A potential error in evaluating cancer screening: A comparison of 2 approaches for modeling underlying disease progression

Sue J. Goldie, Karen M. Kuntz

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Background. Evaluating cancer screening often requires modeling the underlying disease process and not observed disease, particularly in the absence of direct evidence linking screening to a survival benefit. Methods. To illustrate a potential error in modeling disease progression among healthy persons with a history of a precancerous lesion, we constructed 2 models with 4 basic health states (disease free, presence of a precancerous lesion, presence of cancer, dead), calibrated to predict the same 10-year cancer incidence. We assumed a homogeneous cohort enters each model free of disease, the probability of developing a precancerous lesion was greater for patients with a history of a prior lesion, and the screening test was perfect and riskless. In one model, we assigned a higher transition probability from a precancerous lesion to cancer in those with a history of a previously removed lesion; in the other, we assumed it was equal to those with no history. Results. Using the 1st model, life expectancy without screening was 2.4 months longer than with screening. This error did not occur using the 2nd model, in which the transition from precancerous lesions to cancer was not conditional on a history of a lesion. This modeling error's magnitude was examined under a variety of assumptions. Conclusions. We have identified an important error to avoid when modeling the underlying disease process in evaluating screening programs for cancers associated with precancerous states.

Original languageEnglish (US)
Pages (from-to)232-241
Number of pages10
JournalMedical Decision Making
Volume23
Issue number3
DOIs
StatePublished - May 2003

Bibliographical note

Funding Information:
Background. Evaluating cancer screening often requires modeling the underlying disease process and not observed disease, particularly in the absence of direct evidence linking screening to a survival benefit. Methods. To illustrate a potential error in modeling disease progression among healthy persons with a history of a precancerous lesion, we constructed 2 models with 4 basic health states (disease free, presence of a precancerous lesion, presence of cancer, dead), calibrated to predict the same 10-year cancer incidence. We assumed a homogeneous cohort enters each model free of disease, the probability of developing a precancerous lesion was greater for patients with a history of a prior lesion, and the screening test was perfect and riskless. In one model, we assigned a higher transition probability from a precancerous lesion to cancer in those with a history of a previously removed lesion; in the other, we assumed it was equal to those with no history. Results. Using the 1st model, life expectancy without screening was 2.4 months longer than with screening. This error did not occur using the 2nd model, in which the transition from precancerous lesions to cancer was not conditional on a history of a lesion. This modeling error's magnitude was examined under a variety of assumptions. Conclusions. We have identified an important error to avoid when modeling the underlying disease process in evaluating screening programs for cancers associated with precancerous states. model errors cancer screening Markov models hwp-legacy-fpage 232 hwp-legacy-dochead Journal Article 1. Eddy DM. Screening for Cancer: Theory, Analysis, and Design. Englewood Cliffs (NJ): Prentice Hall ; 1980 . 2. Miller AB. An epidemiological perspective on cancer screening . Clin Biochem. 1995 ; 28 : 41 -48 . 3. Wright JC, Weinstein MC. Gains in life expectancy from medical interventions—standardizing data on outcomes . N Engl J Med. 1998 ; 339 : 380 -386 . 4. Goldie SJ, Weinstein MC, Kuntz KM, Freedberg KA. The costs, clinical benefits, and cost-effectiveness of screening for cervical cancer in HIV-infected women . Ann Intern Med. 1999 ; 130 : 97 -107 . 5. Goldie SJ, Kuntz KM, Weinstein MC, Freedberg KA, Welton M, Palefsky JM. The clinical-effectiveness and cost-effectiveness of screening for anal squamous intraepithelial lesions in homosexual and bisexual HIV-positive men . JAMA. 1999 ; 281 : 1822 -1829 . 6. Goldie SJ, Kuntz KM, Weinstein MC, Freedberg KA, Palefsky JM. Cost-effectiveness of screening for HPV-induced anal squamous intraepithelial lesions in homosexual men . Am J Med. 2000 ; 108 : 634 -641 . 7. Frazier AL, Colditz GA, Fuchs CS, Kuntz, KM. Cost-effectiveness of screening for colorectal cancer in the general population . JAMA. 2000 ; 284 : 1954 -1961 . 8. Myers E, McCrory D, Nanda K, Matchar D. Mathematical model for the natural history of human papillomavirus infection and cervical carcinogenesis . Am J Epidemiol. 2000 ; 151 : 1158 -1171 . 9. McCrory D, Mather D, Bastain L, et al. Evaluation of cervical cytology. Evidence report/technology assessment no. 5 (prepared by Duke University under contract no. 290-97-0014). AHCPR publication no. 99-E010. Rockville (MD): Agency for Health Care Policy and Research ; 1999 . 10. Kuntz KM, Goldie SJ. Assessing the sensitivity of decision-analytic results to unobserved markers of risk: defining the effects of heterogeneity bias . Med Decis Making . 2002 ; 22 : 218 -227 . 11. Eddy DM. Screening for cervical cancer . Ann Intern Med. 1990 ; 113 : 214 -226 . 12. Mandel JS, Church TR, Bond JH, et al. The effect of fecal occult-blood screening on the incidence of colorectal cancer . N Engl J Med. 2001 ; 343 : 1603 -1605 . 13. Gold MR, Siegel JE, Russel LB, Weinstein MC, eds. Cost-Effectiveness in Health and Medicine. New York: Oxford University Press ; 1996 . 14. Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide . Med Decis Making . 1993 ; 13 : 322 -338 . 15. Halpern MT, Luce BR, Brown RE, Geneste B. Health and economic outcomes modeling practices: A suggested framework . Value Health . 1998 ; 1 : 131 -146 . 16. Mandelblatt JS, Fryback DG, Weinstein MC, Russell LB, Gold MR. Assessing the effectiveness of health interventions for cost-effectiveness analysis. Panel on Cost-Effectiveness in Health and Medicine . J Gen Intern Med. 1997 ; 12 (9): 551 -558 .

Keywords

  • Cancer screening
  • Markov models
  • Model errors

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