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CDK4/6 inhibitors are oral agents inhibiting key molecules of the cell cycle regulation. In patients with endocrine receptor positive (ER+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer, the combination of CDK4/6 inhibitors with endocrine therapy is an effective treatment in the metastatic setting. Now, two studies in the adjuvant setting - MonarchE (2 years of abemaciclib) and NATALEE (3 years of ribociclib) - report positive invasive disease-free survival. Here, we re-evaluate these seminal trials. First, an excess drop-out or loss-to-follow up occurred early in the control arms of both studies. Since both trials are open-label, there is concern that the patients who drop-out do not do so at random but based on socioeconomic factors and alternative options. Is it possible that the results merely appear favorable due to loss to follow up? Based on re-constructed Kaplan-Meier curves, we concluded the results of these studies remain fragile, being prone to informative censoring. Secondly, adverse events were notably higher in both trials, and some of them, like COVID-19 related deaths in NATALEE, raise serious concerns. Third, the potential costs associated with CDK4/6 inhibition given as adjuvant therapy are unprecedented. The NATALEE strategy, in particular, could affect up to 35 % of patients with newly diagnosed breast cancer, which is the cancer with the highest incidence worldwide. Without confirmatory data based on a placebo-controlled trial, or better identification of patients that would benefit from the addition of CDK4/6 inhibitors in the adjuvant setting, we argue against their routine use as adjuvant therapy in ER+ /HER2- early breast cancer.

The SWOG S1801 trial investigated the role of pembrolizumab, an anti-PD1 immune checkpoint inhibitor, in the perioperative setting of stage III or IV melanoma. This phase 2 trial compared two groups: one receiving pembrolizumab both before and after surgery (neoadjuvant-adjuvant), and another receiving it only post-surgery (adjuvant-only), with event-free survival (EFS) as the primary endpoint. Neoadjuvant strategies, involving pre-surgical drug administration, potentially offer rapid tumor control and a unique opportunity to assess tumor response. However, they may expose to toxicity and delay or preclude surgery. The study met its primary endpoint, with a 72 % EFS rate in the neoadjuvant-adjuvant group, and 49 % in the adjuvant group. Here, we question the results applicability with three potential limitations. Key concerns include an arbitrary rule in event assignment, possibly affecting the event distribution over time. Second, different rates of early censoring between groups introduce the possibility of informative censoring, which could have led to an artefactual benefit in EFS. Lastly, phase 2 trial results, by definition, carry risk of fluke results, and should be confirmed in phase 3 trial before wide adoption. Collectively, these factors must be integrated into a careful interpretation of the SWOG S1801 trial outcomes. More robust data are needed to fully appraise strengths and limitations of neoadjuvant pembrolizumab in melanoma treatment.

Crossover or treatment-switching in randomized controlled trials presents notable challenges not only in the development and approval of new drugs but also poses a complex issue in their reimbursement, especially in oncology. When the investigational treatment is superior to control, crossover from control to investigational treatment upon disease progression or for other reasons will likely cause the underestimation of treatment benefit. Rank Preserving Structural Failure Time (RPSFT) and Two-Stage Estimation (TSE) methods are commonly employed to adjust for treatment switching by estimating counterfactual survival times. However, these methods may induce informative censoring by adjusting censoring times for switchers while leaving those for non-switchers unchanged. Existing approaches such as re-censoring or inverse probability of censoring weighting (IPCW) are often used alongside RPSFT or TSE to handle informative censoring, but may result in long-term information loss or suffer from model misspecification. In this paper, Kaplan–Meier multiple imputation with bootstrap procedure (KMIB) is proposed to address the informative censoring issues in adjustment methods for treatment switching. This approach can avoid information loss and is robust to model misspecification. In the scenarios that we investigate, simulation studies show that this approach performs better than other adjustment methods when the treatment effect is small, and behave similarly under other scenarios despite different switching probability. A case study in non-small cell lung cancer (NSCLC) is also provided to demonstrate the use of this method. [ABSTRACT FROM AUTHOR]

Illegal killing (poaching) of wildlife is a problem that warrants legitimate and objective investigation. Treves et al. (2017) use data from reported wolf mortalities in Wisconsin to estimate unreported and unobserved mortality and poaching rates of unmonitored wolves. This publication is frequently cited and often used to advocate for various policy recommendations despite nontrivial problems in analysis resulting in incorrect inference. We provide a constructive examination of this work and we identify significant methodological and analytical flaws that lead to consequential but faulty conclusions about levels of annual mortality that wolf populations can sustain. We also contrast the conclusions of Treves et al. (2017) with established literature on wolf demographics and observed changes in the Wisconsin wolf population and conclude that the observed population growth would not have been possible with mortality rates estimated by their publication. [ABSTRACT FROM AUTHOR]

Multivariate interval-censored failure time data occur when a failure time study involves several related failure times of interest and only interval-censored observations are available for each of them. Although a great deal of literature has been established for their regression analysis, there does not seem to exist an approach that applies to the situation where there exist both a cured subgroup and informative censoring, the focus of this paper. For the problem, a class of semiparametric transformation non-mixture cure models is presented and a two-step estimation procedure is proposed. For the implementation of the proposed method, an EM algorithm is developed. Numerical results suggest that the proposed method works well for practical situations and an application is provided. [ABSTRACT FROM AUTHOR]

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness. [ABSTRACT FROM AUTHOR]

Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, and assuming censoring and failure time are independent within covariate strata. In yet other instances, events may be censored by other competing events like death and are associated with censoring possibly through prognoses. Realistically, measured covariates can rarely capture all such associations with absolute certainty. In cases of dependent censoring, covariate measurements are often, at best, proxies of underlying prognoses. In this article, we establish a nonparametric identification framework by formally admitting that conditional independent censoring may fail in practice and accounting for covariate measurements as imperfect proxies of underlying association. The framework suggests adaptive estimators, and we provide generic assumptions under which they are consistent, asymptotically normal, and doubly robust. We examine the finite-sample performance of our proposed estimators via a Monte Carlo simulation and apply them to the SEER-Medicare dataset. [ABSTRACT FROM AUTHOR]

Background: The ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study. Methods: We adopt the estimand framework in the context of a study using "trial emulation" to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A "while on treatment" strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis. Results: The primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p < 0.001). For the sensitivity analysis, when we apply the "jump to off prophylaxis" approach, the hazard ratio is almost the same compared to that from the primary analysis (HR 0.80 [0.69, 0.95], p = 0.009). The sensitivity analysis confirmed that the new regime exhibits a clear improvement over the existing guidelines for PcP prophylaxis when those lost to follow-up "jump to off prophylaxis". Conclusions: Our application using reference based multiple imputation demonstrates the method's flexibility and simplicity for sensitivity analyses in the context of the estimand framework for (emulated) trials. [ABSTRACT FROM AUTHOR]

Abstract Background The ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study. Methods We adopt the estimand framework in the context of a study using “trial emulation” to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A “while on treatment” strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis. Results The primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p

Abstract.The choice of the censoring mechanism is very important when we are analyzing survival data because, in some practical situations, there is a dependence between the failure and censoring times, contradicting the assumption of non informative censoring. The present article proposes a censored-data regression in the presence of informative censoring based on the generalized odd log-logistic family of distributions under two systematic components. The proposed model is based on the assumption that failure and censoring times are conditionally independent given a frailty. The choice of the widespread odd log-logistic family for failure and censoring times is justified by the fact that it generalizes some distributions already consolidated in the area of survival and, concomitantly, by the fact that it offers great flexibility in data modeling in practice, in addition to the capacity to present various forms of the risk function, among them, bimodal. The maximum likelihood method is used to estimate the model parameters. For different parameter configurations and sample sizes, some simulations are performed to analyze the behavior of the maximum likelihood estimates. Two real data applications illustrate the usefulness of the proposed survival regression with informative censoring. [ABSTRACT FROM AUTHOR]

The KRAS oncogene is present in up to 25% of solid tumors and for decades had been undruggable. Sotorasib was the first-in-class KRAS inhibitor to reach the US and European market, and its pharmacological inhibition is restricted to the KRAS p.G12C mutation. Sotorasib showed activity (tumor shrinkage) in patients with non-small cell lung cancer harboring this specific mutation, and efficacy was tested in the CodeBreaK 200, open-label, phase 3 trial (NCT04303780). The results were presented in the ESMO 2022 meeting. CodeBreaK 200 found an improvement in the primary endpoint of progression-free survival (PFS), but overall survival, a key secondary endpoint, was not improved. However, critical questions about the trial's design may limit inferences regarding the reported results. The control arm treatment was inferior to the best standard of care. A late protocol modification (which lowered the sample size and allowed a problematic crossover) prohibited the trial from making a determination regarding overall survival. Imbalance in censoring rates, with potential informative censoring, makes PFS estimates unreliable. Quality-of-life data were also limited. Ultimately, CodeBreaK 200 does not clarify how this therapy should be used in practice, and while we maintain cautious enthusiasm for this and other Ras inhibitors, we await more informative trials.

Abtract: The SWOG S1801 trial investigated the role of pembrolizumab, an anti-PD1 immune checkpoint inhibitor, in the perioperative setting of stage III or IV melanoma. This phase 2 trial compared two groups: one receiving pembrolizumab both before and after surgery (neoadjuvant-adjuvant), and another receiving it only post-surgery (adjuvant-only), with event-free survival (EFS) as the primary endpoint. Neoadjuvant strategies, involving pre-surgical drug administration, potentially offer rapid tumor control and a unique opportunity to assess tumor response. However, they may expose to toxicity and delay or preclude surgery. The study met its primary endpoint, with a 72 % EFS rate in the neoadjuvant–adjuvant group, and 49 % in the adjuvant group. Here, we question the results’ applicability with three potential limitations. Key concerns include an arbitrary rule in event assignment, possibly affecting the event distribution over time. Second, different rates of early censoring between groups introduce the possibility of informative censoring, which could have led to an artefactual benefit in EFS. Lastly, phase 2 trial results, by definition, carry risk of fluke results, and should be confirmed in phase 3 trial before wide adoption. Collectively, these factors must be integrated into a careful interpretation of the SWOG S1801 trial outcomes. More robust data are needed to fully appraise strengths and limitations of neoadjuvant pembrolizumab in melanoma treatment.

In non-inferiority (NI) trials with time-to-event data, different types and patterns of censoring may occur, but their impact on trial results is not entirely clear. We investigated the influence of informative and non-informative censoring by conducting extensive simulation studies under the assumption that the NI margin is defined as a maximum acceptable hazard ratio and scenarios typically observed in recent NI trials. We found that while non-informative censoring tends to only affect the power, informative censoring can impact the treatment effect estimates, type I error rate, and power. The magnitude of these effects depends on the between-group differences in the failure and informative censoring risks, as well as the correlation between censoring and failure times, among other factors. The adverse impact of informative censoring was generally decreased with larger NI margins. [ABSTRACT FROM AUTHOR]

In analysis of time-to-event data, subjects prematurely discontinuing from the assigned treatment prior to experiencing an event of interest are often handled by noninformative censoring under censor-at-random assumption. Such methods can be challenged with respect to the robustness of the ignorable or noninformative censoring and sensitivity analyses using informative censoring are often required. In this article, we propose a class of reference-based methods (including Jump to Reference and Copy Reference) and tipping point analysis for time-to-event data with possibly informative censoring. Particularly, the Copy Reference method is a novel method to fit the gap in literature for time-to-event analysis. We further describe and facilitate the implementation of these methods by multiple imputation. An illustrative example is provided to demonstrate the reference-based methods and tipping point analysis. [ABSTRACT FROM AUTHOR]

We have explored several statistical approaches to impute missing time-to-event data that arise from outcome trials with relatively long follow-up periods. Aligning with the primary estimand, such analyses evaluate the robustness of results by imposing an assumption different from censoring at random (CAR). Although there have been debates over which assumption and which method is more appropriate to be applied to the imputation, we propose to use the collection of retrieved dropouts as the basis of missing data imputation. As retrieved dropouts share a similar disposition, such as treatment discontinuation, with subjects who have missing data, they can reasonably be assumed to characterize the distribution of time-to-event among subjects with missing data. In terms of computational intensity and robustness to violation of underlying distributional assumption, we have compared parametric approaches via MCMC or MLE multivariate sampling procedures to a non-parametric bootstrap approach with respect to baseline hazard function. Each of these approaches follows a process of multiple imputation ("proper imputations"), analysis of complete datasets, and final combination. The type-I error, and power rates are examined under a wide range of scenarios to inform the performance characteristics. A subset of a real unblinded phase III CVOT is used to demonstrate the application of the proposed approaches, compared to the Cox proportional hazards model and jump-to-reference multiple imputation. [ABSTRACT FROM AUTHOR]

Single-index models have gained increased popularity in time-to-event analysis owing to their model flexibility and advantage in dimension reduction. We propose a semiparametric framework for the rate function of a recurrent event counting process by modelling its size and shape components with single-index models. With additional monotone constraints on the two link functions for the size and shape components, the proposed model possesses the desired directional interpretability of covariate effects and encompasses many commonly used models as special cases. To tackle the analytical challenges arising from leaving the two link functions unspecified, we develop a two-step rank-based estimation procedure to estimate the regression parameters with or without informative censoring. The proposed estimators are asymptotically normal, with a root-n convergence rate. To guide model selection, we develop hypothesis testing procedures for checking shape and size independence. Simulation studies and a data example on a hematopoietic stem cell transplantation study are presented to illustrate the proposed methodology.

Objective We aimed to systematically characterize reporting missing quality of life (QoL) data in oncology randomized controlled trials (RCTs) and to estimate prevalence of adequate reporting according to existing guidelines. Study Design and Setting This cross-sectional analysis includes all articles on anti-cancer drugs tested in RCTs in six high impact medical/oncology journals, published between January 2015 and May 2020, that reported QoL outcomes. From 1942 identified articles, 215 (11%) met inclusion criteria. Data abstracted included whether compliance for QoL assessment were reported, whether results from a missing data statistical analysis were reported, whether articles met current recommendations for reporting missing data in QoL assessments. Results The results from a missing data statistical analysis were available in 22 trials (10.2%). Overall, 16 trials (7.4%) met current recommendations for reporting missing data in QoL assessments. Articles specifically reporting on QoL or patient reported outcomes were more likely to meet recommendations than other reports (P < 0.0001). Conclusion This systematic cross-sectional study found that most oncology RCTs reporting on QoL do not report adequately on missing data in QoL, with only 7.4% of trials meeting current reporting guidelines. The possibility of informative censoring, therefore, cannot be assessed in most of trials.

Estimation of compiler causal treatment effects has been discussed by many authors under different situations but only limited literature exists for interval-censored failure time data, which often occur in many areas such as longitudinal or periodical follow-up studies. Particularly it does not seem to exist a method that can deal with informative interval censoring, which can happen naturally and make the analysis much more challenging. Also, it has been shown that when the informative censoring exists, the analysis without taking it into account would yield biased or misleading results. To address this, we propose an estimated sieve maximum likelihood approach with the use of instrumental variables. The asymptotic properties of the resulting estimators of regression parameters are established, and a simulation study is performed and suggests that it works well. Finally, it is applied to a set of real data that motivated this study. [ABSTRACT FROM AUTHOR]

Background: The net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework. Methods: The net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study. Results: Our simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy. Conclusions: We proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring. [ABSTRACT FROM AUTHOR]

Ignoring the presence of dependent censoring in data analysis can lead to biased estimates, for example, not considering the effect of abandonment of the tuberculosis treatment may influence inferences about the cure probability. In order to assess the relationship between cure and abandonment outcomes, we propose a copula Bayesian approach. Therefore, the main objective of this work is to introduce a Bayesian survival regression model, capable of taking into account the dependent censoring in the adjustment. So, this proposed approach is based on Clayton's copula, to provide the relation between survival and dependent censoring times. In addition, the Weibull and the piecewise exponential marginal distributions are considered in order to fit the times. A simulation study is carried out to perform comparisons between different scenarios of dependence, different specifications of prior distributions, and comparisons with the maximum likelihood inference. Finally, we apply the proposed approach to a tuberculosis treatment adherence dataset of an HIV cohort from Alvorada‐RS, Brazil. Results show that cure and abandonment outcomes are negatively correlated, that is, as long as the chance of abandoning the treatment increases, the chance of tuberculosis cure decreases. [ABSTRACT FROM AUTHOR]

The Kaplan–Meier estimator is ubiquitously used to estimate survival probabilities for time-to-event data. It is nonparametric, and thus does not require specification of a survival distribution, but it does assume that the risk set at any time t consists of independent observations. This assumption does not hold for data from paired organ systems such as occur in ophthalmology (eyes) or otolaryngology (ears), or for other types of clustered data. In this article, we estimate marginal survival probabilities in the setting of clustered data, and provide confidence limits for these estimates with intra-cluster correlation accounted for by an interval-censored version of the Clayton–Oakes model. We develop a goodness-of-fit test for general bivariate interval-censored data and apply it to the proposed interval-censored version of the Clayton–Oakes model. We also propose a likelihood ratio test for the comparison of survival distributions between two groups in the setting of clustered data under the assumption of a constant between-group hazard ratio. This methodology can be used both for balanced and unbalanced cluster sizes, and also when the cluster size is informative. We compare our test to the ordinary log rank test and the Lin-Wei (LW) test based on the marginal Cox proportional Hazards model with robust standard errors obtained from the sandwich estimator. Simulation results indicate that the ordinary log rank test over-inflates type I error, while the proposed unconditional likelihood ratio test has appropriate type I error and higher power than the LW test. The method is demonstrated in real examples from the Sorbinil Retinopathy Trial, and the Age-Related Macular Degeneration Study. Raw data from these two trials are provided. [ABSTRACT FROM AUTHOR]

Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In addition to censoring, truncating is also very common in practice. This paper examines the regression analysis of current status data with informative censoring times, considering the presence of left truncation. In addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based approach is used to describe the relationship between the failure time of interest and the censoring time. The spline function is employed to approximate the unknown nonparametric function. We have established the asymptotic properties of the proposed estimator. Simulation studies suggest that the developed procedure works well in practice. We also applied the developed method to a real dataset derived from an AIDS cohort research. [ABSTRACT FROM AUTHOR]

IntroductionKaplan-Meier survival analysis, the cornerstone of evaluating efficacy of oncology drugs in randomised controlled trials (RCTs), assumes censored patients are neither healthier nor sicker than those followed. We sought to examine whether censoring patterns differ between the control and experimental arms in one oncology journal that mandates the reporting of the number of patients censored.MethodsIn this retrospective review, proportion of censoring and study design data were gathered from RCTs published in The Lancet Oncology that reported Kaplan-Meier curves between May 2018 and August 2019. Differential censoring rates were analysed at the 1st, 3rd, 6th, and overall time points in each study. Analysis was stratified by curves reporting progression-free survival (PFS) or overall survival (OS) end-points.ResultsOf the 160 articles reviewed, 29 studies with 51 Kaplan-Meier curves were eligible. In both OS (N = 25) and PFS curves (N = 26), the absolute weighted difference in censoring between the control and experimental arms was initially positive, indicating more censoring in the control arm (first time point OS: 0.32%; PFS: 2.00%). The absolute difference then became negative, indicating more censoring in the experimental arm as time progressed (end-of-study OS: -7.54%; PFS: -9.09%).ConclusionDifferences in censoring between control and experimental arms of cancer RCTs suggest that there could be systematic bias present at various study time points that may influence key results. Further investigation is needed, as possible reasons include study assignment disappointment, inappropriate follow-up length, lack of efficacy, or intolerable toxicity, each predominant at specific time points after randomisation.

We address the problem of how COVID-19 deaths observed in an oncology clinical trial can be consistently taken into account in typical survival estimates. We refer to oncological patients since there is empirical evidence of strong correlation between COVID-19 and cancer deaths, which implies that COVID-19 deaths cannot be treated simply as non-informative censoring, a property usually required by the classical survival estimators. We consider the problem in the framework of the widely used Kaplan–Meier (KM) estimator. Through a counterfactual approach, an algorithmic method is developed allowing to include COVID-19 deaths in the observed data by mean-imputation. The procedure can be seen in the class of the Expectation-Maximization (EM) algorithms and will be referred to as Covid-Death Mean-Imputation (CoDMI) algorithm. We discuss the CoDMI underlying assumptions and the convergence issue. The algorithm provides a completed lifetime data set, where each Covid-death time is replaced by a point estimate of the corresponding virtual lifetime. This complete data set is naturally equipped with the corresponding KM survival function estimate and all available statistical tools can be applied to these data. However, mean-imputation requires an increased variance of the estimates. We then propose a natural extension of the classical Greenwood’s formula, thus obtaining expanded confidence intervals for the survival function estimate. To illustrate how the algorithm works, CoDMI is applied to real medical data extended by the addition of artificial Covid-death observations. The results are compared with the estimates provided by the two naïve approaches which count COVID-19 deaths as censoring or as deaths by the disease under study. In order to evaluate the predictive performances of CoDMI an extensive simulation study is carried out. The results indicate that in the simulated scenarios CoDMI is roughly unbiased and outperforms the estimates obtained by the naïve approaches. A user-friendly version of CoDMI programmed in R is freely available.

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity. For the situation, another issue that may occur is that the observation time may be correlated with the interested failure time, which is often referred to as informative censoring or observation times. It is well-known that in the presence of informative censoring, the analysis that ignores it could yield biased or even misleading results. In this paper, the authors consider such data and propose a frailty-based inference procedure. In particular, an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established. The numerical results show that the proposed method works well in practice and an application to a set of real data is provided. [ABSTRACT FROM AUTHOR]

We consider regression analysis of longitudinal data in the presence of outcome-dependent observation times and informative censoring. Existing approaches commonly require correct specification of the joint distribution of the longitudinal measurements, observation time process and informative censoring time under the joint modeling framework, and can be computationally cumbersome due to the complex form of the likelihood function. In view of these issues, we propose a semi-parametric joint regression model and construct a composite likelihood function based on a conditional order statistics argument. As a major feature of our proposed methods, the aforementioned joint distribution is not required to be specified and the random effect in the proposed joint model is treated as a nuisance parameter. Consequently, the derived composite likelihood bypasses the need to integrate over the random effect and offers the advantage of easy computation. We show that the resulting estimators are consistent and asymptotically normal. We use simulation studies to evaluate the finite-sample performance of the proposed method, and apply it to a study of weight loss data that motivated our investigation.

We consider regression analysis of longitudinal data in the presence of outcome-dependent observation times and informative censoring. Existing approaches commonly require correct specification of the joint distribution of the longitudinal measurements, observation time process and informative censoring time under the joint modeling framework, and can be computationally cumbersome due to the complex form of the likelihood function. In view of these issues, we propose a semi-parametric joint regression model and construct a composite likelihood function based on a conditional order statistics argument. As a major feature of our proposed methods, the aforementioned joint distribution is not required to be specified and the random effect in the proposed joint model is treated as a nuisance parameter. Consequently, the derived composite likelihood bypasses the need to integrate over the random effect and offers the advantage of easy computation. We show that the resulting estimators are consistent and asymptotically normal. We use simulation studies to evaluate the finite-sample performance of the proposed method, and apply it to a study of weight loss data that motivated our investigation.

We address the problem of how COVID-19 deaths observed in an oncology clinical trial can be consistently taken into account in typical survival estimates. We refer to oncological patients since there is empirical evidence of strong correlation between COVID-19 and cancer deaths, which implies that COVID-19 deaths cannot be treated simply as non-informative censoring, a property usually required by the classical survival estimators. We consider the problem in the framework of the widely used Kaplan–Meier (KM) estimator. Through a counterfactual approach, an algorithmic method is developed allowing to include COVID-19 deaths in the observed data by mean-imputation. The procedure can be seen in the class of the Expectation-Maximization (EM) algorithms and will be referred to as Covid-Death Mean-Imputation (CoDMI) algorithm. We discuss the CoDMI underlying assumptions and the convergence issue. The algorithm provides a completed lifetime data set, where each Covid-death time is replaced by a point estimate of the corresponding virtual lifetime. This complete data set is naturally equipped with the corresponding KM survival function estimate and all available statistical tools can be applied to these data. However, mean-imputation requires an increased variance of the estimates. We then propose a natural extension of the classical Greenwood's formula, thus obtaining expanded confidence intervals for the survival function estimate. To illustrate how the algorithm works, CoDMI is applied to real medical data extended by the addition of artificial Covid-death observations. The results are compared with the estimates provided by the two naïve approaches which count COVID-19 deaths as censoring or as deaths by the disease under study. In order to evaluate the predictive performances of CoDMI an extensive simulation study is carried out. The results indicate that in the simulated scenarios CoDMI is roughly unbiased and outperforms the estimates obtained by the naïve approaches. A user-friendly version of CoDMI programmed in R is freely available. [ABSTRACT FROM AUTHOR]

Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is based on a joint frailty modeling approach with multivariate random effects to account for the heterogeneous risk of failure and the presence of informative censoring due to a terminal event. We develop a generalized linear mixed model method for the efficient estimation of parameters. We demonstrate the capacity of our approach using a real cancer registry data set on the multimorbidity of melanoma patients and document the relative performance of the proposed joint frailty model to the natural competitor of a standard frailty model via extensive simulation studies. Our new approach is timely to advance evidence-based knowledge to address increasingly complex needs related to multimorbidity and develop interventions that are most effective and viable to better help a large number of individuals with multiple conditions. [ABSTRACT FROM AUTHOR]

Recurrent event and failure time data arise frequently in many clinical and observational studies. In this article, we propose a joint modeling of generalized scale-change models for the recurrent event process and the failure time, and allow the two processes to be correlated through a shared frailty. The proposed joint model is flexible in that it requires neither the Poisson assumption for the recurrent event process nor a parametric assumption on the frailty distribution. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. Simulation studies are conducted to evaluate the finite sample performances of the proposed method. An application to a medical cost study of chronic heart failure patients is provided. [ABSTRACT FROM AUTHOR]

The KRAS oncogene is present in up to 25% of solid tumors and for decades had been undruggable. Sotorasib was the first-in-class KRAS inhibitor to reach the US and European market, and its pharmacological inhibition is restricted to the KRAS p.G12C mutation. Sotorasib showed activity (tumor shrinkage) in patients with non-small cell lung cancer harboring this specific mutation, and efficacy was tested in the CodeBreaK 200, open-label, phase 3 trial (NCT04303780). The results were presented in the ESMO 2022 meeting. CodeBreaK 200 found an improvement in the primary endpoint of progression-free survival (PFS), but overall survival, a key secondary endpoint, was not improved. However, critical questions about the trial's design may limit inferences regarding the reported results. The control arm treatment was inferior to the best standard of care. A late protocol modification (which lowered the sample size and allowed a problematic crossover) prohibited the trial from making a determination regarding overall survival. Imbalance in censoring rates, with potential informative censoring, makes PFS estimates unreliable. Quality-of-life data were also limited. Ultimately, CodeBreaK 200 does not clarify how this therapy should be used in practice, and while we maintain cautious enthusiasm for this and other Ras inhibitors, we await more informative trials

Populations of free-ranging dogs are still a matter of concern in developing countries. The presence of stray dogs is associated with environmental and public health consequences such as the spread of zoonotic diseases. Therefore, public health managers base the promotion of public health on sanitary measures, including the control of the free-ranging dogs' population. In this context, it is necessary to evaluate the free-ranging dogs' life dynamics, taking into account all characteristics of the data, including long-term survival. In long-term studies, some causes of censoring are generally falsely assumed to be independent, leading to bias neglected. Therefore, we propose a likelihood-based approach for long-term clustered survival data, which is suitable to accommodate the dependent censoring. The association between lifetimes and dependent censoring is accommodated through the conditional approach of the frailty models. The marginal distributions can be adjusted assuming Weibull or piecewise exponential distributions, respectively. A Monte Carlo Expectation–Maximization algorithm is developed to estimate the proposed estimators. The simulation study results show a small relative bias and coverage probability near to the nominal level, indicating that the proposed approach works well. Moreover, the model identifiability is assured once data has a cluster structure. Finally, we analyze the survival times of free-ranging dogs from the West Bengal, India, collected between 2010 to 2015, and conclude that survival time (death due to natural cause) is negatively correlated to dependent censoring (missing cause). [ABSTRACT FROM AUTHOR]

In this article, we discuss the regression analysis of dependent current status data under the accelerated failure time model. There exist many literatures discussing the regression analysis of current status data under different models, but few literature discussing the regression problem of dependent current status data under the AFT model. Corresponding to this, we propose a sieve maximum likelihood approach for estimation of covariate effects. In the approach, we model the correlation between the interested survival time and the observation time by the copula function. Simulation study is conducted in order to assess the finite sample behavior of the method. A real data example is provided to illustrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, and the only information obtained is whether the event of interest happened at the moment of observation. In addition to censoring, truncating is also very common in practice. This paper examines the regression analysis of current status data with informative censoring times, considering the presence of left truncation. In addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based approach is used to describe the relationship between the failure time of interest and the censoring time. The spline function is employed to approximate the unknown nonparametric function. We have established the asymptotic properties of the proposed estimator. Simulation studies suggest that the developed procedure works well in practice. We also applied the developed method to a real dataset derived from an AIDS cohort research.

We consider a regression analysis of multivariate interval-censored failure time data where the censoring may be informative. To address this, an approximated maximum likelihood estimation approach is proposed under a general class of semiparametric transformation models, and in the method, the frailty approach is employed to characterize the informative interval censoring. For the implementation of the proposed method, we develop a novel EM algorithm and show that the resulting estimators of the regression parameters are consistent and asymptotically normal. To evaluate the empirical performance of the proposed estimation procedure, we conduct a simulation study, and the results indicate that it performs well for the situations considered. In addition, we apply the proposed approach to a set of real data arising from an AIDS study. [ABSTRACT FROM AUTHOR]

This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring. For the problem, a sieve maximum likelihood estimation approach is proposed and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process. Also I-spline functions are used to approximate the unknown functions in the model, and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations. In addition, an illustrative example is provided. [ABSTRACT FROM AUTHOR]

Health-related quality-of-life (HRQoL) data are central to capturing the quality of patients' life, while endpoints like overall survival (OS) focus on the quantity of life. When analyzing HRQoL data gathered from patients in a randomized trial, a key consideration is the completion rate – indicating the proportion of patients remaining in the trial and with completed questionnaires. When completion rates are disproportionately low in one treatment arm, one likely explanation is that patients who did not complete questionnaires suffered more from toxicities, negatively impacting their HRQoL. This is likely the case when low completion rates occur in the more toxic arm within a randomized trial. If the HRQoL analysis is run as a complete-case analysis – only considering patients without missing data – a decrement in HRQoL can be missed. Conversely, when completion rates are high, the HRQoL data are thought to be more reliable, and informative censoring is less likely. We describe why this reasoning can be inadequate. In trials where high and imbalanced rates of early censoring affect progression-free survival or OS endpoints, the completion rates only apply to the fraction of patients remaining in the trial. In those, HRQoL results should be considered with caution, and reasons for censoring in the primary time-to-event analyses should be explored before making definite conclusions about HRQoL. This is even more relevant in trials with non-inferiority design, where a benefit in HRQoL could be used as a justification to modify practice. • Missing Health-Related quality-of-life (HRQoL) data may be informative. • This happens when patients suffering from added toxicity do not complete the forms. • Completion may be high in trials with high early censoring rates in PFS and OS. • In those, informative censoring in HRQoL could occur even with high completion. • This is particularly relevant when HRQoL data are a key endpoint within a trial. [ABSTRACT FROM AUTHOR]

The semiparametric accelerated failure time (AFT) model linearly relates the logarithm of the failure time to a set of covariates, while leaving the error distribution unspecified. This model has been widely investigated in survival literature due to its simple interpretation and relationship with linear models. However, there has been much less focus on developing AFT‐type linear regression methods for analyzing competing risks data, in which patients can potentially experience one of multiple failure causes. In this article, we propose a simple least‐squares (LS) linear regression model for a cause‐specific subdistribution function, where the conventional LS equation is modified to account for data incompleteness under competing risks. The proposed estimators are shown to be consistent and asymptotically normal with consistent estimation of the variance‐covariance matrix. We further extend the proposed methodology to risk prediction and analysis under clustered competing risks scenario. Simulation studies suggest that the proposed method provides rapid and valid statistical inferences and predictions. Application of our method to two oncology datasets demonstrate its utility in routine clinical data analysis. [ABSTRACT FROM AUTHOR]

CDK4/6 inhibitors are oral agents inhibiting key molecules of the cell cycle regulation. In patients with endocrine receptor positive (ER+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer, the combination of CDK4/6 inhibitors with endocrine therapy is an effective treatment in the metastatic setting. Now, two studies in the adjuvant setting - MonarchE (2 years of abemaciclib) and NATALEE (3 years of ribociclib) - report positive invasive disease-free survival. Here, we re-evaluate these seminal trials. First, an excess drop-out or loss-to-follow up occurred early in the control arms of both studies. Since both trials are open-label, there is concern that the patients who drop-out do not do so at random but based on socioeconomic factors and alternative options. Is it possible that the results merely appear favorable due to loss to follow up? Based on re-constructed Kaplan-Meier curves, we concluded the results of these studies remain fragile, being prone to informative censoring. Secondly, adverse events were notably higher in both trials, and some of them, like COVID-19 related deaths in NATALEE, raise serious concerns. Third, the potential costs associated with CDK4/6 inhibition given as adjuvant therapy are unprecedented. The NATALEE strategy, in particular, could affect up to 35 % of patients with newly diagnosed breast cancer, which is the cancer with the highest incidence worldwide. Without confirmatory data based on a placebo-controlled trial, or better identification of patients that would benefit from the addition of CDK4/6 inhibitors in the adjuvant setting, we argue against their routine use as adjuvant therapy in ER+ /HER2- early breast cancer., Competing Interests: Declaration of Competing Interest Dr Vinay Prasad reported receiving research funding from Arnold Ventures LLC through a grant made to UCSF; royalties for books and writing from Johns Hopkins Press, MedPage, and the Free Press; and consulting fees from UnitedHealthcare and OptumRX. He also reported receiving revenue from Patreon, YouTube, and Substack for the podcasts Plenary Session, VPZD, and Sensible Medicine; for the newsletters Sensible Medicine, The Drug Development Letter, and VP’s Observations and Thoughts; and for the YouTube channel Vinay Prasad MD MPH. All other authors have no conflicts of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The SWOG S1801 trial investigated the role of pembrolizumab, an anti-PD1 immune checkpoint inhibitor, in the perioperative setting of stage III or IV melanoma. This phase 2 trial compared two groups: one receiving pembrolizumab both before and after surgery (neoadjuvant-adjuvant), and another receiving it only post-surgery (adjuvant-only), with event-free survival (EFS) as the primary endpoint. Neoadjuvant strategies, involving pre-surgical drug administration, potentially offer rapid tumor control and a unique opportunity to assess tumor response. However, they may expose to toxicity and delay or preclude surgery. The study met its primary endpoint, with a 72 % EFS rate in the neoadjuvant-adjuvant group, and 49 % in the adjuvant group. Here, we question the results' applicability with three potential limitations. Key concerns include an arbitrary rule in event assignment, possibly affecting the event distribution over time. Second, different rates of early censoring between groups introduce the possibility of informative censoring, which could have led to an artefactual benefit in EFS. Lastly, phase 2 trial results, by definition, carry risk of fluke results, and should be confirmed in phase 3 trial before wide adoption. Collectively, these factors must be integrated into a careful interpretation of the SWOG S1801 trial outcomes. More robust data are needed to fully appraise strengths and limitations of neoadjuvant pembrolizumab in melanoma treatment., Competing Interests: Declaration of competing interest Dr Vinay Prasad reported receiving research funding from Arnold Ventures LLC through a grant made to UCSF; royalties for books and writing from Johns Hopkins Press, MedPage, and the Free Press; and consulting fees from UnitedHealthcare and OptumRX. He also reported receiving revenue from Patreon, YouTube, and Substack for the podcasts Plenary Session, VPZD, and Sensible Medicine; for the newsletters Sensible Medicine, The Drug Development Letter, and VP's Observations and Thoughts; and for the YouTube channel Vinay Prasad MD MPH. Dr Timothée Olivier has no conflicts of interest to declare., (Copyright © 2024. Published by Elsevier Inc.)

Inverse probability of censoring weights (IPCWs) may reduce selection bias due to informative censoring in longitudinal studies. However, in studies with an active comparator, the associations between predictors and censoring may differ across treatment groups. We used the clinical example of anticoagulation treatment with warfarin or a direct oral anticoagulant (DOAC) in atrial fibrillation to illustrate this. The cohort of individuals initiating an oral anticoagulant during 2010–2016 was identified from the Régie de l'assurance maladie du Québec (RAMQ) databases. The parameter of interest was the hazard ratio (HR) of the composite of stroke, major bleeding, myocardial infarction, or death associated with continuous use of warfarin versus DOACs. Two strategies for the specification of the model for estimation of censoring weights were explored: exposure-unstratified and exposure-stratified. The HR associated with continuous treatment with warfarin versus DOACs adjusted with exposure-stratified IPCWs was 1.26 (95% confidence interval: 1.20, 1.33). Using exposure-unstratified IPCWs, the HR differed by 15% in favor of DOACs (1.41, 95% confidence interval: 1.34, 1.48). Not accounting for the different associations between the predictors and informative censoring across exposure groups may lead to misspecification of censoring weights and biased estimate on comparative effectiveness and safety. [ABSTRACT FROM AUTHOR]

The primary analysis of time‐to‐event data typically makes the censoring at random assumption, that is, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved. In such cases, we need to explore the robustness of inference to more pragmatic assumptions about patients post‐censoring in sensitivity analyses. Reference‐based multiple imputation, which avoids analysts explicitly specifying the parameters of the unobserved data distribution, has proved attractive to researchers. Building on results for longitudinal continuous data, we show that inference using a Tobit regression imputation model for reference‐based sensitivity analysis with right censored log normal data is information anchored, meaning the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. We illustrate our theoretical results using simulation and a clinical trial case study. [ABSTRACT FROM AUTHOR]

The additive hazards model is one of the most commonly used models for regression analysis of failure time data and many inference procedures have been developed for it under various situations. In particular, Wang et al. (2018a, Computational Statistics and Data Analysis, 125, 1–9) discussed the situation where one observes informatively interval‐censored data and proposed a likelihood estimation approach. However , it involves estimation of the unknown baseline cumulative hazard function and thus may be time‐consuming. Corresponding to this, we propose two new procedures, an estimating equation‐based one and an empirical likelihood‐based one, and both do not need estimation of the cumulative hazard function and can be easily implemented. The asymptotic properties of the proposed methods are established and an extensive simulation study suggests that they work well in practical situations. An application is also provided. [ABSTRACT FROM AUTHOR]

Methodologies developed for left‐truncated right‐censored failure time data can mostly be categorized according to the assumption imposed on the truncation distribution, i.e., being completely unknown or completely known. While the former approach enjoys robustness, the latter is more efficient when the assumed form of the truncation distribution can be supported by the data. Motivated by data from an HIV/AIDS study, we consider the middle ground and develop methodologies for estimation of a regression function in a semiparametric setting where the truncation distribution is parametrically specified while the failure time, censoring and covariate distribution are left completely unknown. We devise an estimator for the regression function based on a local pseudo‐likelihood approach that properly accounts for the bias induced on the response variable and covariate(s) by the sampling design. One important spin‐off from these results is that they yield the adjustment for length‐biased sampling and right‐censoring; the so‐called stationary case. We study the small and large sample behaviour of our estimators. The proposed method is then applied to analyze a set of HIV/AIDS data. [ABSTRACT FROM AUTHOR]