PURPOSE: The European Randomized Study of Screening for Prostate Cancer (ERSPC) reported a 20% mortality reduction with prostate-specific antigen (PSA) screening. However, they estimated a number needed to screen (NNS) of 1,410 and a number needed to treat (NNT) of 48 to prevent one prostate cancer death at 9 years. Although NNS and NNT are useful statistics to assess the benefits and harms of an intervention, in a survival study setting such as the ERSPC, NNS and NNT are time specific, and reporting values at one time point may lead to misinterpretation of results. Our objective was to re-examine the effect of varying follow-up times on NNS and NNT using data extrapolated from the ERSPC report. MATERIALS AND METHODS: On the basis of published ERSPC data, we modeled the cumulative hazard function using a piecewise exponential model, assuming a constant hazard of 0.0002 for the screening and control groups for years 1 to 7 of the trial and different constant rates of 0.00062 and 0.00102 for the screening and control groups, respectively, for years 8 to 12. Annualized cancer detection and drop-out rates were also approximated based on the observed number of individuals at risk in published ERSPC data. RESULTS: According to our model, the NNS and NNT at 9 years were 1,254 and 43, respectively. Subsequently, NNS decreased from 837 at year 10 to 503 at year 12, and NNT decreased from 29 to 18. CONCLUSION: Despite the seemingly simplistic nature of estimating NNT, there is widespread misunderstanding of its pitfalls. With additional follow-up in the ERSPC, if the mortality difference continues to grow, the NNT to save a life with PSA screening will decrease.
PURPOSE: The European Randomized Study of Screening for Prostate Cancer (ERSPC) reported a 20% mortality reduction with prostate-specific antigen (PSA) screening. However, they estimated a number needed to screen (NNS) of 1,410 and a number needed to treat (NNT) of 48 to prevent one prostate cancer death at 9 years. Although NNS and NNT are useful statistics to assess the benefits and harms of an intervention, in a survival study setting such as the ERSPC, NNS and NNT are time specific, and reporting values at one time point may lead to misinterpretation of results. Our objective was to re-examine the effect of varying follow-up times on NNS and NNT using data extrapolated from the ERSPC report. MATERIALS AND METHODS: On the basis of published ERSPC data, we modeled the cumulative hazard function using a piecewise exponential model, assuming a constant hazard of 0.0002 for the screening and control groups for years 1 to 7 of the trial and different constant rates of 0.00062 and 0.00102 for the screening and control groups, respectively, for years 8 to 12. Annualized cancer detection and drop-out rates were also approximated based on the observed number of individuals at risk in published ERSPC data. RESULTS: According to our model, the NNS and NNT at 9 years were 1,254 and 43, respectively. Subsequently, NNS decreased from 837 at year 10 to 503 at year 12, and NNT decreased from 29 to 18. CONCLUSION: Despite the seemingly simplistic nature of estimating NNT, there is widespread misunderstanding of its pitfalls. With additional follow-up in the ERSPC, if the mortality difference continues to grow, the NNT to save a life with PSA screening will decrease.
Authors: Jonas Hugosson; Sigrid Carlsson; Gunnar Aus; Svante Bergdahl; Ali Khatami; Pär Lodding; Carl-Gustaf Pihl; Johan Stranne; Erik Holmberg; Hans Lilja Journal: Lancet Oncol Date: 2010-07-02 Impact factor: 41.316
Authors: Anna Bill-Axelson; Lars Holmberg; Frej Filén; Mirja Ruutu; Hans Garmo; Christer Busch; Stig Nordling; Michael Häggman; Swen-Olof Andersson; Stefan Bratell; Anders Spångberg; Juni Palmgren; Hans-Olov Adami; Jan-Erik Johansson Journal: J Natl Cancer Inst Date: 2008-08-11 Impact factor: 13.506
Authors: Ruth Etzioni; Alex Tsodikov; Angela Mariotto; Aniko Szabo; Seth Falcon; Jake Wegelin; Dante DiTommaso; Kent Karnofski; Roman Gulati; David F Penson; Eric Feuer Journal: Cancer Causes Control Date: 2007-11-20 Impact factor: 2.506
Authors: Georg Bartsch; Wolfgang Horninger; Helmut Klocker; Alexandre Pelzer; Jasmin Bektic; Wilhelm Oberaigner; Harald Schennach; Georg Schäfer; Ferdinand Frauscher; Mathieu Boniol; Gianluca Severi; Chris Robertson; Peter Boyle Journal: BJU Int Date: 2008-04 Impact factor: 5.588
Authors: Sigrid Carlsson; Andrew J Vickers; Monique Roobol; James Eastham; Peter Scardino; Hans Lilja; Jonas Hugosson Journal: J Clin Oncol Date: 2012-06-18 Impact factor: 44.544
Authors: Alvie Ahsan; Eva Zimmerman; Elisa Marie Rodriguez; Christy Widman; Deborah Oates Erwin; Frances Georgette Saad-Harfouche; Martin Christopher Mahoney Journal: J Cancer Treat Res Date: 2019-03-11