Effect of Screening and Adjuvant Therapy on Mortality from Breast Cancer
Donald A. Berry, Ph.D., Kathleen A. Cronin, Ph.D., Sylvia K. Plevritis, Ph.D., Dennis G. Fryback, Ph.D., Lauren Clarke, M.S., Marvin Zelen, Ph.D., Jeanne S. Mandelblatt, Ph.D., Andrei Y. Yakovlev, Ph.D., J. Dik F. Habbema, Ph.D., Eric J. Feuer, Ph.D., for the Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators
Background We used modeling techniques to assess the relativeand absolute contributions of screening mammography and adjuvanttreatment to the reduction in breast-cancer mortality in theUnited States from 1975 to 2000.
Methods A consortium of investigators developed seven independentstatistical models of breast-cancer incidence and mortality.All seven groups used the same sources to obtain data on theuse of screening mammography, adjuvant treatment, and benefitsof treatment with respect to the rate of death from breast cancer.
Results The proportion of the total reduction in the rate ofdeath from breast cancer attributed to screening varied in theseven models from 28 to 65 percent (median, 46 percent), withadjuvant treatment contributing the rest. The variability acrossmodels in the absolute contribution of screening was largerthan it was for treatment, reflecting the greater uncertaintyassociated with estimating the benefit of screening.
Conclusions Seven statistical models showed that both screeningmammography and treatment have helped reduce the rate of deathfrom breast cancer in the United States.
The Cancer Intervention and Surveillance Modeling Network (CISNET)is a consortium of investigators sponsored by the National CancerInstitute whose purpose is to measure the effect of cancer-controlinterventions on the incidence of and risk of death from cancerin the general population. This report of the CISNET BreastCancer Working Group provides estimates of the contributionsof screening mammography and adjuvant treatment to the reductionin the rate of death from breast cancer among U.S. women from1975 to 2000.
In 1975, the rate of death from breast cancer among women 30to 79 years of age, adjusted for age to the 2000 population,was 48.3 deaths per 100,000 women. By 1990, the rate had increasedslightly to 49.7 per 100,000, but then fell to 38.0 per 100,000by 2000, a decrease of 24 percent from 1990.1 Similar reductionshave been observed in other countries.2 Likely explanationsfor these reductions are early detection by mammographic screeningand advances in treatment.
Screening can reduce the rate of death from breast cancer onlywhen followed by treatment. Tumors that are detected by screeningbefore they metastasize can be cured by surgery, and breastcancer detected at an early stage of metastasis can be effectivelytreated by chemotherapy.3 Currently, most organizations recommendthat women begin to undergo screening mammography in their 40s.4,5,6By the year 2000, about 70 percent of women over the age of40 years reported undergoing mammography in the previous twoyears.7
Controversies over the value of screening mammography arosefrom questions regarding the quality of the randomized trialsthat evaluated the effectiveness of this approach.6,8,9,10 Moreover,the most recent estimates of benefits of screening from someof these trials are substantially lower than earlier estimates.9Even if screening trials provided undisputed evidence of a reducedrate of death from breast cancer, it is unclear how such a findingwould translate to the general population.
The use of adjuvant chemotherapy and tamoxifen for all stagesof breast cancer has also increased. Randomized clinical trialshave demonstrated survival benefits associated with the useof adjuvant therapies, with estimated reductions in the annualodds of death ranging from 8 percent to 28 percent, dependingon the type and duration of therapy, the age of the patients,and the characteristics of the tumor.11,12,13 However, the extentto which these benefits translate to the population outsidethe controlled conditions of clinical trials is unknown. Furthermore,whether advances in adjuvant treatment have diminished or enhancedthe value of screening has not, to our knowledge, been analyzedpreviously.
Knowing whether screening, adjuvant therapy, or both have contributedto the recent reductions in the rate of death from breast canceris important. Since no single national registry contains allthe data needed to evaluate the effect of these interventionsat the population level, a way of integrating relevant informationfrom separate population databases is required. The NationalInstitutes of Health selected seven groups to develop modelsof mortality from breast cancer in the United States. Thesemodels made use of the best information available from the period1975 to 2000 as common sources of data but applied differentmodeling approaches. The results of these various approachesdiffered, but there were points of agreement in the overallconclusions.
Methods
The National Institutes of Health used a competitive peer-reviewprocess to choose seven groups to model the effect of screeningand treatment on trends in the incidence of and rate of deathfrom breast cancer: the Dana–Farber Cancer Institute,Boston (model D); Erasmus University Medical Center, Rotterdam,the Netherlands (model E); Georgetown University, Washington,D.C. (model G); the M.D. Anderson Cancer Center, Houston (modelM); Stanford University, Stanford, California (model S); theUniversity of Rochester, Rochester, New York (model R); andthe University of Wisconsin–Madison, Madison (model W).14The joint analysis was designed by the CISNET consortium of43 investigators (listed in the Appendix). The seven groupsworked independently to develop their models but interactedas a consortium to investigate shared problems and to facilitatecomparisons by developing uniform reporting structures. Thesecomparisons allowed the modelers to identify differences inapproaches and underlying assumptions and helped identify errors,but the general modeling approach and model structures werenot modified to achieve consistency among the seven groups.The National Cancer Institute prepared databases of the informationused in all seven models; individual groups collected data requiredfor their particular models. Data used by all seven groups arepublicly accessible, and additional detail related to the variablescan be found on the CISNET Web site (http://cisnet.cancer.gov/).
The consortium's analyses relied on the incidence of breastcancer as reported by the Surveillance, Epidemiology, and EndResults (SEER) program and the rate of death from breast canceras reported by the National Center for Health Statistics (NCHS).Because neither SEER nor NCHS contains information on screeninghistory or mode of detection and SEER underreports the use ofadjuvant therapy,15 we incorporated additional databases concerninguses of screening and treatment and their efficacy in the population.In the first phase of our collaboration, we jointly developedand agreed on a set of common, or "base-case," variables tomake possible comparisons of the models. The base-case variablesand the data sources that were used to estimate them are listedin Table 1. Base-case variables include the background incidenceof breast cancer in the absence of screening; the use of adjuvanttreatment according to the patients' age, receptor status, calendaryear, and tumor stage; and the risk of death from other causes.The background trend was estimated from the Connecticut TumorRegistry and SEER with the use of an age-period cohort model.16
Cohort-specific screening patterns were estimated from dataon the percentage of the population that had ever undergonemammography, as reported in the National Health Interview Survey7,17and the screening patterns from population-based data on theuse of mammography collected by the Breast Cancer SurveillanceConsortium for the period 1994 to 2000.18
We used breast-cancer–specific survival curves derivedfrom SEER for patients who received a diagnosis of breast cancerfrom 1975 to 1979 to estimate the rate of death among patientswho did not receive chemotherapy or tamoxifen. The birth-cohort–specificrate of death from any cause, obtained from the Human MortalityDatabase,19 was adjusted to exclude deaths from breast cancer.
Table 2 summarizes the base-case variables and other distinguishingcharacteristics of the models. Six of the seven groups estimatedthe benefit derived from screening and treatment with the useof a model of the natural history of invasive breast cancer,with three groups (models E, G, and W) also modeling progressionfrom ductal carcinoma in situ to invasive disease. These threegroups included ductal carcinoma in situ as an early, precursorstage of invasive breast cancer. The other models consideredonly invasive cancer.
Model M did not use a model of the natural history of the diseasebut used Bayesian updating of model variables — includingthe results of treatment trials — to estimate the contributionsof the various interventions. Group M simulated screening andbreast-cancer histories of women who were alive in 1975. Themodel allowed for the possibility that patients with tumorsdetected by screening would survive longer than patients whosecancer was detected on the basis of symptoms, even in the caseof tumors at the same stage in both settings.
In all models except model R, patients with breast cancer eitherreceived adjuvant treatment (tamoxifen, chemotherapy, or both)or did not receive adjuvant treatment, depending on the year,the age of the patients, tumor stage, and estrogen-receptorstatus. A survival benefit of tamoxifen was applied only topatients with estrogen-receptor–positive tumors and dependedon the duration of treatment (two or five years). The estimatedrelative-risk ratios for death from any cause were 0.82 fortwo years of tamoxifen therapy and 0.72 for five years of therapy.13The relative-risk ratio for chemotherapy depended on the ageat detection: 0.73 for women younger than 50 years of age, 0.86for women 50 to 59 years of age, and 0.92 for women 60 yearsof age or older.11
The University of Rochester group (Group R) used SEER data tocalibrate their model according to the effect of treatment andthe change in survival over time, controlling for age, tumorsize, and clinical stage. Thus, the treatment variable in modelR included not only adjuvant therapy but also the possibilitythat surgical and radiation-therapy procedures and patient caremore generally resulted in prolonged survival for patients withbreast cancer.
All the models measured the contribution or effect of variousfactors, such as the use of screening and the reduction in therelative-risk ratio for death owing to five years of tamoxifentherapy. The modelers had to estimate the effect of these factors.Three models (models M, R, and W) were calibrated accordingto the observed rate of death from breast cancer in the UnitedStates, in that they gave more weight to data that producedresults consistent with the observed rates from 1975 to 2000.The other four groups did not fit variables on the basis ofthe observed rate of death from breast cancer in the UnitedStates but instead relied on a variety of data sources and processesto derive mortality trends. More detailed descriptions of theindividual models are available in the Supplementary Appendix(available with the full text of this article at www.nejm.org)and at http://cisnet.cancer.gov/resources/.23
To produce estimates of the effects of screening and adjuvanttherapy from 1975 to 2000, the models were used to simulatetrends in the incidence of and rate of death from breast cancerunder four scenarios: no screening and no adjuvant therapy;use of base-case screening, but no adjuvant therapy; no screening,but base-case use of adjuvant therapy; and base-case screeningand base-case use of adjuvant therapy ("base-case" correspondsto the estimated actual use of screening and adjuvant therapyfrom 1975 to 2000). The models estimated the contribution ofscreening in the absence of treatment by comparing the firstand second scenarios, the effect of treatment in the absenceof screening by comparing the first and third scenarios, andthe effect of both screening and treatment by comparing theestimated individual contributions with the combined contributionas estimated by comparing the base-case (fourth) scenario withthe first scenario.
The models provide estimates of absolute reductions in the rateof death in 2000 owing to the use of tamoxifen, chemotherapy,these two treatments combined, screening, and all these factorscombined. We present and plot the seven pairs of estimates (declinesin the rate of death from breast cancer due to screening andadjuvant therapy) that were produced by the models. For thepurposes of representing the uncertainty in these estimates,we regarded these seven pairs to be a sample from a larger populationof possible model results. Using a statistical method calledkernel estimation,24 we estimated the distribution of this largerpopulation. We used a bivariate normal kernel with a standarddeviation and correlation taken from the data plot of the sevenestimates.
Results
Figure 1A shows the use of screening (annual, every two years,irregular, and never) among women who were at least 40 yearsof age in calendar years 1985, 1990, 1995, and 2000.25 The useof screening increased dramatically during this period. Theuse of adjuvant treatment depended on the calendar year, theage of the patients, the tumor stage, and estrogen-receptorstatus.26 These data were derived from SEER treatment informationadjusted for underreporting as assessed from SEER patterns-of-carestudies.15Figure 1B shows representative curves describingthe use of treatment for node-positive stage II or stage IIIAdisease among women 50 to 69 years of age. The percentage ofwomen who received chemotherapy increased from essentially 0percent in 1975 to about 80 percent in 2000. Similarly, theuse of tamoxifen increased from essentially 0 percent in 1975to about 50 percent in 2000; this drug was used mainly in patientswith estrogen-receptor–positive tumors.
Figure 1. Changes in the Pattern of Use of Screening Mammography among Women 40 to 79 Years of Age (Panel A) and in the Use of Adjuvant Therapy among Women 50 to 69 Years of Age with Node-Positive Stage II or IIIA Breast Cancer (Panel B).
The models used these data as common variables. In Panel B, the tamoxifen-only curve drops during the 1990s because of the increased use of adjuvant chemotherapy for patients with estrogen-receptor–positive tumors; the overall rate of use of tamoxifen, with and without chemotherapy, in the 1990s is nearly constant at about 60 percent of patients, mostly those with estrogen-receptor–positive tumors. The use of chemotherapy (calculated by adding the blue and red curves) increased from 55 percent to 87 percent during the same period.
Figure 2A shows the rates of death from breast cancer from 1975to 2000 as estimated by the seven models, in comparison withthe actual rate in the United States. Models that were calibratedaccording to the actual mortality rate (models M, R, and W)tend to fit better than those that were not calibrated in thismanner. However, the shapes of the seven curves are similar.Moreover, Figure 2A shows that all models predict similar proportionalreductions in mortality from the combination of screening andadjuvant therapy. Figure 2B shows the age-adjusted rate of deathfrom breast cancer in the absence of screening or adjuvant treatmentor both for a single model. We selected Model W for this demonstrationbecause its results were typical. This figure shows that theestimated rate of death from breast cancer in the absence ofscreening and adjuvant therapy would have increased by about30 percent from 1975 to 2000. This increase derives from themodeled background trend in the incidence of breast cancer inthe absence of screening.
Figure 2. Estimated and Actual Rates of Death from Breast Cancer among Women 30 to 79 Years of Age from 1975 to 2000 (Panel A) and under Hypothetical Assumptions about the Use of Screening Mammography and Adjuvant Treatment (Panel B).
Panel A, which compares the model-based results with the actual rates in the United States from 1975 to 2000, shows the variability across the model estimates. Some of the models were calibrated according to the observed rate of death from breast cancer in the United States, and some were not. Panel B shows the results from model W (the University of Wisconsin–Madison) of estimated mortality trends for the four scenarios considered: no screening and no adjuvant treatment; base-case screening, but no adjuvant treatment; no screening, but base-case adjuvant treatment; base-case screening and adjuvant treatment. Rates in both panels are age-adjusted to the 2000 U.S. standard.
Table 3 gives the estimated reductions in the rate of deathin 2000 owing to the use of tamoxifen therapy, chemotherapy,both therapies combined, screening, and the overall combination.The decline in the rate of death from breast cancer in 2000attributable to the combination of screening and adjuvant therapyranges from 24.9 percent to 38.3 percent, which because of theincreasing background trend (that is, with no screening) islarger than the actual decline of 21.3 percent from 1975 to2000. However, in these models, the relative contributions ofscreening and treatment to the reduction in the rate of deathfrom breast cancer are insensitive to variations in the backgroundtrend of the incidence of breast cancer. We estimated that screeningas practiced in the United States reduced the rate of deathfrom breast cancer in the range of 7 to 23 percent across theseven models, with a median of 15 percent. The percentage ofthe reduction attributable to adjuvant therapy was in the rangeof 12 to 21 percent, with a median of 19 percent. The combinationof screening and adjuvant therapy reduced the rate of deathby an estimated 25 to 38 percent, with a median of 30 percent.For each of the seven models, the combination of screening andadjuvant therapy reduced the rate of death by slightly lessthan the sum of the contributions from screening and adjuvanttherapy alone. As indicated in Table 3, the proportion of thedecrease in the rate of death from breast cancer that was attributableto screening ranged from 28 percent to 65 percent, with a medianof 46 percent.
Table 3. Estimated Reductions in the Rate of Death from Breast Cancer in 2000 Attributed to Adjuvant Treatments and Screening.
Figure 3A shows a contour plot of the estimated distributionof a larger population of model results from which our sevenmodels represent a sample. When considering the results of allseven models, the most likely conclusion is that the contributionsof screening and adjuvant treatment are similar. The spreadof the distribution shown in Figure 3B reflects the uncertaintythat is present in the available data and the differences inthe modeling approaches.
Figure 3. Estimated Joint Distribution of the Reduction in the Rate of Death from Breast Cancer among U.S. Women 30 to 79 Years of Age Attributed to Adjuvant Treatment and to Screening Mammography.
Values are compared with the rate of death in 2000 in the absence of both screening and treatment. Panel A shows the point estimates from the individual models (designated by their letters) provided in Table 3. The distribution contours for the combined model results are derived by kernel-density estimation; each contour shows the locus of points having a constant density. Each model's point estimate is assumed to be at the mean of its own bivariate normal density whose covariance structure was estimated from that of the seven model estimates. These seven densities were then averaged with equal weights to obtain an estimated posterior joint distribution. The "hill" in Panel B is a three-dimensional rendering of the contour plot. The height of the hill shows the likelihood of the corresponding reductions due to screening and treatment. For example, a point near the top of the hill (from 10 to 15 percent for screening and from 15 to 20 percent for treatment) is about twice as likely to be the actual state than is a point on the third largest contour in Panel A.
Discussion
We present results from seven models that were developed toestimate the effect of screening mammography and adjuvant therapyon the rates of death from breast cancer from 1975 to 2000 inthe United States. The models used common sources of data, buttheir approaches and assumptions differed. Despite these differences,all seven groups concluded that screening and treatment havecontributed to the observed decline in the rate of death frombreast cancer and that the decline can be explained by a combinationof screening and therapy and not by either one alone.
Although the conclusions of these models are qualitatively similar,their estimates vary. This variability is not surprising, giventhe diversity of the modeling approaches and assumptions. Althoughwe strove for some consistency in modeling by using common sourcesof data concerning the use of mammography and adjuvant therapyover time, rates of death from causes other than breast cancer,and trends in background incidence that were not due to screening,some groups involved in our consortium used equivalent "input"variables suiting their models' structure. In addition, thegroups used different data sets (surveillance information, resultsof meta-analyses of treatment trials, and data from screeningtrials) to estimate variables specific to their models. Somemodels focused on invasive breast cancer, and others accountedfor ductal carcinoma in situ in the natural history of and screeningfor the disease. The differences in conclusions reflect uncertaintiesin the interpretation of available information, rather thancontradictions among the models.
The variability in the quantitative conclusions across the models(Figure 3 and Table 3) demonstrates an interplay between screeningand treatment. Screening would have no benefit if not followedby treatment (including surgery), and treatment is likely tobe more effective if cancer is detected at earlier stages byscreening. Because the increasing use of adjuvant therapiesand screening occurred over nearly the same periods, distinguishingbetween the two effects is not easy. A consequence of the concurrentintroduction of the two interventions is that slight variationsin modeling assumptions can result in marked changes in estimatedeffects. It is reassuring that the qualitative conclusions agreein the face of this sensitivity to assumptions.
The slight negative statistical correlation between the models'estimates of the relative contributions of the two interventions(Figure 3) reflects the intuitive notion that either screeningor therapy can provide some of the benefit not offered by theother intervention. For example, if adjuvant therapy explainsmore of the observed reduction in mortality, then less is leftfor screening to explain. One of the models (model M) specificallyaddressed within-model uncertainty. The variability among modelsshown in Figure 3A is similar to the variability within theresults of model M (not shown), suggesting that the variationamong models is similar in magnitude to the variability withina single model's estimate after the uncertainties related tovarious aspects of the analysis are accounted for.
Addressing a complex public health question through the collaborationof seven modeling groups is unusual in health and medical-decisionsciences. The challenges of model comparisons were mitigatedby this collaborative effort.
Supported by grants (U01CA63740, U01CA86076, U01CA86082, U01CA63736,U01CA70013, U01CA69976, U01CA63731, and U01CA70040) from theNational Cancer Institute to fund the Breast Cancer SurveillanceConsortium and the Group Health Cooperative for informationon breast-cancer–screening practices and characteristicsof tumors detected by screening and by cooperative agreements(CA88278, CA88211, CA88202, CA88248, CA88177, CA88270, and CA88283)with the National Cancer Institute.
Dr. Berry reports having received consulting fees from Pfizer,Novartis, Eli Lilly, and Bristol-Myers Squibb.
* The CISNET collaborators are listed in the Appendix.
Source Information
From M.D. Anderson Cancer Center, Houston (D.A.B.); the National Cancer Institute, Bethesda, Md. (K.A.C., E.J.F.); Stanford University, Stanford, Calif. (S.K.P.); the University of Wisconsin–Madison, Madison (D.G.F.); Cornerstone Systems, Lynden, Wash. (L.C.); Dana–Farber Cancer Institute, Boston (M.Z.); Georgetown University, Washington, D.C. (J.S.M.); the University of Rochester, Rochester, N.Y. (A.Y.Y.); and Erasmus University Medical Center, Rotterdam, the Netherlands (J.D.F.H.).
Address reprint requests to Dr. Berry at the Department of Biostatistics and Applied Mathematics, M.D. Anderson Cancer Center, Unit 447, 1515 Holcombe Blvd., Houston, TX 77030, or at dberry{at}mdanderson.org.
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Appendix
The following were CISNET collaborators: M. Zelen (principalinvestigator), S.J. Lee, H. Huang, and R.S. Gelman, Dana–FarberCancer Institute, Boston; J.D.F. Habbema (principal investigator),S.Y.G.L. Tan, G.J. van Oortmarssen, H.J. de Koning, and R. Boer,Erasmus University Medical Center, Rotterdam, the Netherlands;J.S. Mandelblatt (principal investigator), W.F. Lawrence, B.Yi, J. Cullen, and K.R. Yabroff, Georgetown University, Washington,D.C.; C.B. Schechter, Albert Einstein College of Medicine, NewYork; D.A. Berry (principal investigator), L.Y. Inoue, M.F.Munsell, J. Venier, Y. Shen, G. Ball, E. Hoy, R.L. Theriault,and M.L. Bondy, M.D. Anderson Cancer Center, Houston; A.Y. Yakovlev(principal investigator), A.V. Zorin, and L.G. Hanin, Universityof Rochester, Rochester, N.Y.; S.K. Plevritis (principal investigator),B.M. Sigal, P. Salzman, P.W. Glynn, J. Rosenberg, and S. Rai,Stanford University, Stanford, Calif.; D.G. Fryback (principalinvestigator), M.A. Rosenberg, A. Trentham-Dietz, P.L. Remington,N.K. Stout, and V. Kuruchittham, University of Wisconsin–Madison,Madison; E.J. Feuer (program director), K.A. Cronin, and A.B.Mariotto, National Cancer Institute, Bethesda, Md.; and L. Clarke,Cornerstone Systems, Lynden, Wash.
Screening and Breast Cancer
Baker S. G., Gøtzsche P. C., Baglioni P., Smith D. W., Retsky M. W., Demicheli R., Hrushesky W. J.M., Berry D. A., Plevritis S. K., Fryback D. G.
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