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Book: Joint models for longitudinal and time to event data

Rizopoulos, Dimitris

with applications in R

(Chapman & Hall, CRC biostatistics series)

2012

Title variant	Joint models for longitudinal and time-to-event data
Author's details	Dimitris Rizopoulos
Series title	Chapman & Hall, CRC biostatistics series
Keywords	Numerical analysis--Data processing ; R (Computer program language) ; Numerische Mathematik ; Programmiersprache
Subject	Programmierungssprache ; Numerik ; Numerical analysis ; Numerische Analysis
Subject code	518
Language	English
Size	XIV, 261 S. : graph. Darst., 24 cm
Publisher	CRC Press
Publishing place	Boca Raton u.a.
Publishing country	United States
Document type	Book
HBZ-ID	HT017338405
ISBN	978-1-4398-7286-4 ; 1-4398-7286-4 ; 9781439872871 ; 1439872872
Database	Catalogue ZB MED Medicine, Health

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Article ; Online: Optimizing dynamic predictions from joint models using super learning.

Rizopoulos, Dimitris / Taylor, Jeremy M G

Statistics in medicine

2024 Volume 43, Issue 7, Page(s) 1315–1328

Abstract: Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions ... ...

Abstract	Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.
MeSH term(s)	Humans ; Computer Simulation ; Precision Medicine/methods
Language	English
Publishing date	2024-01-25
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.10010
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Article ; Online: The Rhinoplasty Rosetta Stone: using Rasch analysis to create and validate crosswalks between the NOSE and SCHNOS functional subscale.

van Zijl, Floris Vwj / Declau, Frank / Rizopoulos, Dimitris / Datema, Frank R

Plastic and reconstructive surgery

2024

Abstract: Background: The NOSE and SCHNOS functional subscale are widely used PROMs to measure functional outcomes of rhinoplasty. However, as different instruments produce scores on different metrics, results of these instruments cannot be linked directly. This ... ...

Abstract	Background: The NOSE and SCHNOS functional subscale are widely used PROMs to measure functional outcomes of rhinoplasty. However, as different instruments produce scores on different metrics, results of these instruments cannot be linked directly. This hinders comparing and aggregating rhinoplasty outcome data from practices using either instrument. The aim of this study was to develop and validate crosswalks between the NOSE and SCHNOS-O. Methods: In a sample of 552 rhinoplasty patients who completed both instruments, the NOSE and SCHNOS-O scales were co-calibrated onto a common interval-scaled metric using Rasch analysis. Separate Rasch models were run per instrument and the latent constructs were estimated using the calibrated item parameters. By anchoring original PROM scores of both instruments to this Rasch computed measurement scale, the scores of both instruments were linked. A second independent sample was used to validate the created crosswalks. Results: Total scores on the NOSE and SCHNOS-O were strongly correlated. The Rasch-based co-calibration of the NOSE and SCHNOS-O items resulted in a model that adequately fitted the data. Back-and-forth crosswalk tables were created from the NOSE to the SCHNOS-O. For patients with moderate nasal obstruction, predicted SCHNOS-O scores were slightly higher for a given level of the NOSE. Intraclass correlation coefficients between predicted and actual scores were 0.93 for both directions, indicating adequate agreement for group-level comparisons. Conclusion: This study developed and validated Rasch-based crosswalks from the NOSE to the SCHNOS-O and vice-versa. The provided crosswalks enhance comparison and harmonization of functional rhinoplasty outcomes.
Language	English
Publishing date	2024-03-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	208012-6
ISSN	1529-4242 ; 0032-1052 ; 0096-8501
ISSN (online)	1529-4242
ISSN	0032-1052 ; 0096-8501
DOI	10.1097/PRS.0000000000011438
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Article ; Online: Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy.

Rizopoulos, Dimitris / Taylor, Jeremy Mg / Papageorgiou, Grigorios / Morgan, Todd M

Statistical methods in medical research

2024 Volume 33, Issue 5, Page(s) 894–908

Abstract: Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to ... ...

Abstract	Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package
MeSH term(s)	Humans ; Male ; Prostatectomy ; Salvage Therapy ; Prostatic Neoplasms/surgery ; Longitudinal Studies ; Prostate-Specific Antigen/blood ; Models, Statistical ; Neoplasm Recurrence, Local
Chemical Substances	Prostate-Specific Antigen (EC 3.4.21.77)
Language	English
Publishing date	2024-03-19
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802241239003
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Article ; Online: Sample size calculation for clinical trials analyzed with the meta-analytic-predictive approach.

Qi, Hongchao / Rizopoulos, Dimitris / van Rosmalen, Joost

Research synthesis methods

2023 Volume 14, Issue 3, Page(s) 396–413

Abstract: The meta-analytic-predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the ...

Abstract	The meta-analytic-predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the new trial when historical data is available, and the MAP approach is used in the analysis. In previous applications of the MAP approach, the prior effective sample size (ESS) acted as a metric to quantify the number of subjects the historical information is worth. However, the validity of using the prior ESS in sample size calculation (i.e., reducing the number of randomized controls by the derived prior ESS) is questionable, because different approaches may yield different values for prior ESS. In this work, we propose a straightforward Monte Carlo approach to calculate the sample size that achieves the desired power in the new trial given available historical controls. To make full use of the available historical information to simulate the new trial data, the control parameters are not taken as a point estimate but sampled from the MAP prior. These sampled control parameters and the MAP prior based on the historical data are then used to derive the statistical power for the treatment effect and the resulting required sample size. The proposed sample size calculation approach is illustrated with real-life data sets with different outcomes from three studies. The results show that this approach to calculating the required sample size for the MAP analysis is straightforward and generic.
MeSH term(s)	Humans ; Sample Size ; Models, Statistical ; Bayes Theorem ; Monte Carlo Method ; Research Design ; Computer Simulation
Language	English
Publishing date	2023-01-14
Publishing country	England
Document type	Meta-Analysis ; Journal Article
ZDB-ID	2548499-0
ISSN	1759-2887 ; 1759-2879
ISSN (online)	1759-2887
ISSN	1759-2879
DOI	10.1002/jrsm.1618
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Article ; Online: Exploring the relation of active surveillance schedules and prostate cancer mortality.

Yang, Zhenwei / Heijnsdijk, Eveline A M / Newcomb, Lisa F / Rizopoulos, Dimitris / Erler, Nicole S

Cancer medicine

2024 Volume 13, Issue 5, Page(s) e6977

Abstract: Background: Active surveillance (AS), where treatment is deferred until cancer progression is detected by a biopsy, is acknowledged as a way to reduce overtreatment in prostate cancer. However, a consensus on the frequency of taking biopsies while in AS ...

Abstract	Background: Active surveillance (AS), where treatment is deferred until cancer progression is detected by a biopsy, is acknowledged as a way to reduce overtreatment in prostate cancer. However, a consensus on the frequency of taking biopsies while in AS is lacking. In former studies to optimize biopsy schedules, the delay in progression detection was taken as an evaluation indicator and believed to be associated with the long-term outcome, prostate cancer mortality. Nevertheless, this relation was never investigated in empirical data. Here, we use simulated data from a microsimulation model to fill this knowledge gap. Methods: In this study, the established MIcrosimulation SCreening Analysis model was extended with functionality to simulate the AS procedures. The biopsy sensitivity in the model was calibrated on the Canary Prostate Cancer Active Surveillance Study (PASS) data, and four (tri-yearly, bi-yearly, PASS, and yearly) AS programs were simulated. The relation between detection delay and prostate cancer mortality was investigated by Cox models. Results: The biopsy sensitivity of progression detection was found to be 50%. The Cox models show a positive relation between a longer detection delay and a higher risk of prostate cancer death. A 2-year delay resulted in a prostate cancer death risk of 2.46%-2.69% 5 years after progression detection and a 10-year risk of 5.75%-5.91%. A 4-year delay led to an approximately 8% greater 5-year risk and an approximately 25% greater 10-year risk. Conclusion: The detection delay is confirmed as a surrogate for prostate cancer mortality. A cut-off for a "safe" detection delay could not be identified.
MeSH term(s)	Male ; Humans ; Prostatic Neoplasms/pathology ; Prostate-Specific Antigen ; Watchful Waiting/methods ; Disease Progression ; Prostate/pathology ; Biopsy/methods
Chemical Substances	Prostate-Specific Antigen (EC 3.4.21.77)
Language	English
Publishing date	2024-03-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2659751-2
ISSN	2045-7634 ; 2045-7634
ISSN (online)	2045-7634
ISSN	2045-7634
DOI	10.1002/cam4.6977
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Article ; Online: Incorporating historical control information in ANCOVA models using the meta-analytic-predictive approach.

Qi, Hongchao / Rizopoulos, Dimitris / van Rosmalen, Joost

Research synthesis methods

2022 Volume 13, Issue 6, Page(s) 681–696

Abstract: The meta-analytic-predictive (MAP) approach is a Bayesian meta-analytic method to synthesize and incorporate information from historical controls in the analysis of a new trial. Classically, only a single parameter, typically the intercept or rate, is ... ...

Abstract	The meta-analytic-predictive (MAP) approach is a Bayesian meta-analytic method to synthesize and incorporate information from historical controls in the analysis of a new trial. Classically, only a single parameter, typically the intercept or rate, is assumed to vary across studies, which may not be realistic in more complex models. Analysis of covariance (ANCOVA) is often used to analyze trials with a pretest-posttest design, where both the intercept and the baseline effect (coefficient of the outcome at baseline) affect the estimated treatment effect. We extended the MAP approach to ANCOVA, to allow for variation in the intercept and the baseline effect across studies, and possibly also correlation between these parameters. The method was illustrated using data from the Alzheimer's Disease Cooperative Study (ADCS) and assessed with a simulation study. In the ADCS data, the proposed multivariate MAP approach yielded a prior effective sample size of 79 and 58 for the intercept and the baseline effect respectively and reduced the posterior standard deviation of the treatment effect by 12.6%. The result was robust to the choice of prior for the between-study variation. In the simulations, the proposed approach yielded power gains with a good control of the type I error rate. Ignoring the between-study correlation of the parameters or assuming no variation in the baseline effect generally led to less power gain. In conclusion, the MAP approach can be extended to a multivariate version for ANCOVA, which may improve the estimation of the treatment effect.
MeSH term(s)	Bayes Theorem ; Models, Statistical ; Sample Size ; Research Design ; Computer Simulation
Language	English
Publishing date	2022-04-26
Publishing country	England
Document type	Meta-Analysis ; Journal Article
ZDB-ID	2548499-0
ISSN	1759-2887 ; 1759-2879
ISSN (online)	1759-2887
ISSN	1759-2879
DOI	10.1002/jrsm.1561
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Article ; Online: Comments on 'Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian Joint Modeling Working Group'.

Rizopoulos, Dimitris

Statistics in medicine

2015 Volume 34, Issue 14, Page(s) 2196–2197

MeSH term(s)	Clinical Trials as Topic/methods ; Epidemiologic Research Design ; Humans ; Models, Statistical ; Survival Analysis
Language	English
Publishing date	2015-06-30
Publishing country	England
Document type	Comment ; Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.6260
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Article ; Online: Incorporating historical controls in clinical trials with longitudinal outcomes using the modified power prior.

Qi, Hongchao / Rizopoulos, Dimitris / Lesaffre, Emmanuel / van Rosmalen, Joost

Pharmaceutical statistics

2022 Volume 21, Issue 5, Page(s) 818–834

Abstract: Several dynamic borrowing methods, such as the modified power prior (MPP), the commensurate prior, have been proposed to increase statistical power and reduce the required sample size in clinical trials where comparable historical controls are available. ...

Abstract	Several dynamic borrowing methods, such as the modified power prior (MPP), the commensurate prior, have been proposed to increase statistical power and reduce the required sample size in clinical trials where comparable historical controls are available. Most methods have focused on cross-sectional endpoints, and appropriate methodology for longitudinal outcomes is lacking. In this study, we extend the MPP to the linear mixed model (LMM). An important question is whether the MPP should use the conditional version of the LMM (given the random effects) or the marginal version (averaged over the distribution of the random effects), which we refer to as the conditional MPP and the marginal MPP, respectively. We evaluated the MPP for one historical control arm via a simulation study and an analysis of the data of Alzheimer's Disease Cooperative Study (ADCS) with the commensurate prior as the comparator. The conditional MPP led to inflated type I error rate when there existed moderate or high between-study heterogeneity. The marginal MPP and the commensurate prior yielded a power gain (3.6%-10.4% vs. 0.6%-4.6%) with the type I error rates close to 5% (5.2%-6.2% vs. 3.8%-6.2%) when the between-study heterogeneity is not excessively high. For the ADCS data, all the borrowing methods improved the precision of estimates and provided the same clinical conclusions. The marginal MPP and the commensurate prior are useful for borrowing historical controls in longitudinal data analysis, while the conditional MPP is not recommended due to inflated type I error rates.
MeSH term(s)	Bayes Theorem ; Computer Simulation ; Cross-Sectional Studies ; Humans ; Linear Models ; Models, Statistical ; Research Design ; Sample Size
Language	English
Publishing date	2022-02-06
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2083706-9
ISSN	1539-1612 ; 1539-1604
ISSN (online)	1539-1612
ISSN	1539-1604
DOI	10.1002/pst.2195
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Article ; Online: Machine learning for causal inference in Biostatistics.

Rose, Sherri / Rizopoulos, Dimitris

Biostatistics (Oxford, England)

2019 Volume 21, Issue 2, Page(s) 336–338

MeSH term(s)	Biostatistics/methods ; Causality ; Humans ; Machine Learning
Language	English
Publishing date	2019-11-18
Publishing country	England
Document type	Introductory Journal Article
ZDB-ID	2031500-4
ISSN	1468-4357 ; 1465-4644
ISSN (online)	1468-4357
ISSN	1465-4644
DOI	10.1093/biostatistics/kxz045
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