Validation of the Harvard Cancer Risk Index: a prediction tool for individual cancer risk.

OBJECTIVE Risk appraisal tools are increasingly being used in the clinical setting to estimate individuals' risks of developing and dying from diseases. The Harvard Cancer Risk Index is one such tool constructed to predict the risks of individuals, aged 40 and above, for developing the leading types of cancer in U.S. men and women relative to the general population. To date, the Risk Index has not been prospectively validated. STUDY DESIGN AND SETTING Over a period of 10 years' follow-up in the Nurses' Health Study and the Health Professionals' Follow-up Study, age-standardized incidence ratios for cancer of the ovary, colon, and pancreas were calculated for the Risk Index's relative risk categories to assess goodness of fit for risk prediction at the aggregate level. Age-adjusted concordance statistics were determined as measures of discriminatory accuracy at the individual level. RESULTS The Risk Index was well calibrated with observed relative risks across categories for ovarian and colon cancer in women and pancreatic cancer in men, while it performed moderately for colon cancer in men. Discriminatory accuracy was modest for ovarian cancer (age-adjusted concordance statistic = 0.59), and relatively good for pancreatic cancer (concordance statistic of 0.72), and colon cancer in men and women (concordance statistics of 0.71, 0.67 respectively). CONCLUSION The results of this prospective validation provide evidence for the validity of the Risk Index in predicting individuals' risks of cancers, and thereby offer support for future applications of this risk appraisal tool.

[1]  R. Berkowitz,et al.  Osteopontin as a potential diagnostic biomarker for ovarian cancer. , 2002, JAMA.

[2]  G. Colditz,et al.  The nurses' health study: a cohort of US women followed since 1976. , 1995, Journal of the American Medical Women's Association.

[3]  W. Browner,et al.  Improving the prediction of coronary heart disease to aid in the management of high cholesterol levels: what a difference a decade makes. , 1998, JAMA.

[4]  W. Kannel,et al.  A general cardiovascular risk profile: the Framingham Study. , 1976, The American journal of cardiology.

[5]  I. White,et al.  Systematic review of the prospective cohort studies on meat consumption and colorectal cancer risk: a meta-analytical approach. , 2001, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[6]  H. Joensuu,et al.  Artificial Neural Networks Applied to Survival Prediction in Breast Cancer , 1999, Oncology.

[7]  Robert Fletcher,et al.  Tailored Computer-Based Cancer Risk Communication: Correcting Colorectal Cancer Risk Perception , 2004, Journal of health communication.

[8]  W D Plummer,et al.  Understanding the relationship between relative and absolute risk , 1996, Cancer.

[9]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[10]  E. Rimm,et al.  A prospective study of dietary fat and risk of prostate cancer. , 1993, Journal of the National Cancer Institute.

[11]  J. Bunker,et al.  Putting the risk of breast cancer in perspective , 1998, BMJ.

[12]  R B D'Agostino,et al.  Primary and subsequent coronary risk appraisal: new results from the Framingham study. , 2000, American heart journal.

[13]  J Benichou,et al.  Validation studies for models projecting the risk of invasive and total breast cancer incidence. , 1999, Journal of the National Cancer Institute.

[14]  W. Willett,et al.  Harvard Report on Cancer Prevention Volume 4: Harvard Cancer Risk Index , 2004, Cancer Causes & Control.

[15]  U. P. S. T. Force.  Guide to Clinical Preventive Services: Report of the U S Preventive Services Task Force , 1996 .

[16]  G. Colditz,et al.  Colon Cancer: Risk Perceptions and Risk Communication , 2004, Journal of health communication.

[17]  I. Thompson,et al.  Development of a decision-making tool to predict risk of prostate cancer: the Cancer of the Prostate Risk Index (CAPRI) test. , 1997, Urology.

[18]  Stefano Bonassi,et al.  Biomarkers in molecular epidemiology studies for health risk prediction. , 2002, Mutation research.

[19]  M. Gail,et al.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. , 1989, Journal of the National Cancer Institute.

[20]  Louise C. Walter,et al.  Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization. , 2001 .

[21]  H. Sox,et al.  Clinical prediction rules. Applications and methodological standards. , 1985, The New England journal of medicine.

[22]  A. Laupacis,et al.  Clinical prediction rules. A review and suggested modifications of methodological standards. , 1997, JAMA.

[23]  R. D'Agostino,et al.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. , 2001, JAMA.

[24]  G Rose,et al.  Sick individuals and sick populations. , 1985, International journal of epidemiology.

[25]  G. Glendon,et al.  Putting the risk of breast cancer in perspective. , 1999, The New England journal of medicine.

[26]  B. Levin,et al.  American Cancer Society Guidelines for the Early Detection of Cancer , 2002, CA: a cancer journal for clinicians.

[27]  D Spiegelman,et al.  Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. , 2001, Journal of the National Cancer Institute.

[28]  Taylor Murray,et al.  Cancer statistics, 2000 , 2000, CA: a cancer journal for clinicians.