Cure Rate Models with Applications to Melanoma and Prostate Cancer Data

2009 ◽  
Author(s):  
Ming-Hui Chen ◽  
Sungduk Kim
Keyword(s):  
Prostate Cancer ◽  
Cure Rate ◽  
Cure Rate Models ◽  
Cancer Data

464: Is Obesity Associated with more Aggressive Prostate Cancer? (Data from Capsure)

2004 ◽  
Vol 171 (4S) ◽  
pp. 124-124 ◽  
Author(s):  
Christopher J. Kane ◽  
William W. Bassett ◽  
Natalia Sadetsky ◽  
Stephanie J. Silva ◽  
David J. Pasta ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Aggressive Prostate Cancer ◽  
Cancer Data

scholarly journals The Value of Real-World Data in Understanding Prostate Cancer Risk and Improving Clinical Care: Examples from Swedish Registries

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 875
Author(s):  
Kerri Beckmann ◽  
Hans Garmo ◽  
Ingela Franck Lissbrant ◽  
Pär Stattin
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Clinical Care ◽  
Prostate Cancer Risk ◽  
Real World Data ◽  
Level Of Evidence ◽  
World Data ◽  
Cancer Data ◽  
Using Data ◽  
Randomised Controlled

Real-world data (RWD), that is, data from sources other than controlled clinical trials, play an increasingly important role in medical research. The development of quality clinical registers, increasing access to administrative data sources, growing computing power and data linkage capacities have contributed to greater availability of RWD. Evidence derived from RWD increases our understanding of prostate cancer (PCa) aetiology, natural history and effective management. While randomised controlled trials offer the best level of evidence for establishing the efficacy of medical interventions and making causal inferences, studies using RWD offer complementary evidence about the effectiveness, long-term outcomes and safety of interventions in real-world settings. RWD provide the only means of addressing questions about risk factors and exposures that cannot be “controlled”, or when assessing rare outcomes. This review provides examples of the value of RWD for generating evidence about PCa, focusing on studies using data from a quality clinical register, namely the National Prostate Cancer Register (NPCR) Sweden, with longitudinal data on advanced PCa in Patient-overview Prostate Cancer (PPC) and data linkages to other sources in Prostate Cancer data Base Sweden (PCBaSe).

Absolute Prostate-Specific Antigen Value After Androgen Deprivation Is a Strong Independent Predictor of Survival in New Metastatic Prostate Cancer: Data From Southwest Oncology Group Trial 9346 (INT-0162)

2006 ◽  
Vol 24 (24) ◽  
pp. 3984-3990 ◽  
Author(s):  
Maha Hussain ◽  
Catherine M. Tangen ◽  
Celestia Higano ◽  
Paul F. Schelhammer ◽  
James Faulkner ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Proportional Hazards ◽  
Strong Predictor ◽  
Specific Antigen ◽  
Androgen Deprivation ◽  
Time To Death ◽  
Cancer Data ◽  
Risk Of Death ◽  
Group Trial

Purpose To establish whether absolute prostate-specific antigen (PSA) value after androgen deprivation (AD) is prognostic in metastatic (D2) prostate cancer (PCa). Patients and Methods D2 PCa patients with baseline PSA of at least 5 ng/mL received 7 months induction AD. Patients achieving PSA of 4.0 ng/mL or less on months 6 and 7 are randomly assigned to continuous versus intermittent AD on month 8. Eligibility for this analysis required a prestudy PSA with at least two subsequent PSAs and that patients be registered at least 1 year before analysis date. Survival was defined as time to death after 7 months of AD. Associations were evaluated by proportional hazards regression models. Results One thousand one hundred thirty four of 1,345 eligible patients achieved a PSA of 4 ng/mL or less. At end of induction, 965 patients maintained PSA of 4 or less and 604 had a PSA of 0.2 ng/mL or less. After controlling for prognostic factors, patients with a PSA of 4 or less to more than 0.2 ng/mL had less than one third the risk of death (ROD) as those with a PSA of more than 4 ng/mL (P < .001). Patients with PSA of 0.2 ng/mL or less had less than one fifth the ROD as patients with a PSA of more than 4 ng/mL (P < .001) and had significantly better survival than those with PSA of more than 0.2 to 4 ng/mL or less (P < .001). Median survival was 13 months for patients with a PSA of more than 4 ng/mL, 44 months for patients with PSA of more than 0.2 to 4 ng/mL or less, and 75 months for patients with PSA of 0.2 ng/mL or less. Conclusion A PSA of 4 ng/mL or less after 7 months of AD is a strong predictor of survival. This data should be used to tailor future trial design for D2 prostate cancer.

Active Surveillance as Initial Management of Newly Diagnosed Prostate Cancer: Data from the PURC

2019 ◽  
Vol 201 (5) ◽  
pp. 929-936 ◽  
Author(s):  
Mahesh Botejue ◽  
Daniel Abbott ◽  
John Danella ◽  
Claudette Fonshell ◽  
Serge Ginzburg ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Newly Diagnosed ◽  
Initial Management ◽  
Cancer Data

Relaxed Poisson cure rate models

2015 ◽  
Vol 58 (2) ◽  
pp. 397-415 ◽  
Author(s):  
Josemar Rodrigues ◽  
Gauss M. Cordeiro ◽  
Vicente G. Cancho ◽  
N. Balakrishnan
Keyword(s):  
Cure Rate ◽  
Cure Rate Models

scholarly journals Fitting Cure Rate Model to Breast Cancer Data of Cancer Research Center

2015 ◽  
Vol 16 (17) ◽  
pp. 7923-7927 ◽  
Author(s):  
Ahmad Reza Baghestani ◽  
Farid Zayeri ◽  
Mohammad Esmaeil Akbari ◽  
Leyla Shojaee ◽  
Naghmeh Khadembashi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
Research Center ◽  
Rate Model ◽  
Breast Cancer Data ◽  
Cure Rate ◽  
Cancer Data ◽  
Cure Rate Model

Bayesian Approaches to Cure Rate Models

2005 ◽  
Author(s):  
Joseph G. Ibrahim ◽  
Ming-Hui Chen ◽  
Debajyoti Sinha
Keyword(s):  
Cure Rate ◽  
Bayesian Approaches ◽  
Cure Rate Models

scholarly journals Gradient and Likelihood Ratio Tests in Cure Rate Models

2016 ◽  
Vol 5 (4) ◽  
pp. 9 ◽  
Author(s):  
Hérica P. A. Carneiro ◽  
Dione M. Valença
Keyword(s):  
Likelihood Ratio ◽  
Asymptotic Properties ◽  
Information Matrix ◽  
Survival Models ◽  
Finite Sample ◽  
Cure Rate ◽  
Time Model ◽  
Cure Rate Models ◽  
Large Sample ◽  
Finite Samples

In some survival studies part of the population may be no longer subject to the event of interest. The called cure rate models take this fact into account. They have been extensively studied for several authors who have proposed extensions and applications in real lifetime data. Classic large sample tests are usually considered in these applications, especially the likelihood ratio. Recently  a new test called \textit{gradient test} has been proposed. The gradient statistic shares the same asymptotic properties with the classic likelihood ratio and does not involve knowledge of the information matrix, which can be an advantage in survival models. Some simulation studies have been carried out to explore the behavior of the gradient test in finite samples and compare it with the classic tests in different models. However little is known about the properties of these large sample tests in finite sample for cure rate models. In this work we  performed a simulation study based on the promotion time model with Weibull distribution, to assess the performance of likelihood ratio and gradient tests in finite samples. An application is presented to illustrate the results.

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