Literature DB >> 25624879

PACE Continuous Innovation Indicators-a novel tool to measure progress in cancer treatments.

Silvia Paddock1, Lauren Brum1, Kathleen Sorrow1, Samuel Thomas1, Susan Spence1, Catharina Maulbecker-Armstrong2, Clifford Goodman3, Michael Peake4, Gordon McVie5, Gary Geipel6, Rose Li1.   

Abstract

Concerns about rising health care costs and the often incremental nature of improvements in health outcomes continue to fuel intense debates about 'progress' and 'value' in cancer research. In times of tightening fiscal constraints, it is increasingly important for patients and their representatives to define what constitutes 'value' to them. It is clear that diverse stakeholders have different priorities. Harmonisation of values may be neither possible nor desirable. Stakeholders lack tools to visualise or otherwise express these differences and to track progress in cancer treatments based on variable sets of values. The Patient Access to Cancer care Excellence (PACE) Continuous Innovation Indicators are novel, scientifically rigorous progress trackers that employ a three-step process to quantify progress in cancer treatments: 1) mine the literature to determine the strength of the evidence supporting each treatment; 2) allow users to weight the analysis according to their priorities and values; and 3) calculate Evidence Scores (E-Scores), a novel measure to track progress, based on the strength of the evidence weighted by the assigned value. We herein introduce a novel, flexible value model, show how the values from the model can be used to weight the evidence from the scientific literature to obtain E-Scores, and illustrate how assigning different values to new treatments influences the E-Scores. The Indicators allow users to learn how differing values lead to differing assessments of progress in cancer research and to check whether current incentives for innovation are aligned with their value model. By comparing E-Scores generated by this tool, users are able to visualise the relative pace of innovation across areas of cancer research and how stepwise innovation can contribute to substantial progress against cancer over time. Learning from experience and mapping current unmet needs will help to support a broad audience of stakeholders in their efforts to accelerate and maximise progress against cancer.

Entities:  

Keywords:  cancer; indicators; innovation; progress; value

Year:  2015        PMID: 25624879      PMCID: PMC4303618          DOI: 10.3332/ecancer.2015.498

Source DB:  PubMed          Journal:  Ecancermedicalscience        ISSN: 1754-6605


  18 in total

1.  Will future progress in non-small-cell lung cancer be step by step... or by leaps and bounds?

Authors:  Benjamin Movsas
Journal:  J Clin Oncol       Date:  2005-08-08       Impact factor: 44.544

2.  Impact of cancer research bureaucracy on innovation, costs, and patient care.

Authors:  David P Steensma; Hagop M Kantarjian
Journal:  J Clin Oncol       Date:  2014-01-06       Impact factor: 44.544

3.  Breaking a vicious cycle.

Authors:  Daniel F Hayes; Jeff Allen; Carolyn Compton; Gary Gustavsen; Debra G B Leonard; Robert McCormack; Lee Newcomer; Kristin Pothier; David Ransohoff; Richard L Schilsky; Ellen Sigal; Sheila E Taube; Sean R Tunis
Journal:  Sci Transl Med       Date:  2013-07-31       Impact factor: 17.956

Review 4.  Dissemination and publication of research findings: an updated review of related biases.

Authors:  F Song; S Parekh; L Hooper; Y K Loke; J Ryder; A J Sutton; C Hing; C S Kwok; C Pang; I Harvey
Journal:  Health Technol Assess       Date:  2010-02       Impact factor: 4.014

5.  Rectal cancer trials: no movement.

Authors:  Martin R Weiser
Journal:  J Clin Oncol       Date:  2011-05-23       Impact factor: 44.544

6.  Lead time gained by diagnostic screening for breast cancer.

Authors:  G B Hutchison; S Shapiro
Journal:  J Natl Cancer Inst       Date:  1968-09       Impact factor: 13.506

Review 7.  Cancer stem cells: current status and evolving complexities.

Authors:  Jane E Visvader; Geoffrey J Lindeman
Journal:  Cell Stem Cell       Date:  2012-06-14       Impact factor: 24.633

8.  Randomized phase III study of surgery alone or surgery plus preoperative cisplatin and gemcitabine in stages IB to IIIA non-small-cell lung cancer.

Authors:  Giorgio V Scagliotti; Ugo Pastorino; Johan F Vansteenkiste; Lorenzo Spaggiari; Francesco Facciolo; Tadeusz M Orlowski; Luigi Maiorino; Martin Hetzel; Monika Leschinger; Carla Visseren-Grul; Valter Torri
Journal:  J Clin Oncol       Date:  2011-11-28       Impact factor: 44.544

9.  Sentinel lymph node surgery after neoadjuvant chemotherapy in patients with node-positive breast cancer: the ACOSOG Z1071 (Alliance) clinical trial.

Authors:  Judy C Boughey; Vera J Suman; Elizabeth A Mittendorf; Gretchen M Ahrendt; Lee G Wilke; Bret Taback; A Marilyn Leitch; Henry M Kuerer; Monet Bowling; Teresa S Flippo-Morton; David R Byrd; David W Ollila; Thomas B Julian; Sarah A McLaughlin; Linda McCall; W Fraser Symmans; Huong T Le-Petross; Bruce G Haffty; Thomas A Buchholz; Heidi Nelson; Kelly K Hunt
Journal:  JAMA       Date:  2013-10-09       Impact factor: 56.272

Review 10.  The evolution of assessing bias in Cochrane systematic reviews of interventions: celebrating methodological contributions of the Cochrane Collaboration.

Authors:  Lucy Turner; Isabelle Boutron; Asbjørn Hróbjartsson; Douglas G Altman; David Moher
Journal:  Syst Rev       Date:  2013-09-23
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  3 in total

1.  Survival Gains from First-Line Systemic Therapy in Metastatic Non-Small Cell Lung Cancer in the U.S., 1990-2015: Progress and Opportunities.

Authors:  Joshua A Roth; Bernardo H L Goulart; Arliene Ravelo; Holli Kolkey; Scott D Ramsey
Journal:  Oncologist       Date:  2017-02-27

2.  Approving cancer treatments based on endpoints other than overall survival: an analysis of historical data using the PACE Continuous Innovation Indicators™ (CII).

Authors:  Neon Brooks; Mario Campone; Silvia Paddock; Scott Shortenhaus; David Grainger; Jacqueline Zummo; Samuel Thomas; Rose Li
Journal:  Drugs Context       Date:  2017-11-15

3.  Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine.

Authors:  Silvia Paddock; Hamed Abedtash; Jacqueline Zummo; Samuel Thomas
Journal:  BMC Med Inform Decis Mak       Date:  2019-12-04       Impact factor: 2.796

  3 in total

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