Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models

Taylor, Jeremy M. G.; Park, Yongseok; Ankerst, Donna P.; Proust‐Lima, Cécile; Williams, Scott; Kestin, Larry L.; Bae, K.; Pickles, Tom; Sandler, Howard M.

doi:10.1111/j.1541-0420.2012.01823.x

Cited by 92 publications

(139 citation statements)

References 15 publications

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“…For example, if the risks of upgrading and treatment initiation both increase with PSA velocity, the two risks will be positively correlated. In practice, we estimate a so-called joint model for the evolution of the patient variables and the risks of upgrading and treatment initiation (13–15). After fitting the joint model, we extract the risk of upgrading in the absence of competing treatments using standard statistical formulas for obtaining marginal from conditional data summaries.…”

Section: Data Synthesis and Analysismentioning

confidence: 99%

Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts

et al. 2017

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Background Active surveillance (AS) is increasingly accepted for managing low-risk prostate cancer, yet there is no consensus about implementation. The lack of consensus is due in part to uncertainty about risks of disease progression, which have not been systematically compared or integrated across AS studies with variable surveillance protocols and dropout to treatment. Objective To (1) compare risks of upgrading from Gleason score (GS) ≤6 to ≥7 across AS studies after accounting for differences in surveillance intervals and competing treatments and (2) evaluate tradeoffs of more vs less frequent biopsies. Design Joint statistical model of longitudinal prostate-specific antigen (PSA) levels and risks of biopsy upgrading. Setting Johns Hopkins University (JHU), Canary Prostate Active Surveillance Study (PASS), University of California San Francisco (UCSF), and University of Toronto (UT) AS studies. Patients 2,576 men aged 40–79 years diagnosed with GS ≤6 and clinical stage ≤T2 prostate cancer enrolled in 1995–2014. Measurements PSA levels and biopsy GS. Results After accounting for variable surveillance intervals and competing treatment, estimated risks of biopsy upgrading in PASS and UT were similar, but UCSF was higher and JHU lower. All cohorts produced a delay of 3–5 months in detecting upgrading under biennial biopsies starting after a first confirmatory biopsy relative to annual biopsies. Limitations The model does not account for possible biopsy GS misclassification. Conclusions Men in different AS studies exhibit different risks of biopsy upgrading after accounting for variable surveillance protocols and competing treatments. Nonetheless, despite these differences, the consequences of more versus less frequent biopsies appear to be similar across cohorts. Biennial biopsies appear to be an acceptable alternative to annual biopsies. Funding Source National Cancer Institute and others.

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Section: Data Synthesis and Analysismentioning

confidence: 99%

Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts

et al. 2017

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“…[22] and [38], with different joint modeling approaches, showed that accounting for longitudinal PSA measures was a powerful tool for individual prediction. [31], following the joint modeling work of [22], implemented real-time predictions of PCa recurrence on a web-based calculator. In the early detection area, several studies have assessed the predictive value of dynamic PSA markers like PSA velocity, PSA doubling time or PSA percentual change.…”

Section: Discussionmentioning

confidence: 99%

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

Serrat

Rué

Pardo

et al. 2015

Journal of Applied Statistics

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The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer (ERSPC) study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.

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“…Joint models constitute a valuable tool that can be used to derive such probabilities and also provide predictions for future biomarker levels. More specifically, under the Bayesian specification of the joint model, presented in Section 2, we can derive subjectspecific predictions for either the survival or longitudinal outcomes (Yu, Taylor, and Sandler 2008;Rizopoulos 2011Rizopoulos , 2012Taylor, Park, Ankerst, Proust-Lima, Williams, Kestin, Bae, Pickles, and Sandler 2013). To put it more formally, based on a joint model fitted to a sample D n = {T i , δ i , y i ; i = 1, .…”

Section: Definitions and Estimationmentioning

confidence: 99%

“…More details can be found in Yu et al (2008), Rizopoulos (2011Rizopoulos ( , 2012, and Taylor et al (2013).…”

Section: Definitions and Estimationmentioning

confidence: 99%

TheRPackageJMbayesfor Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC

Rizopoulos¹

2016

J. Stat. Soft.

205

265

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Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markov chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.

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Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models

Cited by 92 publications

References 15 publications

Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts

Comparative Analysis of Biopsy Upgrading in Four Prostate Cancer Active Surveillance Cohorts

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

TheRPackageJMbayesfor Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC

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