Biostatistics Seminar
James Hanley, PhD
Professor, Department of Epidemiology, Biostatistics and Occupational Health, McGill University
Population data to measure mortality reductions produced by organized cancer screening: analyze with care
ALL ARE WELCOME
Abstract:
Although many of the trials were carried out decades ago, and did not necessarily produce valid or precise estimates of the reductions that might be expected from a sustained screening program, data from randomized cancer screening trials are still relied on by many task forces. Re-analyses of the published data from two trials will be used to illustrate why, if screening does what it is intended to do, hazard rates are automatically non-proportional; they cannot be handled within prevailing Cochrane meta-analysis practices.
Increasingly, the focus is on non-experimental evidence, i.e., data from populations where organized screening programs have been introduced.
In the evaluation of the impact of such programs, before-after comparisons of cancer mortality rates need to take account of concomitant improvements in cancer care over these same decades. Time-, age- and place-matched comparisons, and attention to which deaths could/could not be averted by the screening program, are essential for valid estimates of benefit.
Using organized population-based programs of mammography screening for breast cancer as an example, we show that by ignoring these issues, many of the prevailing statistical approaches to the analysis of such population-based data underestimate the mortality reductions produced by these programs. Statistical approaches that can deal with these 'dilutions' will be described.
[Joint work with Ailish Hannigan, Olli Saarela and Harald Weedon-Fekjaer; supported by CIHR]
Bio: