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We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime. The proposed model extends the parametric conditional approach given terminal event time in recent literature, and thus avoids potential model misspecification. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computed sandwich covariance matrix estimator. We conduct extensive simulations that show desirable performance of the proposed approach, and apply the method to analyzing the medical cost data for patients with end-stage renal disease. for this article are available online. [ABSTRACT FROM AUTHOR]

BackgroundUse of alteplase improves outcome in some patients with stroke. Several types of barrier frequently prevent its use. We assessed whether a standardised, barrier-assessment, multicomponent intervention could increase alteplase use in community hospitals in Michigan, USA.MethodsIn a cluster-randomised controlled trial, we selected adult, non-specialty, acute-care community hospitals in the Lower Peninsula of Michigan, USA. Eligible hospitals discharged at least 100 patients who had had a stroke per year, had less than 100 000 visits to the emergency department per year, and were not academic comprehensive stroke centres. Using a computer-generated randomisation sequence, we selected 12 matched pairs of eligible hospitals. Within pairs, the hospitals were allocated to intervention or control groups with restricted randomisation in January, 2007. Between January, 2007, and December, 2007, intervention hospitals implemented a multicomponent intervention that included qualitative and quantitative assessment of barriers to alteplase use and ways to address the findings, and provided additional support. The primary outcome was change in alteplase use in patients with stroke in emergency departments between the pre-intervention period (January, 2005, to December, 2006) and the post-intervention period (January, 2008, to January, 2010). Physicians in participating hospitals and the coordinating centre could not be masked to group assignment, but were masked to progress made in paired control hospitals. External medical reviewers who were masked to group assignment assessed outcomes. We did intention-to-treat (ITT) and target-population (without one pair that was excluded after randomisation) analyses. This trial is registered at ClinicalTrials.gov, number NCT00349479.FindingsAll 24 hospitals completed the study. Overall, 745 of 40 823 patients with stroke received intravenous alteplase treatment. In the ITT analysis, the proportion of patients with stroke who were admitted and treated with alteplase increased between the pre-intervention and post-intervention periods in intervention hospitals (89 [1·25%] of 7119 patients to 235 [2·79%] of 8419) to a greater extent than in control hospitals (99 [1·25%] of 7946 to 194 [2·10%] of 9222), but the difference between groups was not significant (relative risk [RR] 1·37, 95% CI 0·96-1·93; p=0·08). In the target-population analysis, the increase in alteplase use in intervention hospitals (59 [1·00%] of 5882 to 191 [2·62%] of 7288) was significantly greater than in control hospitals (65 [1·09%] of 5957 to 120 [1·72%] of 6989; RR 1·68, 95% CI 1·09-2·57; p=0·02), but was still clinically modest.InterpretationThe intervention did not significantly increase alteplase use in patients with ischaemic stroke. The increase in use of alteplase in the target population was significant, but smaller than the effect to which the study was powered. Additional strategies to increase acute stroke treatment are needed.FundingNational Institutes of Health National Institute of Neurological Disorders and Stroke.

Recurrent events such as hospitalisations are outcomes that can be used to monitor dialysis facilities' quality of care. However, current methods are not adequate to analyse data from many facilities with multiple hospitalisations, especially when adjustments are needed for multiple time scales. It is also controversial whether direct or indirect standardisation should be used in comparing facilities. This study is motivated by the need of the Centers for Medicare and Medicaid Services to evaluate US dialysis facilities using Medicare claims, which involve almost 8,000 facilities and over 500,000 dialysis patients. This scope is challenging for current statistical software's computational power. We propose a method that has a flexible baseline rate function and is computationally efficient. Additionally, the proposed method shares advantages of both indirect and direct standardisation. The method is evaluated under a range of simulation settings and demonstrates substantially improved computational efficiency over the existing R package survival. Finally, we illustrate the method with an important application to monitoring dialysis facilities in the U.S., while making time-dependent adjustments for the effects of COVID-19. [ABSTRACT FROM AUTHOR]

On average, randomization achieves balance in covariate distributions between treatment groups; yet in practice, chance imbalance exists post randomization, which increases the error in estimating treatment effects. This is an important issue, especially in cluster randomized trials, where the experimental units (the clusters) are highly heterogeneous and relatively few in number. To address this, several restricted randomization designs have been proposed to balance on a few covariates of particular interest. More recently, approaches involving rerandomization have been proposed that aim to achieve simultaneous balance on several important prognostic factors. In this article, we comment on some properties of rerandomized designs and propose a new design for comparing two or more treatments. This design combines optimal nonbipartite matching of the subjects together with rerandomization, both aimed at minimizing a measure of distance between elements in blocks to achieve reductions in the mean squared error of estimated treatment effects. Compared with the existing alternatives, the proposed design can substantially reduce the mean squared error of the estimated treatment effect. This enhanced efficiency is evaluated both theoretically and empirically, and robustness properties are also noted. The design is generalized to three or more treatment arms. [ABSTRACT FROM AUTHOR]

In the context of time‐to‐event analysis, a primary objective is to model the risk of experiencing a particular event in relation to a set of observed predictors. The Concordance Index (C‐Index) is a statistic frequently used in practice to assess how well such models discriminate between various risk levels in a population. However, the properties of conventional C‐Index estimators when applied to left‐truncated time‐to‐event data have not been well studied, despite the fact that left‐truncation is commonly encountered in observational studies. We show that the limiting values of the conventional C‐Index estimators depend on the underlying distribution of truncation times, which is similar to the situation with right‐censoring as discussed in Uno et al. (2011) [On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30(10), 1105–1117]. We develop a new C‐Index estimator based on inverse probability weighting (IPW) that corrects for this limitation, and we generalize this estimator to settings with left‐truncated and right‐censored data. The proposed IPW estimators are highly robust to the underlying truncation distribution and often outperform the conventional methods in terms of bias, mean squared error, and coverage probability. We apply these estimators to evaluate a predictive survival model for mortality among patients with end‐stage renal disease. [ABSTRACT FROM AUTHOR]

Prognostic models are useful tools for assessing a patient's risk of experiencing adverse health events. In practice, these models must be validated before implementation to ensure that they are clinically useful. The concordance index (C‐Index) is a popular statistic that is used for model validation, and it is often applied to models with binary or survival outcome variables. In this paper, we summarize existing criticism of the C‐Index and show that many limitations are accentuated when applied to survival outcomes, and to continuous outcomes more generally. We present several examples that show the challenges in achieving high concordance with survival outcomes, and we argue that the C‐Index is often not clinically meaningful in this setting. We derive a relationship between the concordance probability and the coefficient of determination under an ordinary least squares model with normally distributed predictors, which highlights the limitations of the C‐Index for continuous outcomes. Finally, we recommend existing alternatives that more closely align with common uses of survival models. [ABSTRACT FROM AUTHOR]