Literature DB >> 21624962

Measuring mortality reductions in cancer screening trials.

James A Hanley1.   

Abstract

Randomized trials involving large numbers of people and long follow-up have helped measure the mortality reductions achievable by screening for cancer. However, in many of these trials, the reported reductions have been modest. Part of the reason is the inappropriate way the reductions have been calculated. Analyses have largely ignored the fact that there is a time window in the first several years after screening begins in which there cannot be a sizable mortality reduction, followed by one in which the reductions become evident, and-unless screening is continued-a third window in which mortality rates in the screened group revert to those in the unscreened group. This review uses time-specific mortality ratios to address the timing and extent of the reductions achieved in trials of screening for prostate, breast, and colorectal cancer. The author finds that the mortality reductions reported in the literature have substantially underestimated what might be accomplished with continued screening. The natural history of the disease, the frequency of screening, and the duration of follow-up determine the time patterns in the reductions observed in trials. Without appropriate analyses, results from cancer screening trials will be distorted.

Entities:  

Mesh:

Year:  2011        PMID: 21624962     DOI: 10.1093/epirev/mxq021

Source DB:  PubMed          Journal:  Epidemiol Rev        ISSN: 0193-936X            Impact factor:   6.222


  10 in total

Review 1.  The benefits and harms of breast cancer screening: an independent review.

Authors:  M G Marmot; D G Altman; D A Cameron; J A Dewar; S G Thompson; M Wilcox
Journal:  Br J Cancer       Date:  2013-06-06       Impact factor: 7.640

2.  Expected population impacts of discontinued prostate-specific antigen screening.

Authors:  Roman Gulati; Alex Tsodikov; Ruth Etzioni; Rachel A Hunter-Merrill; John L Gore; Angela B Mariotto; Matthew R Cooperberg
Journal:  Cancer       Date:  2014-07-25       Impact factor: 6.860

3.  Disaggregating the mortality reductions due to cancer screening: model-based estimates from population-based data.

Authors:  James Anthony Hanley; Sisse Helle Njor
Journal:  Eur J Epidemiol       Date:  2017-12-05       Impact factor: 8.082

4.  Response: Reading between the lines of cancer screening trials: using modeling to understand the evidence.

Authors:  Ruth Etzioni; Roman Gulati
Journal:  Med Care       Date:  2013-04       Impact factor: 2.983

Review 5.  What do the screening trials really tell us and where do we go from here?

Authors:  Ruth D Etzioni; Ian M Thompson
Journal:  Urol Clin North Am       Date:  2014-02-19       Impact factor: 2.241

6.  Men (aged 40-49 years) with a single baseline prostate-specific antigen below 1.0 ng/mL have a very low long-term risk of prostate cancer: results from a prospectively screened population cohort.

Authors:  Christopher J Weight; Simon P Kim; Debra J Jacobson; Michaela E McGree; R Jeffrey Karnes; Jennifer St Sauver
Journal:  Urology       Date:  2013-10-19       Impact factor: 2.649

7.  Mammograms and Mortality: How Has the Evidence Evolved?

Authors:  Amanda E Kowalski
Journal:  J Econ Perspect       Date:  2021

8.  All-cause mortality versus cancer-specific mortality as outcome in cancer screening trials: A review and modeling study.

Authors:  Eveline A M Heijnsdijk; Marcell Csanádi; Andrea Gini; Kevin Ten Haaf; Rita Bendes; Ahti Anttila; Carlo Senore; Harry J de Koning
Journal:  Cancer Med       Date:  2019-08-18       Impact factor: 4.452

9.  UKCTOCS update: applying insights of delayed effects in cancer screening trials to the long-term follow-up mortality analysis.

Authors:  Matthew Burnell; Aleksandra Gentry-Maharaj; Steven J Skates; Andy Ryan; Chloe Karpinskyj; Jatinderpal Kalsi; Sophia Apostolidou; Naveena Singh; Anne Dawnay; Robert Woolas; Lesley Fallowfield; Stuart Campbell; Alistair McGuire; Ian J Jacobs; Mahesh Parmar; Usha Menon
Journal:  Trials       Date:  2021-03-01       Impact factor: 2.279

Review 10.  Statistical analysis and decision making in cancer screening.

Authors:  Dik Habbema
Journal:  Eur J Epidemiol       Date:  2018-05-12       Impact factor: 8.082

  10 in total

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