months pre-Fontan in survivors of the Norwood procedure.MethodsWe compared 2-dimensional and Doppler echocardiographic indices of RV size and function, neo-aortic and tricuspid valve annulus dimensions and function, and aortic size and patency at 14.1 ± 1.2 months and 33.6 ± 9.6 months in subjects randomized to a Norwood procedure using either the modified Blalock-Taussig shunt (MBTS) or right ventricle to pulmonary artery shunt (RVPAS).ResultsAcceptable echocardiograms were available at both time points in 240 subjects (114 MBTS, 126 RVPAS). At 14 months, all indices were similar between shunt groups. From the 14-month to pre-Fontan echocardiogram, the MBTS group had stable indexed RV volumes and ejection fraction, while the RVPAS group had increased RV end-systolic volume (p = 0.004) and decreased right ventricular ejection fraction (RVEF) (p = 0.004). From 14 months to pre-Fontan, the treatment groups were similar with respect to decline in indexed neo-aortic valve area, >mild neo-aortic valve regurgitation (<5% at each time), indexed tricuspid valve area, and ≥moderate tricuspid valve regurgitation (<20% at each time).ConclusionsInitial Norwood shunt type influences pre-Fontan RV remodeling during the second and third years of life in survivors with single RV anomalies, with greater RVEF deterioration after RVPAS. Encouragingly, other indices of RV function remain stable before Fontan regardless of shunt type. (Comparison of Two Types of Shunts in Infants with Single Ventricle Defect Undergoing Staged Reconstruction-Pediatric Heart Network; NCT00115934).
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Project description:Estimation and prediction of heterogeneous restricted mean survival time (hRMST) is of great clinical importance, which can provide an easily interpretable and clinically meaningful summary of the survival function in the presence of censoring and individual covariates. The existing methods for the modeling of hRMST rely on proportional hazards or other parametric assumptions on the survival distribution. In this paper, we propose a random forest based estimation of hRMST for right-censored survival data with covariates and prove a central limit theorem for the resulting estimator. In addition, we present a computationally efficient construction for the confidence interval of hRMST. Our simulations show that the resulting confidence intervals have the correct coverage probability of the hRMST, and the random forest based estimate of hRMST has smaller prediction errors than the parametric models when the models are mis-specified. We apply the method to the ovarian cancer data set from The Cancer Genome Atlas (TCGA) project to predict hRMST and show an improved prediction performance over the existing methods. A software implementation, srf using R and C++, is available at https://github.com/lmy1019/SRF.
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Project description:In clinical studies with time-to-event outcomes, the restricted mean survival time (RMST) has attracted substantial attention as a summary measurement for its straightforward clinical interpretation. When the data are subject to length-biased sampling, which is frequently encountered in observational cohort studies, existing methods to estimate the RMST are not applicable. In this article, we consider nonparametric and semiparametric regression methods to estimate the RMST under the setting of length-biased sampling. To assess the covariate effects on the RMST, a semiparametric regression model that directly relates the covariates and the RMST is assumed. Based on the model, we develop unbiased estimating equations to obtain consistent estimators of covariate effects by properly adjusting for informative censoring and length bias. Stochastic process theories are used to establish the asymptotic properties of the proposed estimators. We investigate the finite sample performance through simulations and illustrate the methods by analyzing a prevalent cohort study of dementia in Canada.
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Project description:The restricted mean survival time (RMST), which evaluates the expected survival time up to a pre-specified time point τ, has been widely used to summarize the survival distribution due to its robustness and straightforward interpretation. In comparative studies with time-to-event data, the RMST-based test has been utilized as an alternative to the classic log-rank test because the power of the log-rank test deteriorates when the proportional hazards assumption is violated. To overcome the challenge of selecting an appropriate time point τ, we develop an RMST-based omnibus Wald test to detect the survival difference between two groups throughout the study follow-up period. Treating a vector of RMSTs at multiple quantile-based time points as a statistical functional, we construct a Wald χ2 test statistic and derive its asymptotic distribution using the influence function. We further propose a new procedure based on the influence function to estimate the asymptotic covariance matrix in contrast to the usual bootstrap method. Simulations under different scenarios validate the size of our RMST-based omnibus test and demonstrate its advantage over the existing tests in power, especially when the true survival functions cross within the study follow-up period. For illustration, the proposed test is applied to two real datasets, which demonstrate its power and applicability in various situations.
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Project description:Background/objectivesRestricted mean survival time (RMST) summarizes treatment effect in terms of a gain or loss in the event-free days. It remains uncertain whether communicating treatment benefit and harm using RMST-based summary is more effective than conventional summary based on absolute and relative risk reduction. We compared the effect of RMST-based approach and conventional approach on decisional conflict using an example of intensive versus standard blood pressure-lowering strategies.DesignOn-line survey.SettingA convenience sample of patients in the United States.ParticipantsTwo hundred adults aged 65 and older with hypertension requiring anti-hypertensive treatment (response rate 85.5%).InterventionsParticipants were randomly assigned to either RMST-based summary or conventional summary about the benefit and harm of blood pressure-lowering strategies.MeasurementsDecisional Conflict Scale (DCS), ranging from 0 (no conflict) to 100 (high conflict), and preference for intensive blood pressure-lowering strategy.ResultsParticipants assigned to RMST-based approach (n = 100) and conventional approach (n = 100) had similar age (mean [standard deviation, SD]: 72.3 [5.6] vs 72.8 [5.5] years) and proportions of female (50 [50.0%] vs 61 [61.0%]) and white race (92 [92.0%] vs 92 [92.0%]). The mean (SD) DCS score was 25.2 (15.0) for RMST-based approach and 25.6 (14.1) for conventional approach (p = 0.84). The number (%) of participants who preferred intensive strategy was 10 (10.0%) for RMST-based approach and 14 (14.0%) for conventional approach (p = 0.52). The results were consistent in subgroups defined by age, sex, education level, cardiovascular disease status, and predicted mortality risk categories.ConclusionIn a sample of relatively healthy older adults with hypertension, RMST-based approach was as effective as conventional approach on decisional conflict about choosing a blood pressure-lowering strategy. This study provides proof-of-concept evidence that RMST-based approach can be used in conjunction with absolute and relative risk reduction for communicating treatment benefit and harm in a decision aid.
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Project description:Hazard ratios are ubiquitously used in time to event analysis to quantify treatment effects. Although hazard ratios are invaluable for hypothesis testing, other measures of association, both relative and absolute, may be used to fully elucidate study results. Restricted mean survival time (RMST) differences between groups have been advocated as useful measures of association. Recent work focused on model-free estimates of the difference in restricted mean survival through follow-up times, instead of focusing on a single time horizon. The resulting curve can be used to quantify the association in time units with a simultaneous confidence band. In this work a model-based estimate of the curve is proposed using pseudo-values allowing for possible covariate adjustment. The method is easily implementable with available software and makes possible to compute a simultaneous confidence region for the curve. The pseudo-values regression using multiple restriction times is in good agreement with the estimates obtained by standard direct regression models fixing a single restriction time. Moreover, the proposed method is flexible enough to reproduce the results of the non-parametric approach when no covariates are considered. Examples where it is important to adjust for baseline covariates will be used to illustrate the different methods together with some simulations.
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