ArticleStatistics in medicine2017
A semiparametric joint model for terminal trend of quality of life and survival in palliative care research.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EASL position paper on palliative care in advanced chronic liver disease.JHEP reports : innovation in hepatology · 2026Guideline
- Flexible modeling of longitudinal health-related quality of life data accounting for informative dropout in a cancer clinical trial.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023Trial
- Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.Lifetime data analysis · 2026Article
- Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research.Biostatistics (Oxford, England) · 2024Article
- Kernel Estimation of Bivariate Time-varying Coefficient Model for Longitudinal Data with Terminal Event.Journal of the American Statistical Association · 2024Article
- Semiparametric regression of panel count data with informative terminal event.Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability · 2023Article
- Statistical methods and graphical displays of quality of life with survival outcomes in oncology clinical trials for supporting the estimand framework.BMC medical research methodology · 2022Review
- Backward joint model and dynamic prediction of survival with multivariate longitudinal data.Statistics in medicine · 2021Article
- Observational
- Survival models and health sequences.Lifetime data analysis · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients' diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.