ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2015
Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.
Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- On a Penalized Likelihood Approach for Joint Modeling of Longitudinal Covariates and Partly Interval-Censored Data-An Application to the Anti-PD1 Brain Collaboration Trial.Biometrical journal. Biometrische Zeitschrift · 2026Article
- DyVAESurv: A VAE-Enhanced Model for Dynamic Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Modelling longitudinal and time-to-event data: a phase IV simulation study comparing R package implementations of joint models with time-varying Cox proportional-hazards regression, and the two-stage approach.BMC medical research methodology · 2026Article
- Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.Journal of the American Statistical Association · 2026Article
- JointLIME: An interpretation method for machine learning survival models with endogenous time-varying covariates in credit scoring.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS.The annals of applied statistics · 2024Article
- Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations.Annals of the American Thoracic Society · 2023Article
- Robust joint modelling of left-censored longitudinal data and survival data with application to HIV vaccine studies.The annals of applied statistics · 2023Article
- Joint analysis of recurrence and termination: A Bayesian latent class approach.Statistical methods in medical research · 2021Article
- Item-Weighted Likelihood Method for Measuring Growth in Longitudinal Study With Tests Composed of Both Dichotomous and Polytomous Items.Frontiers in psychology · 2021Article
- A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior.Entropy (Basel, Switzerland) · 2020Article
- Explaining the Sex Effect on Survival in Cystic Fibrosis: a Joint Modeling Study of UK Registry Data.Epidemiology (Cambridge, Mass.) · 2020Article
- Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes.Statistical methods in medical research · 2020Article
- An imputation approach for fitting two-part mixed effects models for longitudinal semi-continuous data.Statistical methods in medical research · 2020Article
- Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset.BMC pulmonary medicine · 2020Article
- A sensitivity analysis approach for informative dropout using shared parameter models.Biometrics · 2019Article
- Two-part models with stochastic processes for modelling longitudinal semicontinuous data: Computationally efficient inference and modelling the overall marginal mean.Statistical methods in medical research · 2018Article
- A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies.Biostatistics (Oxford, England) · 2018Article
- Article
- Comparison of Nutrition and Lung Function Outcomes in Patients with Cystic Fibrosis Living in Canada and the United States.American journal of respiratory and critical care medicine · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and event history data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models. The method uses a discretization of the timescale for the time-to-event outcome, which is often unavoidable in any case when events correspond to dropout. We place no restriction on the times at which repeated measurements are made. An analysis of repeated lung function measurements in a cystic fibrosis cohort is used to illustrate the method.
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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.