PMID- 29254526
OWN - NLM
STAT- MEDLINE
DCOM- 20180731
LR  - 20181202
IS  - 1873-2607 (Electronic)
IS  - 0749-3797 (Linking)
VI  - 54
IP  - 1S1
DP  - 2018 Jan
TI  - Collaborative Modeling: Experience of the U.S. Preventive Services Task Force.
PG  - S53-S62
LID - S0749-3797(17)30378-1 [pii]
LID - 10.1016/j.amepre.2017.07.003 [doi]
AB  - Models can be valuable tools to address uncertainty, trade-offs, and preferences 
      when trying to understand the effects of interventions. Availability of results 
      from two or more independently developed models that examine the same question 
      (comparative modeling) allows systematic exploration of differences between 
      models and the effect of these differences on model findings. Guideline groups 
      sometimes commission comparative modeling to support their recommendation 
      process. In this commissioned collaborative modeling, modelers work with the 
      people who are developing a recommendation or policy not only to define the 
      questions to be addressed but ideally, work side-by-side with each other and with 
      systematic reviewers to standardize selected inputs and incorporate selected 
      common assumptions. This paper describes the use of commissioned collaborative 
      modeling by the U.S. Preventive Services Task Force (USPSTF), highlighting the 
      general challenges and opportunities encountered and specific challenges for some 
      topics. It delineates other approaches to use modeling to support evidence-based 
      recommendations and the many strengths of collaborative modeling compared with 
      other approaches. Unlike systematic reviews prepared for the USPSTF, the 
      commissioned collaborative modeling reports used by the USPSTF in making 
      recommendations about screening have not been required to follow a common format, 
      sometimes making it challenging to understand key model features. This paper 
      presents a checklist developed to critically appraise commissioned collaborative 
      modeling reports about cancer screening topics prepared for the USPSTF.
CI  - Copyright (c) 2017 American Journal of Preventive Medicine. All rights reserved.
FAU - Petitti, Diana B
AU  - Petitti DB
AD  - Department of Biomedical Informatics, College of Medicine-Phoenix, University of 
      Arizona, Phoenix, Arizona. Electronic address: diana.petitti@yahoo.com.
FAU - Lin, Jennifer S
AU  - Lin JS
AD  - Kaiser Permanente Center for Health Research, Portland, Oregon.
FAU - Owens, Douglas K
AU  - Owens DK
AD  - VA Palo Alto Health Care System, Palo Alto, California; Center for Primary Care 
      and Outcomes Research, Department of Medicine, School of Medicine, Stanford 
      University, Stanford, California.
FAU - Croswell, Jennifer M
AU  - Croswell JM
AD  - Patient-Centered Outcomes Research Institute, Washington, District of Columbia.
FAU - Feuer, Eric J
AU  - Feuer EJ
AD  - Division of Cancer Control and Population Sciences, National Cancer Institute, 
      Bethesda, Maryland.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Am J Prev Med
JT  - American journal of preventive medicine
JID - 8704773
SB  - IM
MH  - Advisory Committees/*standards
MH  - Checklist/statistics & numerical data
MH  - Computer Simulation/*statistics & numerical data
MH  - Evidence-Based Medicine
MH  - Humans
MH  - Preventive Health Services/methods/*standards
MH  - United States
EDAT- 2017/12/20 06:00
MHDA- 2018/08/01 06:00
CRDT- 2017/12/20 06:00
PHST- 2017/03/30 00:00 [received]
PHST- 2017/06/12 00:00 [revised]
PHST- 2017/07/06 00:00 [accepted]
PHST- 2017/12/20 06:00 [entrez]
PHST- 2017/12/20 06:00 [pubmed]
PHST- 2018/08/01 06:00 [medline]
AID - S0749-3797(17)30378-1 [pii]
AID - 10.1016/j.amepre.2017.07.003 [doi]
PST - ppublish
SO  - Am J Prev Med. 2018 Jan;54(1S1):S53-S62. doi: 10.1016/j.amepre.2017.07.003.