||1U01CA253913-01 Interpret this number
||Sloan-Kettering Inst Can Research
||Comparative Modeling of Effective Policies for Colorectal Cancer Control
Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The long-term goal
of our proposed project is to reduce the population burden of CRC by providing the information needed to
address key policy questions across the CRC control continuum in an accessible and transparent manner. To
accomplish this goal we will use our population-based microsimulation models to: 1) Evaluate the impact of
screening as practiced in the US; 2) Inform the debate about the increase in CRC incidence before age 50; 3)
Consider the effectiveness of precision of screening and surveillance; 4) Address other emerging issues and
opportunities in CRC control; and 5) Use novel methods to improve model accessibility and transparency. Our
team will fill critical gaps in knowledge, enabling decision makers to act. New evidence that we will incorporate
in our models to better inform CRC control opportunities will be 1) updated information on screening patterns in
the US (in collaboration with the Population-based Research Optimizing Screening through Personalized
Regimen, or PROSPR), 2) data on the increased risk of CRC in persons under age 50 (in collaboration with
Rebecca Siegal of the American Cancer Society, who did the seminal work in this area), and 3) state-of-the art
colonoscopy screening data to incorporate alternative carcinogenesis pathways in the natural history models
(in collaboration with the New Hampshire Colonoscopy Registry). We will synthesize and incorporate the
growing body of evidence in the literature to assess the clinical utility of personalized screening and treatment,
as well as the potential role for novel computer-aided detection and diagnosis modalities. We will expand our
models to project clinical and resource-based outcomes for middle-income countries that are considering the
implementation of a screening program. Lastly, there is a critical need to make our models assessible and
transparent. To this end we will use high performance computing approaches to develop and apply deep-
learning methods for model calibration and model emulation, which will aid in model sharing. The three
participating modeling groups are well positioned to carry out this work, bringing a wealth of experience,
expertise, and insight to issues related to microsimulation modeling of CRC, and have a proven track record of
collaboration and disseminating our work to health policy decision makers.
Colorectal Cancer Screening: An Updated Modeling Study for the US Preventive Services Task Force.
, Rutter C.M.
, Peterse E.F.P.
, Lietz A.P.
, Seguin C.L.
, Meester R.G.S.
, Perdue L.A.
, Lin J.S.
, Siegel R.L.
, Doria-Rose V.P.
, et al.
JAMA, 2021-05-18; 325(19), p. 1998-2011.
Comparative benefit and cost-effectiveness of mailed-out faecal immunochemical tests vs collection at the general practitioner.
, Osoro C.B.
, Bardou M.
, Lansdorp-Vogelaar I.
Alimentary pharmacology & therapeutics, 2021 05; 53(10), p. 1118-1125.
MICROSIMULATION MODEL CALIBRATION USING INCREMENTAL MIXTURE APPROXIMATE BAYESIAN COMPUTATION.
, Ozik J.
, DeYoreo M.
, Collier N.
The annals of applied statistics, 2019 Dec; 13(4), p. 2189-2212.
A Gaussian Approximation Approach for Value of Information Analysis.
, Alarid-Escudero F.
Medical decision making : an international journal of the Society for Medical Decision Making, 2018 02; 38(2), p. 174-188.