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Article ; Online: Bayesian joint modelling of longitudinal and time to event data

Maha Alsefri / Maria Sudell / Marta García-Fiñana / Ruwanthi Kolamunnage-Dona

BMC Medical Research Methodology, Vol 20, Iss 1, Pp 1-

a methodological review

2020 Volume 17

Abstract: Abstract Background In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and ... ...

Abstract	Abstract Background In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint models have been extensively reviewed, and due to the recent popularity of data-driven Bayesian approaches, a review on current Bayesian estimation of joint model is useful to draw recommendations for future researches. Methods We have undertaken a comprehensive review on Bayesian univariate and multivariate joint models. We focused on type of outcomes, model assumptions, association structure, estimation algorithm, dynamic prediction and software implementation. Results A total of 89 articles have been identified, consisting of 75 methodological and 14 applied articles. The most common approach to model the longitudinal and time-to-event outcomes jointly included linear mixed effect models with proportional hazards. A random effect association structure was generally used for linking the two sub-models. Markov Chain Monte Carlo (MCMC) algorithms were commonly used (93% articles) to estimate the model parameters. Only six articles were primarily focused on dynamic predictions for longitudinal or event-time outcomes. Conclusion Methodologies for a wide variety of data types have been proposed; however the research is limited if the association between the two outcomes changes over time, and there is also lack of methods to determine the association structure in the absence of clinical background knowledge. Joint modelling has been proved to be beneficial in producing more accurate dynamic prediction; however, there is a lack of sufficient tools to validate the prediction.
Keywords	Joint models ; Longitudinal outcomes ; Time-to-event ; Dynamic prediction ; Bayesian estimation ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2020-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correction to

Maria Sudell / Ruwanthi Kolamunnage-Dona / Catrin Tudur-Smith

BMC Medical Research Methodology, Vol 18, Iss 1, Pp 1-

joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis

2018 Volume 1

Abstract: Abstract Following publication of the original article [1] the authors reported that reference 15 (Cella et al.) had been incorrectly replaced with a duplicate of Brombin et al. during publication. ...

Abstract	Abstract Following publication of the original article [1] the authors reported that reference 15 (Cella et al.) had been incorrectly replaced with a duplicate of Brombin et al. during publication.
Keywords	Medicine (General) ; R5-920
Language	English
Publishing date	2018-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correction

Miranta Antoniou / Ruwanthi Kolamunnage-Dona / Andrea L. Jorgensen

Journal of Personalized Medicine, Vol 8, Iss 2, p

Antoniou, M.; et al. Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review. J. Pers. Med. 2017, 7, 1

2018 Volume 17

Abstract: ... n/ ... ...

Abstract	n/a
Keywords	n/a ; Medicine ; R
Language	English
Publishing date	2018-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Correction

Miranta Antoniou / Andrea L. Jorgensen / Ruwanthi Kolamunnage-Dona

Journal of Personalized Medicine, Vol 8, Iss 2, p

Antoniou, M.; et al. Fixed and Adaptive Parallel Subgroup-Specific Design for Survival Outcomes: Power and Sample Size. J. Pers. Med. 2017, 7, 19

2018 Volume 18

Abstract: ... n/ ... ...

Abstract	n/a
Keywords	n/a ; Medicine ; R
Language	English
Publishing date	2018-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Fixed and Adaptive Parallel Subgroup-Specific Design for Survival Outcomes

Miranta Antoniou / Andrea L. Jorgensen / Ruwanthi Kolamunnage-Dona

Journal of Personalized Medicine, Vol 7, Iss 4, p

Power and Sample Size

2017 Volume 19

Abstract: Biomarker-guided clinical trial designs, which focus on testing the effectiveness of a biomarker-guided approach to treatment in improving patient health, have drawn considerable attention in the era of stratified medicine with many different designs ... ...

Abstract	Biomarker-guided clinical trial designs, which focus on testing the effectiveness of a biomarker-guided approach to treatment in improving patient health, have drawn considerable attention in the era of stratified medicine with many different designs being proposed in the literature. However, planning such trials to ensure they have sufficient power to test the relevant hypotheses can be challenging and the literature often lacks guidance in this regard. In this study, we focus on the parallel subgroup-specific design, which allows the evaluation of separate treatment effects in the biomarker-positive subgroup and biomarker-negative subgroup simultaneously. We also explore an adaptive version of the design, where an interim analysis is undertaken based on a fixed percentage of target events, with the option to stop each biomarker-defined subgroup early for futility or efficacy. We calculate the number of events and patients required to ensure sufficient power in each of the biomarker-defined subgroups under different scenarios when the primary outcome is time-to-event. For the adaptive version, stopping probabilities are also explored. Since multiple hypotheses are being tested simultaneously, and multiple interim analyses are undertaken, we also focus on controlling the overall type I error rate by way of multiplicity adjustment.
Keywords	biomarker ; biomarker-guided trial design ; clinical research design ; personalized medicine ; fixed design ; adaptive design ; sample size ; simulation study ; multiplicity issues ; Medicine ; R
Subject code	310
Language	English
Publishing date	2017-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Time-dependent ROC curve analysis in medical research

Adina Najwa Kamarudin / Trevor Cox / Ruwanthi Kolamunnage-Dona

BMC Medical Research Methodology, Vol 17, Iss 1, Pp 1-

current methods and applications

2017 Volume 19

Abstract: Abstract Background ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical ( ... ...

Abstract	Abstract Background ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker. Methods We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver. Results From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking. Conclusions The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research.
Keywords	ROC curve ; Time-dependent AUC ; Biomarker evaluation ; Event-time ; Longitudinal data ; Software ; Medicine (General) ; R5-920
Subject code	005
Language	English
Publishing date	2017-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III

Miranta Antoniou / Ruwanthi Kolamunnage-Dona / Andrea L. Jorgensen

Journal of Personalized Medicine, Vol 7, Iss 1, p

A Methodological Review

2017 Volume 1

Abstract: Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past ... ...

Abstract	Biomarker-guided treatment is a rapidly developing area of medicine, where treatment choice is personalised according to one or more of an individual’s biomarker measurements. A number of biomarker-guided trial designs have been proposed in the past decade, including both adaptive and non-adaptive trial designs which test the effectiveness of a biomarker-guided approach to treatment with the aim of improving patient health. A better understanding of them is needed as challenges occur both in terms of trial design and analysis. We have undertaken a comprehensive literature review based on an in-depth search strategy with a view to providing the research community with clarity in definition, methodology and terminology of the various biomarker-guided trial designs (both adaptive and non-adaptive designs) from a total of 211 included papers. In the present paper, we focus on non-adaptive biomarker-guided trial designs for which we have identified five distinct main types mentioned in 100 papers. We have graphically displayed each non-adaptive trial design and provided an in-depth overview of their key characteristics. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. Our comprehensive review provides guidance for those designing biomarker-guided trials.
Keywords	biomarker-guided trial design ; clinical research design ; phase II ; phase III ; personalized medicine ; predictive biomarker ; prognostic biomarker ; non-adaptive trial designs ; clinical trials methodology ; sample size ; Medicine ; R
Subject code	310
Language	English
Publishing date	2017-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modelling of longitudinal data to predict cardiovascular disease risk

David Stevens / Deirdre A. Lane / Stephanie L. Harrison / Gregory Y. H. Lip / Ruwanthi Kolamunnage-Dona

BMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-

a methodological review

2021 Volume 24

Abstract: Abstract Objective The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories. Methods We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms ... ...

Abstract	Abstract Objective The identification of methodology for modelling cardiovascular disease (CVD) risk using longitudinal data and risk factor trajectories. Methods We screened MEDLINE-Ovid from inception until 3 June 2020. MeSH and text search terms covered three areas: data type, modelling type and disease area including search terms such as “longitudinal”, “trajector” and “cardiovasc” respectively. Studies were filtered to meet the following inclusion criteria: longitudinal individual patient data in adult patients with ≥3 time-points and a CVD or mortality outcome. Studies were screened and analyzed by one author. Any queries were discussed with the other authors. Comparisons were made between the methods identified looking at assumptions, flexibility and software availability. Results From the initial 2601 studies returned by the searches 80 studies were included. Four statistical approaches were identified for modelling the longitudinal data: 3 (4%) studies compared time points with simple statistical tests, 40 (50%) used single-stage approaches, such as including single time points or summary measures in survival models, 29 (36%) used two-stage approaches including an estimated longitudinal parameter in survival models, and 8 (10%) used joint models which modelled the longitudinal and survival data together. The proportion of CVD risk prediction models created using longitudinal data using two-stage and joint models increased over time. Conclusions Single stage models are still heavily utilized by many CVD risk prediction studies for modelling longitudinal data. Future studies should fully utilize available longitudinal data when analyzing CVD risk by employing two-stage and joint approaches which can often better utilize the available data.
Keywords	Cardiovascular disease ; Longitudinal ; Repeated measures ; Risk prediction ; Methodological review ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III

Miranta Antoniou / Andrea L Jorgensen / Ruwanthi Kolamunnage-Dona

PLoS ONE, Vol 11, Iss 2, p e

A Methodological Review.

2016 Volume 0149803

Abstract: Background Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as ... ...

Abstract	Background Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible. Methods and findings We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design. Conclusions Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2016-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Joint models for longitudinal and time-to-event data

Maria Sudell / Ruwanthi Kolamunnage-Dona / Catrin Tudur-Smith

BMC Medical Research Methodology, Vol 16, Iss 1, Pp 1-

a review of reporting quality with a view to meta-analysis

2016 Volume 11

Abstract: Abstract Background Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its ... ...

Abstract	Abstract Background Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical literature. During this review we aim to assess the current standard of reporting of joint models applied in the literature, and to determine whether current reporting standards would allow or hinder future aggregate data meta-analyses of model results. Methods We undertook a literature review of non-methodological studies that involved joint modelling of longitudinal and time-to-event medical data. Study characteristics were extracted and an assessment of whether separate meta-analyses for longitudinal, time-to-event and association parameters were possible was made. Results The 65 studies identified used a wide range of joint modelling methods in a selection of software. Identified studies concerned a variety of disease areas. The majority of studies reported adequate information to conduct a meta-analysis (67.7% for longitudinal parameter aggregate data meta-analysis, 69.2% for time-to-event parameter aggregate data meta-analysis, 76.9% for association parameter aggregate data meta-analysis). In some cases model structure was difficult to ascertain from the published reports. Conclusions Whilst extraction of sufficient information to permit meta-analyses was possible in a majority of cases, the standard of reporting of joint models should be maintained and improved. Recommendations for future practice include clear statement of model structure, of values of estimated parameters, of software used and of statistical methods applied.
Keywords	Joint model ; Meta-analysis ; Longitudinal ; Time-to-event ; Review ; Reporting standards ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2016-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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