A semiparametric joint model for terminal trend of quality of life and survival in palliative care research

Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research

Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inferences because it can account for the associations between those two types of data. Modeling quality of life on a retrospective from death time scale is useful for investigators to interpret the analysis results of palliative care studies which have relatively short life expectancies. However, informative censoring remains a complex challenge for modeling quality of life on the retrospective time scale although it has been addressed for joint models on the prospective time scale. To fill this gap, we develop a novel joint modeling approach that can address the challenge by allowing informative censoring events to be dependent on patients' quality of life and survival through a random effect. There are two sub-models in our approach: a linear mixed effect model for the longitudinal quality of life and a competing-risk model for the death time and dropout time that share the same random effect as the longitudinal model. Our approach can provide unbiased estimates for parameters of interest by appropriately modeling the informative censoring time. Model performance is assessed with a simulation study and compared with existing approaches. A real-world study is presented to illustrate the application of the new approach.

Palliative clinical trials in advanced chronic liver disease: Challenges and opportunities

Patients with advanced chronic liver disease have a complex symptom burden and many are not candidates for curative therapy. Despite this, provision of palliative interventions remains woefully inadequate, with an insufficient evidence base being a contributory factor. Designing and conducting palliative interventional trials in advanced chronic liver disease remains challenging for a multitude of reasons. In this manuscript we review past and ongoing palliative interventional trials. We identify barriers and facilitators and offer guidance on addressing these challenges. We hope that this will reduce the inequity in palliative care provision in advanced chronic liver disease.

Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial

PURPOSE

A joint modeling approach is recommended for analysis of longitudinal health-related quality of life (HRQoL) data in the presence of potentially informative dropouts. However, the linear mixed model modeling the longitudinal HRQoL outcome in a joint model often assumes a linear trajectory over time, an oversimplification that can lead to incorrect results. Our aim was to demonstrate that a more flexible model gives more reliable and complete results without complicating their interpretation.

METHODS

Five dimensions of HRQoL in patients with esophageal cancer from the randomized clinical trial PRODIGE 5/ACCORD 17 were analyzed. Joint models assuming linear or spline-based HRQoL trajectories were applied and compared in terms of interpretation of results, graphical representation, and goodness of fit.

RESULTS

Spline-based models allowed arm-by-time interaction effects to be highlighted and led to a more precise and consistent representation of the HRQoL over time; this was supported by the martingale residuals and the Akaike information criterion.

CONCLUSION

Linear relationships between continuous outcomes (such as HRQoL scores) and time are usually the default choice. However, the functional form turns out to be important by affecting both the validity of the model and the statistical significance.

TRIAL REGISTRATION

This study is registered with ClinicalTrials.gov, number NCT00861094.

Kernel Estimation of Bivariate Time-varying Coefficient Model for Longitudinal Data with Terminal Event

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.

Statistical methods and graphical displays of quality of life with survival outcomes in oncology clinical trials for supporting the estimand framework

Background

Although there are discussions regarding standards of the analysis of patient-reported outcomes and quality of life (QOL) in oncology clinical trials, that of QOL with death events is not within their scope. For example, ignoring death can lead to bias in the QOL analysis for patients with moderate or high mortality rates in the palliative care setting. This is discussed in the estimand framework but is controversial. Information loss by summary measures under the estimand framework may make it challenging for clinicians to interpret the QOL analysis results. This study illustrated the use of graphical displays in the framework. They can be helpful for discussions between clinicians and statisticians and decision-making by stakeholders.

Methods

We reviewed the time-to-deterioration analysis, prioritized composite outcome approach, semi-competing risk analysis, survivor analysis, linear mixed model for repeated measures, and principal stratification approach. We summarized attributes of estimands and graphs in the statistical analysis and evaluated them in various hypothetical randomized controlled trials.

Results

Graphs for each analysis method provide different information and impressions. In the time-to-deterioration analysis, it was not easy to interpret the difference in the curves as an effect on QOL. The prioritized composite outcome approach provided new insights for QOL considering death by defining better conditions based on the distinction of OS and QOL. The semi-competing risk analysis provided different insights compared with the time-to-deterioration analysis and prioritized composite outcome approach. Due to the missing assumption, graphs by the linear mixed model for repeated measures should be carefully interpreted, even for descriptive purposes. The principal stratification approach provided pure comparison, but the interpretation was difficult because the target population was unknown.

Conclusions

Graphical displays can capture different aspects of treatment effects that should be described in the estimand framework.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01735-1.

Protocol for an observational cohort study identifying factors predicting accurately end of life in dementia with Lewy bodies and promoting quality end-of-life experiences: the PACE-DLB study

INTRODUCTION

Dementia with Lewy bodies (DLB) is one of the most common degenerative dementias. Despite the fact that most individuals with DLB die from complications of the disease, little is known regarding what factors predict impending end of life or are associated with a quality end of life.

METHODS AND ANALYSIS

This is a multisite longitudinal cohort study. Participants are being recruited from five academic centres providing subspecialty DLB care and volunteers through the Lewy Body Dementia Association (not receiving specialty care). Dyads must be US residents, include individuals with a clinical diagnosis of DLB and at least moderate-to-severe dementia and include the primary caregiver, who must pass a brief cognitive screen. The first dyad was enrolled 25 February 2021; recruitment is ongoing. Dyads will attend study visits every 6 months through the end of life or 3 years. Study visits will occur in-person or virtually. Measures include demographics, DLB characteristics, caregiver considerations, quality of life and satisfaction with end-of-life experiences. For dyads where the individual with DLB dies, the caregiver will complete a final study visit 3 months after the death to assess grief, recovery and quality of the end-of-life experience. Terminal trend models will be employed to identify significant predictors of approaching end of life (death in the next 6 months). Similar models will assess caregiver factors (eg, grief, satisfaction with end-of-life experience) after the death of the individual with DLB. A qualitative descriptive analysis approach will evaluate interview transcripts regarding end-of-life experiences.

ETHICS AND DISSEMINATION

This study was approved by the University of Florida institutional review board (IRB202001438) and is listed on clinicaltrials.gov (NCT04829656). Data sharing follows National Institutes of Health policies. Study results will be disseminated via traditional scientific strategies (conferences, publications) and through collaborating with the Lewy Body Dementia Association, National Institute on Aging and other partnerships.

Backward joint model and dynamic prediction of survival with multivariate longitudinal data

An important approach to dynamic prediction of time-to-event outcomes using longitudinal data is based on modeling the joint distribution of longitudinal and time-to-event data. The widely used joint model for this purpose is the shared random effect model. Presumably, adding more longitudinal predictors improves the predictive accuracy. However, the shared random effect model can be computationally difficult or prohibitive when a large number of longitudinal variables are used. In this paper, we study an alternative way of modeling the joint distribution of longitudinal and time-to-event data. Under this formulation, the log-likelihood involves no more than one-dimensional integration, regardless of the number of longitudinal variables in the model. Therefore, this model is particularly suitable in dynamic prediction problems with large number of longitudinal predictors. The model fitting can be implemented with tractable and stable computation by using a combination of pseudo maximum likelihood estimation, Expectation-Maximization algorithm, and convex optimization. We evaluate the proposed methodology and its predictive accuracy with varying number of longitudinal variables using simulations and data from a primary biliary cirrhosis study.

Survival models and health sequences

Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique-reverse alignment-for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.