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We compare the performance of nonparametric estimators for the mean number of recurrent events and provide a systematic overview for different recurrent event settings. The mean number of recurrent events is an easily interpreted marginal feature often used for treatment comparisons in clinical trials. Incomplete observations, dependencies between successive events, terminating events acting as competing risk, or gaps between at risk periods complicate the estimation. We use survival multistate models to represent different complex recurrent event situations, profiting from recent advances in nonparametric estimation for non‐Markov multistate models, and explain several estimators by using multistate intensity processes, including the common Nelson–Aalen‐type estimators with and without competing mortality. In addition to building on estimation of state occupation probabilities in non‐Markov models, we consider a simple extension of the Nelson–Aalen estimator by allowing for dependence on the number of prior recurrent events. We pay particular attention to the assumptions required for the censoring mechanism, one issue being that some settings require the censoring process to be entirely unrelated while others allow for state‐dependent or event‐driven censoring. We conducted extensive simulation studies to compare the estimators in various complex situations with recurrent events. Our practical example deals with recurrent chronic obstructive pulmonary disease exacerbations in a clinical study, which will also be used to illustrate two‐sample‐inference using resampling. [ABSTRACT FROM AUTHOR]

Purpose: In analyzing pregnancy data concerning drug exposure in the first trimester, the risk of spontaneous abortions is of primary interest. For estimating the cumulative incidence function, the Aalen–Johansen estimator is typically used, and competing risks such as induced abortion and livebirth are considered. However, the delayed study entry can lead to overly small risk sets for the first events. This results in large jumps in the estimated cumulative incidence function of spontaneous abortions or induced abortions using the Aalen–Johansen estimator, and consequently in an overestimation of the probability. Methods: Several approaches account for early overly small risk sets. The first approach is conditioning on the event time being greater than the event time causing the large jump. Second, the events can be ignored by censoring them. Third, the events can be postponed until a large enough number is at risk. These three approaches are compared. Results: All approaches are applied using data of 54 lacosamide‐exposed pregnancies. The Aalen–Johansen estimate of the probability of spontaneous abortion is 22.64%, which is relatively large for only three spontaneous abortions in the dataset. The conditional approach and the ignore approach have an estimated probability of 7.17%. In contrast, the estimate of the postpone approach is 16.45%. In this small sample, bootstrapped confidence intervals seem more accurate. Conclusions: In the analyses of pregnancy data with rare events, the postpone approach is favorable as no events are excluded. However, the approach that ignores early events has the narrowest confidence interval. [ABSTRACT FROM AUTHOR]

Randomized clinical trials with time‐to‐event endpoints are frequently stopped after a prespecified number of events has been observed. This practice leads to dependent data and nonrandom censoring, which can in general not be solved by conditioning on the underlying baseline information. In case of staggered study entry, matters are complicated substantially. The present paper demonstrates that the study design at hand entails general independent censoring in the counting process sense, provided that the analysis is based on study time information only. To illustrate that the filtrations must not use abundant information, we simulated data of event‐driven trials and evaluated them by means of Cox regression models with covariates for the calendar times. The Breslow curves of the cumulative baseline hazard showed considerable deviations, which implies that the analysis is disturbed by conditioning on the calendar time variables. A second simulation study further revealed that Efron's classical bootstrap, unlike the (martingale‐based) wild bootstrap, may lead to biased results in the given setting, as the assumption of random censoring is violated. This is exemplified by an analysis of data on immunotherapy in patients with advanced, previously treated nonsmall cell lung cancer. [ABSTRACT FROM AUTHOR]

Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is "time since treatment initiation", but individuals may have initiated treatment before the start of longitudinal data collection. When the focus is on baseline effects, one widespread approach is to fit a Cox proportional hazards model incorporating the measurements at delayed study entry. This has been criticized because of the potential time dependency of covariates. We tackle this problem by using a Bayesian joint model that combines a mixed-effects model for the longitudinal trajectory with a proportional hazards model for the event of interest incorporating the baseline covariate, possibly unmeasured in the presence of left truncation. The novelty is that our procedure is not used to account for non-continuously monitored longitudinal covariates in right-censored time-to-event studies, but to utilize these trajectories to make inferences about missing baseline measurements in left-truncated data. Simulating times-to-event depending on baseline covariates we also compared our proposal to a simpler two-stage approach which performed favorably. Our approach is illustrated by investigating the impact of baseline blood glucose levels on antidiabetic treatment failure using data from a German diabetes register. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether osteoarthritis (OA)‐specific assessment values (i.e., Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC]) and generic pain and function (visual analog scale, Hanover Functionality Status Questionnaire) measured before and 12 months after arthroplasty are associated with the risk of long‐term mortality in a cohort of patients with advanced OA of the hip or knee. Methods: The Ulm Osteoarthritis Study was a prospective cohort study of OA patients with unilateral total hip or knee replacement between January 1995 and December 1996. Correlation coefficients were calculated to describe the agreement between the different assessments. Mortality was assessed during the follow‐up period (last update July 2019). Cox proportional regression models were used to estimate hazard ratios (HRs) for mortality after adjusting for covariates. Results: Arthroplasty was accompanied by a clear reduction in pain and improved function throughout all assessments in the 706 included patients. The results of the adjusted Cox models showed no relationship between baseline and follow‐up joint‐specific WOMAC assessments and long‐term mortality. However, an independent increased risk of mortality was found with generic function assessments. In the final adjusted model, the HR for the 12‐month follow‐up value was 1.79 (95% confidence interval 1.24–2.60) in the group with clinically relevant impairment versus the reference group. Conclusion: Poor function based on the generic assessment was associated with increased long‐term mortality, suggesting that functional impairments in daily life activities may be more important for long‐term survival than OA‐specific impairments in this patient group. [ABSTRACT FROM AUTHOR]

Recently, treatment interruptions such as a clinical hold in randomized clinical trials have been investigated by using a multistate model approach. The phase III clinical trial START (Stimulating Targeted Antigenic Response To non-small-cell cancer) with primary endpoint overall survival was temporarily placed on hold for enrollment and treatment by the US Food and Drug Administration (FDA). Multistate models provide a flexible framework to account for treatment interruptions induced by a time-dependent external covariate. Extending previous work, we propose a censoring and a filtering approach both aimed at estimating the initial treatment effect on overall survival in the hypothetical situation of no clinical hold. A special focus is on creating a link to causal inference. We show that calculating the matrix of transition probabilities in the multistate model after application of censoring (or filtering) yields the desired causal interpretation. Assumptions in support of the identification of a causal effect by censoring (or filtering) are discussed. Thus, we provide the basis to apply causal censoring (or filtering) in more general settings such as the COVID-19 pandemic. A simulation study demonstrates that both causal censoring and filtering perform favorably compared to a naïve method ignoring the external impact. [ABSTRACT FROM AUTHOR]

Background: The current standard endpoint to assess disability accumulation in multiple sclerosis (MS) clinical trials is the time to the first confirmed disability progression, which excludes subsequent progression events. Including recurrent progression events may permit a more comprehensive assessment of treatment effects on disability progression. Objective: To propose a definition of recurrent disability progression events and to compare time-to-first and recurrent event analysis. Methods: Recurrent disability progression events were defined by expanding the recommended first event definition. Marginal recurrent event methods (negative binomial model, Lin–Wei–Yang–Ying model) were compared with Cox regression in data from three randomized controlled trials in relapsing multiple sclerosis (RMS) and primary progressive multiple sclerosis (PPMS), and in simulated randomized controlled trial data. Results: The recurrent event analyses included a substantially larger number of progression events compared with the time-to-first-event analyses (+7.5% and +9.9% in the RMS trials and +22.7% in the PPMS trial). The increase in the number of events resulted in more precise treatment effect estimates and a corresponding gain in statistical power. Conclusion: Our results support the use of recurrent event data analysis, especially in progressive MS trials, to improve estimates of treatment effects, increase statistical power, and better capture the clinically meaningful long-term disability progression experience. [ABSTRACT FROM AUTHOR]

Incidence of ICU events is mostly measured in one of two ways which differ by the denominator only. Either the number of incident events divided by the number of ICU patients is reported or the number of incident events per 1,000 ICU days is calculated. The difference is relevant, but a connection is rarely made. We give an example where pneumonia diagnosis on admission has no effect on one measure of mortality incidence, but increases the other. We demonstrate how to connect the two measures of incidence. The conclusion is that so-called 'competing incidences' should also be reported., Author(s): Jan Beyersmann [sup.1], Petra Gastmeier [sup.2], Martin Schumacher [sup.3] Author Affiliations: (1) grid.6582.9, 0000000419369748, Institute of Statistics, University of Ulm, , Ulm, Germany (2) grid.6363.0, 0000000122184662, Institute of Hygiene [...]