Reporting of cause-specific treatment effects in cancer clinical trials with competing risks: A systematic review

2012 ◽  
Vol 33 (5) ◽  
pp. 920-924 ◽  
Author(s):  
Loren K. Mell ◽  
Steven K. Lau ◽  
Brent S. Rose ◽  
Jong-Hyeon Jeong
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Competing Risks ◽  
Treatment Effects ◽  
Specific Treatment ◽  
Cancer Clinical Trials

scholarly journals Application of multi-state models in cancer clinical trials

2018 ◽  
Vol 15 (5) ◽  
pp. 489-498 ◽  
Author(s):  
Jennifer G Le-Rademacher ◽  
Ryan A Peterson ◽  
Terry M Therneau ◽  
Ben L Sanford ◽  
Richard M Stone ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Complete Remission ◽  
Treatment Effects ◽  
Remission Rate ◽  
Disease Process ◽  
Time Dependent ◽  
Cancer Clinical Trials ◽  
Cox Models ◽  
Multiple Paths ◽  
State Models

Background/aims The goal of this article is to illustrate the utility of multi-state models in cancer clinical trials. Our specific aims are to describe multi-state models and how they differ from standard survival methods, to illustrate how multi-state models can facilitate deeper understanding of the treatment effect on multiple paths along the disease process that patients could experience in cancer clinical trials, to explain the differences between multi-state models and time-dependent Cox models, and to briefly describe available software to conduct such analyses. Methods Data from 717 newly diagnosed acute myeloid leukemia patients who enrolled in the CALGB 10603 trial were used as an illustrative example. The current probability-in-state was estimated using the Aalen–Johansen estimator. The restricted mean time in state was calculated as the area under the probability-in-state curves. Cox-type regression was used to evaluate the effect of midostaurin on the various clinical paths. Simulation was conducted using a newly constructed shiny application. All analyses were performed using the R software. Results Multi-state model analyses of CALGB 10603 suggested that the overall improvement in survival with midostaurin seen in the primary analysis possibly resulted from a higher complete remission rate in combination with a lower risk of relapse and of death after complete remission in patients treated with midostaurin. Simulation results, in a three-state illness-death without recovery model, demonstrate that multi-state models and time-dependent Cox models evaluate treatment effects from different frameworks. Conclusion Multi-state models allow detailed evaluation of treatment effects in complex clinical trial settings where patients can experience multiple paths between study enrollment and the final outcome. Multi-state models can be used as a complementary tool to standard survival analyses to provide deeper insights to the effects of treatment in trial settings with complex disease process.

scholarly journals Older adult participation in cancer clinical trials: A systematic review of barriers and interventions

2020 ◽  
Author(s):  
Mina S. Sedrak ◽  
Rachel A. Freedman ◽  
Harvey J. Cohen ◽  
Hyman B. Muss ◽  
Aminah Jatoi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Older Adult ◽  
Cancer Clinical Trials ◽  
Adult Participation

scholarly journals Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties

2016 ◽  
Vol 25 (6) ◽  
pp. 2488-2505 ◽  
Author(s):  
Il Do Ha ◽  
Nicholas J Christian ◽  
Jong-Hyeon Jeong ◽  
Junwoo Park ◽  
Youngjo Lee
Keyword(s):  
Clinical Trials ◽  
Competing Risks ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Likelihood Function ◽  
Profile Likelihood ◽  
Partial Likelihood ◽  
Likelihood Method ◽  
Subdistribution Hazard ◽  
Competing Risks Data

Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline risks may vary among centers. In this paper, we propose a subdistribution hazard regression model with multivariate frailty to investigate heterogeneity in treatment effects among centers from multi-center clinical trials. For inference, we develop a hierarchical likelihood (or h-likelihood) method, which obviates the need for an intractable integration over the frailty terms. We show that the profile likelihood function derived from the h-likelihood is identical to the partial likelihood, and hence it can be extended to the weighted partial likelihood for the subdistribution hazard frailty models. The proposed method is illustrated with a dataset from a multi-center clinical trial on breast cancer as well as with a simulation study. We also demonstrate how to present heterogeneity in treatment effects among centers by using a confidence interval for the frailty for each individual center and how to perform a statistical test for such heterogeneity using a restricted h-likelihood.

scholarly journals Barriers to recruiting underrepresented populations to cancer clinical trials: A systematic review

Cancer ◽  
2008 ◽  
Vol 112 (2) ◽  
pp. 228-242 ◽  
Author(s):  
Jean G. Ford ◽  
Mollie W. Howerton ◽  
Gabriel Y. Lai ◽  
Tiffany L. Gary ◽  
Shari Bolen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Cancer Clinical Trials ◽  
Underrepresented Populations

scholarly journals Rare use of patient-reported outcomes in childhood cancer clinical trials – a systematic review of clinical trial registries

2021 ◽  
Vol 152 ◽  
pp. 90-99
Author(s):  
David Riedl ◽  
Maria Rothmund ◽  
Anne-Sophie Darlington ◽  
Samantha Sodergren ◽  
Roman Crazzolara ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Childhood Cancer ◽  
Patient Reported Outcomes ◽  
Cancer Clinical Trials ◽  
Clinical Trial Registries ◽  
Patient Reported

Analysis of Adverse Events Attribution and Reporting in Cancer Clinical Trials: A Systematic Review

2021 ◽  
pp. 103296
Author(s):  
Diego Enrico ◽  
Federico Waisberg ◽  
Jeannette Burton ◽  
Pablo Mandó ◽  
Matías Chacón
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Adverse Events ◽  
Cancer Clinical Trials
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