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J Clin Oncol. 2011 February 1; 29(4): 464–467.
Published online 2010 December 28. doi:  10.1200/JCO.2010.30.6373
PMCID: PMC3058289

What Is the True Number Needed to Screen and Treat to Save a Life With Prostate-Specific Antigen Testing?



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.


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.


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.


Mortality from prostate cancer (PCa) has decreased substantially in the United States, coinciding with the initiation of widespread prostate-specific antigen (PSA) –based screening. From 1994 to 2006, mortality rates declined by an average of 4% per year, the most rapid decline observed for any cancer site.1 Mathematical models have estimated that the stage migration induced by screening likely accounts for 45% to 70% of the observed reduction in PCa mortality through 2000.2 Notably, a similar decline was observed in Tyrol, Austria after the introduction of a PSA screening program, compared with the rest of the country where screening and curative treatment were uncommonly performed.3

The European Randomized Study of Screening for Prostate Cancer (ERSPC) recently reported a 20% reduction in PCa mortality and a 41% reduction in metastatic disease at diagnosis in an intent-to-screen analysis conducted after a median follow-up time of 9 years.4 More recently, ERSPC estimated a mortality reduction of 31% after adjustment for noncompliance in the screening arm and contamination in the control arm.5 However, serious concerns were raised because the original ERSPC report included estimates indicating that a large number of men would have to be screened and treated to prevent one death from PCa.6 The number needed to treat (NNT) is a useful statistic to assess the balance of benefits and harms of an intervention.7 The goal of this study is to highlight some of the pitfalls in the calculation and interpretation of the NNT statistic and, in particular, to provide revised estimates of the NNT from the ERSPC trial accounting for the important effects of longer follow-up time.

Whether or not one accepts that PSA screening has a mortality benefit or at least reduces the incidence of metastatic disease, it must be acknowledged that screening programs engender costs at both the individual and societal level.8 Central to the debate over PSA screening are concerns regarding the diagnosis and treatment of tumors that may not cause harm.9 In the ERSPC trial, Schroder et al4 used the difference in cumulative mortality between the screening and control arms and the excess incidence of PCa in the screening arm to estimate a NNT of 48 to prevent one PCa death after a median follow-up time of 9 years. Because not every patient diagnosed with PCa will require treatment, NNT can be described more accurately in this context as the number needed to diagnose.10 The number needed to screen (NNS), which is simply the reciprocal of the absolute difference in cumulative mortality, was initially reported by ERSPC as 1,410 at the 9-year follow-up mark. This number can be reinterpreted as the number needed to be offered screening. The NNS was 1,068 when screening arm assignees who never underwent any screening were excluded.

Previous authors noted that the NNT statistic frequently has been used incorrectly in clinical trial reports in leading journals.11,12 NNT is easily understood when referring to proportions of patients assigned to each group at baseline but becomes more complex when dealing with differences in time-to-event data or event rates, which are based on actual person-time of observation. First, when rates, rather than proportions, are used as the basis for estimating NNT in the context of mortality, the NNT represents the amount of person-time (usually person-years), not the number of persons, that must be treated to prevent one death. Although this approach has been advocated as a way of standardizing the observation period and thus dealing with trials that have long and varying follow-up times for patients, the results are less intuitively appealing to clinicians, and their validity depends on the assumption that risk changes at a constant rate over time.1214 Second, in almost all long-term trials such as ERSPC, some participants are removed from observation (ie, censored as a result of death or loss to follow-up) at varying points during follow-up, and the rates of censoring can also vary between treatment groups. Ignoring censoring, particularly differential censoring, can distort estimates of NNT that are based on simple proportions. This risk of distortion can be mitigated by instead calculating NNT based on the survival curves (or equivalent cumulative hazard functions [CHFs]) for each treatment group derived from common statistics such as the Kaplan-Meier or Nelsen-Aalen estimators, which account for variations in follow-up time among patients.15 Schroder et al4 appear to have used the Nelsen-Aalen CHF in estimating NNS and NNT. Because recalculation of the NNS and NNT using simple proportions based on the number of patients at baseline and the number of PCa deaths in each group yields nearly identical results, we assume that the pattern of censoring was approximately equivalent in each arm of the trial.

In the current analysis, however, we focused on another concern that we believe has a major effect on interpretation of NNT and NNS in the ERSPC, namely the fact that these statistics are time specific and will change as the risks for the treatment groups either converge or diverge over time. By extracting hazard rate estimates from the authors' Nelsen-Aalen curves and applying those rates in an appropriate model, we calculated predicted NNS and NNT estimates for different periods of follow-up.


We modeled the CHF of PCa-specific mortality for each treatment group using a piecewise exponential (PWE) model. The PWE model is a widely used approach to survival analysis that is particularly suited for situations involving nonproportional hazards (ie, those such as ERSPC where the relative hazards for PCa death and, therefore, the absolute mortality differences change over time).16,17 PWE models incorporate covariates into an actuarial life-table approach to survival analysis in much the way the Cox model incorporates covariates into a Kaplan-Meier approach. Unlike the Cox model, which does not specify any baseline hazard rate, the PWE model divides follow-up time into discrete, nonoverlapping intervals. The baseline hazard (ie, excluding the effects of covariates) can vary from one interval to the next but remains constant within the interval. The PWE and Cox models have been shown to yield nearly equivalent results for estimating covariate effects in many situations. However, the PWE model allows one to make predictions for individual patients based on covariate histories and, more to the point here, allows flexibility in defining the shape of the hazard function over time.16,18 This flexibility is important in situations such as PCa screening trials where the delayed emergence of a mortality benefit can be expected.

In our model, we assumed a constant hazard of 0.0002 for both the screening and control groups for years 1 to 7 of the trial. This is based on assuming the CHF to be 0.001 at 5 years based directly on the estimated CHF shown in Figure 2 of Schroder et al.4 Similarly, for years 8 to 12 of the trial, we assumed different constant rates of 0.00062 for the screening group (assuming a CHF of 0.0045 at 12 years) and 0.00102 for the control group (assuming a CHF of 0.0065 at 12 years), all based directly on Figure 2 of Schroder et al.4 Given this nonproportional hazards assumption, we computed PCa-free survival and cumulative hazard ratios over time as a function of the CHF. Annualized cancer detection and drop-out rates were also approximated based on the observed number of individuals at risk in published ERSPC data.4


Figure 1 compares the modeled CHFs to published data from the ERSPC. According to our model, the NNS and NNT at 9 years were 1,254 and 43, respectively (Table 1); these numbers are close to the published figures of 1,410 and 48, respectively. Our model also corresponds to a cumulative hazard ratio of 0.77, similar to the crude hazard ratio of 0.80 from the ERSPC report. Subsequently, the NNS decreased from 837 at year 10 to 503 at year 12, and the NNT decreased from 29 at year 10 to 18 at year 12, an estimate that is similar to the one determined by Welch et al9 using population data from the Surveillance, Epidemiology, and End Results program and by Bill-Axelson et al19 based on a randomized trial of surgery versus no treatment for PCa. Finally, Hugosson et al10 recently reported results from the Goteborg PCa screening trial, which was designed independently but included a subset of participants from the ERSPC. Using data from extended follow-up, these investigators calculated an NNS of 293 and NNT of 12 to prevent one PCa death at a median follow-up time of 14 years, suggesting that the estimates from our PWE model are highly plausible. We note that the NNS and NNT estimates from the Goteborg trial,10 unlike the ERSPC results, seem to have been based on simple proportions and may have been overestimated. The NNS calculated using the inverse of the difference (0.40%) in the Kaplan-Meier cumulative risk of PCa death is 250.

Fig 1.
Modeled cumulative hazard functions assuming a piecewise exponential model. NNT, number needed to treat; NNS, number needed to screen; CHR, cumulative hazard ratio; HR, hazard ratio.
Table 1.
Modeled Results Assuming a Piecewise Exponential Model


Overall, our results demonstrate that NNS and NNT are highly sensitive to the time-dependent effects of the screening intervention on PCa mortality. Accordingly, estimates of NNS and NNT at a single time point during a survival study may be misleading. In addition to their dependence on time of follow-up and changes in the slopes of the hazard functions, NNS and NNT estimates from an intent-to-treat analysis may also be influenced by other features of a screening study, such as noncompliance and contamination. It is clear, based on both the CHF reported by Schroder et al4 and the estimated CHF using our PWE model (Fig 1), that the hazard rates for PCa mortality are not proportional over time and that there is a sharp increase in PCa-related deaths after 7 years that must be accounted for when estimating NNS and NNT as a function of time. Indeed, because of the long natural history of PCa, a follow-up time of more than 10 years is necessary to evaluate cancer-specific mortality.

Despite the seemingly simplistic nature of estimating NNT, there is widespread misunderstanding of its pitfalls among the medical community, the media, and the general public. Specifically, in the setting of a survival study such as the ERSPC, quoting one set of values for NNS and NNT at a single time point may be misleading. With additional follow-up in the ERSPC, the mortality difference between the screening and control arms will likely continue to grow, thus leading to further decreases in the NNT estimates.


See accompanying editorial on page 345 and article on page 355

Supported by the Urological Research Foundation, Prostate Specialized Programs of Research Excellence Grant No. P50 CA90386-05S2, Robert H. Lurie Comprehensive Cancer Center Grant No. P30 CA60553 (W.J.C.) and the Intramural Research Program of the National Institutes of Health, National Institute on Aging (E.J.M.).

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.


Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment or Leadership Position: None Consultant or Advisory Role: William J. Catalona, Beckman Coulter (U), Ohmx (U) Stock Ownership: None Honoraria: William J. Catalona, Beckman Coulter, GlaxoSmithKline Research Funding: William J. Catalona, Beckman Coulter Expert Testimony: None Other Remuneration: None


Conception and design: Stacy Loeb, Edward F. Vonesh, E. Jeffrey Metter, H. Ballentine Carter, Peter H. Gann, William J. Catalona

Administrative support: Edward F. Vonesh, E. Jeffrey Metter, H. Ballentine Carter, William J. Catalona

Provision of study materials or patients: Stacy Loeb, Edward F. Vonesh, William J. Catalona

Collection and assembly of data: Stacy Loeb, Edward F. Vonesh, E. Jeffrey Metter, H. Ballentine Carter, Peter H. Gann, William J. Catalona

Data analysis and interpretation: Stacy Loeb, Edward F. Vonesh, E. Jeffrey Metter, H. Ballentine Carter, Peter H. Gann, William J. Catalona

Manuscript writing: Stacy Loeb, Edward F. Vonesh, E. Jeffrey Metter, H. Ballentine Carter, Peter H. Gann, William J. Catalona

Final approval of manuscript: Stacy Loeb, Edward F. Vonesh, E. Jeffrey Metter, H. Ballentine Carter, Peter H. Gann, William J. Catalona


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