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Reconciling Results of PSA Screening Efficacy Using PLCO and ERSPC data: CISNET modeling project

Principal Investigator
Name
Angela Mariotto
Institution
NCI, DCCPS, DMB
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
2009-0182
Initial CDAS Request Approval
Nov 12, 2009
Title
Reconciling Results of PSA Screening Efficacy Using PLCO and ERSPC data: CISNET modeling project
Summary
The Cancer Intervention and Surveillance Modeling Network (CISNET) is a consortium of NCI funded modelers whose purpose is to utilize simulation and other modeling techniques to understand the impact of cancer control interventions on national trends in cancer rates. The CISNET prostate collaborative team, consists of three modeling groups, focused on modeling prostate cancer progression, screening and treatment in the US population, with the aims of quantifying the contribution of screening and treatment in the decline prostate cancer mortality, while waiting for final results on the efficacy of PSA from the two large randomized trials of PSA screening. Recently, the New England Journal of Medicine published early results of these two trials (1, 5). While the European Randomized Study of Screening for Prostate Cancer (ERSPC) indicated a benefit of screening, the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial showed no benefit. Although the trial results are apparently in conflict, there are many differences in their design and implementation that makes results comparison difficult. Modeling provides a unique opportunity to clarify the message on screening benefit contained in the two trials. The use of three different models enhances the reliability of modeling results and provides a sensitivity analysis on model structures and underlying assumptions. The objective of this collaboration is to use the three CISNET prostate cancer models to make inferences about the efficacy of PSA screening from the published trials by using more detailed data from the trials and to determine whether the trials provide consistent evidence regarding PSA screening efficacy.
Aims

1. To estimate, using standardized methods and models, screening frequencies in both arms of each trial: PLCO and ERSPC. 2. To fit/calibrate the three CISNET models to prostate cancer incidence in the control and intervention arms of each trial. Common input on PSA screening frequencies will be used. Re-calibration of the natural history parameters will be considered and documented. The models will attempt to fit the incidence data in each arm of each trial by time period, age, method of detection (screen detected or not), stage and grade. Specific questions being addressed include: How different do the natural history parameters need to be to reproduce cancer incidence in the PLCO and ERSPC trials? How much higher is incidence in the control arm of the PLCO as compared to the overall US? 3. To estimate initial treatment practices in both arms of each trial in a consistent manner. Since part of the decline in prostate cancer mortality can be attributed to improvements in prostate cancer treatment, it is important to estimate and compare treatment practices in each trial. 4. To fit, using the cancer incidence and treatment modeling results, the mortality data for each trial and specifically, to estimate the mean and variance of a screening survival benefit parameter for each trial. Each CISNET model will be restructured so that improvement in post-lead time survival due to screening is modeled in terms of a single survival benefit parameter (e.g., a hazard ratio or a relative cure probability). 5. Under the range of survival benefit parameter values consistent with the trial findings (e.g., 95% confidence interval), the models will project the benefits that would be expected when comparing a group screened according to a given protocol with 100% compliance to a completely unscreened group. This aim is therefore to project observed results to an uncontaminated version of the trials.

Collaborators

Eric J. Feuer (DCCPS)
Harry de Koning (Erasmus MC, The Netherlands)
Paul Pinsky (DCP)
Philip Prorok (DCP)
Ruth Etzioni (FHCRC)
Sue Moss (Institute of Cancer Research, UK)
Alex Tsodikov (University of Michigan)

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