Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model

Tess V Clendenen, Wenzhen Ge, Karen L Koenig, Yelena Afanasyeva, Claudia Agnoli, Louise A Brinton, Farbod Darvishian, Joanne F Dorgan, A Heather Eliassen, Roni T Falk, Göran Hallmans, Susan E Hankinson, Judith Hoffman-Bolton, Timothy J Key, Vittorio Krogh, Hazel B Nichols, Dale P Sandler, Minouk J Schoemaker, Patrick M Sluss, Malin Sund, Anthony J Swerdlow, Kala Visvanathan, Anne Zeleniuch-Jacquotte, Mengling Liu, Tess V Clendenen, Wenzhen Ge, Karen L Koenig, Yelena Afanasyeva, Claudia Agnoli, Louise A Brinton, Farbod Darvishian, Joanne F Dorgan, A Heather Eliassen, Roni T Falk, Göran Hallmans, Susan E Hankinson, Judith Hoffman-Bolton, Timothy J Key, Vittorio Krogh, Hazel B Nichols, Dale P Sandler, Minouk J Schoemaker, Patrick M Sluss, Malin Sund, Anthony J Swerdlow, Kala Visvanathan, Anne Zeleniuch-Jacquotte, Mengling Liu

Abstract

Background: Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50.

Methods: In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.

Results: The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.

Conclusions: AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.

Keywords: Anti-Müllerian hormone; Breast cancer risk prediction; Gail model; Testosterone.

Conflict of interest statement

Ethics approval and consent to participate

Each cohort was approved by its institutional ethical review board.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Area under the receiver operating curve (AUC) estimates and 95% confidence intervals
Fig. 2
Fig. 2
Reclassification of absolute 5-year risk of breast cancer with the addition of AMH and testosterone to the Gail model

References

    1. U.S. Preventive Services Task Force Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151(10):716–726.
    1. Smith R, Cokkinides V, Brooks D, Saslow D, Brawley O. Cancer screening in the United States, 2010: a review of current American Cancer Society guidelines and issues in cancer screening. Ca. 2010;60(2):99–119.
    1. Hauk L. Practice guidelines: American College of Obstetricians and Gynecologists updates Breast Cancer screening guidelines. Am Fam Physician. 2012;85:654–655.
    1. American College of Obstetricians and gynecologists Practice bulletin no. 122: breast cancer screening. Obstet Gynecol. 2011;118:372–382.
    1. Klabunde CN, Ballard-Barbash R. Evaluating population-based screening mammography programs internationally. Semin Breast Dis. 2007;10(2):102–107.
    1. Schopper D, de Wolf C. How effective are breast cancer screening programmes by mammography? Review of the current evidence. Eur J Cancer. 2009;45(11):1916–1923.
    1. Oeffinger KC, Fontham EH, Etzioni R, et al. Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA. 2015;314(15):1599–1614.
    1. Gail MH, Costantino JP, Bryant J, Croyle R, Freedman L, Helzlsouer K, Vogel V. Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst. 1999;91(21):1829–1846.
    1. Fisher B, Costantino JP, Wickerham DL, Redmond CK, Kavanah M, Cronin WM, Vogel V, Robidoux A, Dimitrov N, Atkins J, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst. 1998;90(18):1371–1388.
    1. Vogel VG, Costantino JP, Wickerham DL, Cronin WM, Cecchini RS, Atkins JN, Bevers TB, Fehrenbacher L, Pajon ER, Jr, Wade JL, 3rd, et al. Effects of tamoxifen vs raloxifene on the risk of developing invasive breast cancer and other disease outcomes: the NSABP study of tamoxifen and raloxifene (STAR) P-2 trial. JAMA. 2006;295(23):2727–2741.
    1. Visvanathan K, Chlebowski RT, Hurley P, Col NF, Ropka M, Collyar D, Morrow M, Runowicz C, Pritchard KI, Hagerty K, et al. American society of clinical oncology clinical practice guideline update on the use of pharmacologic interventions including tamoxifen, raloxifene, and aromatase inhibition for breast cancer risk reduction. J Clin Oncol. 2009;27(19):3235–3258.
    1. Freedman AN, Graubard BI, Rao SR, McCaskill-Stevens W, Ballard-Barbash R, Gail MH. Estimates of the number of US women who could benefit from tamoxifen for breast cancer chemoprevention. J Natl Cancer Inst. 2003;95(7):526–532.
    1. Visvanathan K, Hurley P, Bantug E, Brown P, Col NF, Cuzick J, Davidson NE, DeCensi A, Fabian C, Ford L, et al. Use of pharmacologic interventions for breast cancer risk reduction: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol. 2013;31(23):2942–2962.
    1. Costantino JP, Gail MH, Pee D, Anderson S, Redmond CK, Benichou J, Wieand HS. Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst. 1999;91:1541–1548.
    1. Rockhill B, Spiegelman D, Byrne C, Hunter DJ, Colditz GA. Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst. 2001;93(5):358–366.
    1. Chlebowski RT, Anderson GL, Lane DS, Aragaki AK, Rohan T, Yasmeen S, Sarto G, Rosenberg CA, Hubbell FA, Investigators FtWsHI Predicting risk of breast cancer in postmenopausal women by hormone receptor status. J Natl Cancer Inst. 2007;99(22):1695–1705.
    1. Bondy ML, Vogel VG. Re: validation of the Gail et al. model for predicting individual breast cancer risk. J Natl Cancer Inst. 1994;86(17):1350.
    1. Pankratz VS, Hartmann LC, Degnim AC, Vierkant RA, Ghosh K, Vachon CM, Frost MH, Maloney SD, Reynolds C, Boughey JC. Assessment of the accuracy of the Gail model in women with atypical hyperplasia. J Clin Oncol. 2008;26(33):5374–5379.
    1. Pfeiffer RM, Park Y, Kreimer AR, Lacey JV, Pee D, Greenlee RT, Buys SS, Hollenbeck A, Rosner B, Gail MH, et al. Risk prediction for breast, endometrial, and ovarian cancer in White women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med. 2013;10(7):e1001492.
    1. Decarli A, Calza S, Masala G, Specchia C, Palli D, Gail MH. Gail model for prediction of absolute risk of invasive breast cancer: independent evaluation in the Florence–European prospective investigation into Cancer and Nutrition Cohort. J Natl Cancer Inst. 2006;98(23):1686–1693.
    1. Matsuno RK, Costantino JP, Ziegler RG, Anderson GL, Li H, Pee D, Gail MH. Projecting individualized absolute invasive breast cancer risk in Asian and Pacific Islander American women. J Natl Cancer Inst. 2011;103(12):951–961.
    1. Gail MH, Costantino JP, Pee D, Bondy M, Newman L, Selvan M, Anderson GL, Malone KE, Marchbanks PA, McCaskill-Stevens W, et al. Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst. 2007;99(23):1782–1792.
    1. Schonfeld SJ, Pee D, Greenlee RT, Hartge P, Lacey JV, Jr, Park Y, Schatzkin A, Visvanathan K, Pfeiffer RM. Effect of changing breast cancer incidence rates on the calibration of the Gail model. J Clin Oncol. 2010;28(14):2411–2417.
    1. Ge W, Clendenen TV, Afanasyeva Y, Koenig KL, Agnoli C, Brinton LA, Dorgan JF, Eliassen AH, Falk RT, Hallmans G, et al. Circulating anti-Mullerian hormone and breast cancer risk: a study in ten prospective cohorts. Int J Cancer. 2018;142(11):2215–2226.
    1. Zeleniuch-Jacquotte A, Afanasyeva Y, Kaaks R, Rinaldi S, Scarmo S, Liu M, Arslan AA, Toniolo P, Shore RE, Koenig KL. Premenopausal serum androgens and breast cancer risk: a nested case-control study. Breast Cancer Res. 2012;14(1):R32.
    1. Micheli A, Muti P, Secreto G, Krogh V, Meneghini E, Venturelli E, Sieri S, Pala V, Berrino F. Endogenous sex hormones and subsequent breast cancer in premenopausal women. Int J Cancer. 2004;112(2):312–318.
    1. Kaaks R, Berrino F, Key T, Rinaldi S, Dossus L, Biessy C, Secreto G, Amiano P, Bingham S, Boeing H, et al. Serum sex steroids in premenopausal women and breast cancer risk within the European Prospective Investigation into Cancer and Nutrition (EPIC) J Natl Cancer Inst. 2005;97(10):755–765.
    1. Eliassen AH, Missmer SA, Tworoger SS, Spiegelman D, Barbieri RL, Dowsett M, Hankinson SE. Endogenous steroid hormone concentrations and risk of breast cancer among premenopausal women. J Natl Cancer Inst. 2006;98(19):1406–1415.
    1. Dorgan JF, Stanczyk FZ, Kahle LL, Brinton LA. Prospective case-control study of premenopausal serum estradiol and testosterone levels and breast cancer risk. Breast Cancer Res. 2010;12(6):R98.
    1. Kaaks R, Tikk K, Sookthai D, Schock H, Johnson T, Tjonneland A, Olsen A, Overvad K, Clavel-Chapelon F, Dossus L, et al. Premenopausal serum sex hormone levels in relation to breast cancer risk, overall and by hormone receptor status - results from the EPIC cohort. Int J Cancer. 2014;134(8):1947–1957.
    1. Key T, Appleby P, Barnes I, Reeves G, Endogenous H, Breast Cancer Collaborative G Endogenous sex hormones and breast cancer in postmenopausal women: reanalysis of nine prospective studies. J Natl Cancer Inst. 2002;94(8):606–616.
    1. Zeleniuch-Jacquotte A, Shore RE, Koenig KL, Akhmedkhanov A, Afanasyeva Y, Kato I, Kim MY, Rinaldi S, Kaaks R, Toniolo P. Postmenopausal levels of oestrogen, androgen, and SHBG and breast cancer: long-term results of a prospective study. Br J Cancer. 2004;90(1):153–159.
    1. Manjer J, Johansson R, Berglund G, Janzon L, Kaaks R, Agren A, Lenner P. Postmenopausal breast cancer risk in relation to sex steroid hormones, prolactin and SHBG (Sweden) Cancer Causes Control. 2003;14(7):599–607.
    1. Kaaks R, Rinaldi S, Key TJ, Berrino F, Peeters PH, Biessy C, Dossus L, Lukanova A, Bingham S, Khaw KT, et al. Postmenopausal serum androgens, oestrogens and breast cancer risk: the European prospective investigation into cancer and nutrition. Endocr Relat Cancer. 2005;12(4):1071–1082.
    1. Baglietto L, Severi G, English DR, Krishnan K, Hopper JL, McLean C, Morris HA, Tilley WD, Giles GG. Circulating steroid hormone levels and risk of breast cancer for postmenopausal women. Cancer Epidemiol Biomarkers Prev. 2010;19(2):492–502.
    1. Farhat GN, Cummings SR, Chlebowski RT, Parimi N, Cauley JA, Rohan TE, Huang AJ, Vitolins M, Hubbell FA, Manson JE, et al. Sex hormone levels and risks of estrogen receptor-negative and estrogen receptor-positive breast cancers. J Natl Cancer Inst. 2011;103(7):562–570.
    1. Zhang X, Tworoger SS, Eliassen AH, Hankinson SE. Postmenopausal plasma sex hormone levels and breast cancer risk over 20 years of follow-up. Breast Cancer Res Treat. 2013;137(3):883–892.
    1. Sieri S, Krogh V, Bolelli G, Abagnato CA, Grioni S, Pala V, Evangelista A, Allemani C, Micheli A, Tagliabue G, et al. Sex hormone levels, breast cancer risk, and cancer receptor status in postmenopausal women: the ORDET cohort. Cancer Epidemiol Biomarkers Prev. 2009;18(1):169–176.
    1. Eliassen AH, Zeleniuch-Jacquotte A, Rosner B, Hankinson SE. Plasma anti-Mullerian hormone concentrations and risk of breast cancer among premenopausal women in the Nurses' Health Studies. Cancer Epidemiol Biomarkers Prev. 2016;25(5):854–860.
    1. van Rooij IA, Broekmans FJ, Scheffer GJ, Looman CW, Habbema JD, de Jong FH, Fauser BJ, Themmen AP, te Velde ER. Serum antimullerian hormone levels best reflect the reproductive decline with age in normal women with proven fertility: a longitudinal study. Fertil Steril. 2005;83(4):979–987.
    1. Dorgan JF, Spittle CS, Egleston BL, Shaw CM, Kahle LL, Brinton LA. Assay reproducibility and within-person variation of Mullerian inhibiting substance. Fertil Steril. 2010;94(1):301–304.
    1. Missmer SA, Spiegelman D, Bertone-Johnson ER, Barbieri RL, Pollak MN, Hankinson SE. Reproducibility of plasma steroid hormones, prolactin, and insulin-like growth factor levels among premenopausal women over a 2- to 3-year period. Cancer Epidemiol Biomarkers Prev. 2006;15(5):972–978.
    1. de Kat AC, van der Schouw YT, Eijkemans MJ, Herber-Gast GC, Visser JA, Verschuren WM, Broekmans FJ. Back to the basics of ovarian aging: a population-based study on longitudinal anti-Mullerian hormone decline. BMC Med. 2016;14(1):151.
    1. La Marca A, Spada E, Grisendi V, Argento C, Papaleo E, Milani S, Volpe A. Normal serum anti-Mullerian hormone levels in the general female population and the relationship with reproductive history. Eur J Obstet Gynecol Reprod Biol. 2012;163(2):180–184.
    1. Schoemaker MJ, Folkerd EJ, Jones ME, Rae M, Allen S, Ashworth A, Dowsett M, Swerdlow AJ. Combined effects of endogenous sex hormone levels and mammographic density on postmenopausal breast cancer risk: results from the Breakthrough Generations Study. Br J Cancer. 2014;110(7):1898–1907.
    1. Thomas HV, Key TJ, Allen DS, Moore JW, Dowsett M, Fentiman IS, Wang DY. A prospective study of endogenous serum hormone concentrations and breast cancer risk in premenopausal women on the island of Guernsey. Br J Cancer. 1997;75(7):1075–1079.
    1. Nichols HB, Baird DD, Stanczyk FZ, Steiner AZ, Troester MA, Whitworth KW, Sandler DP. Anti-Mullerian hormone concentrations in premenopausal women and breast cancer risk. Cancer Prev Research (Phila). 2015;8(6):528-34.
    1. Fortner RT, Eliassen AH, Spiegelman D, Willett WC, Barbieri RL, Hankinson SE. Premenopausal endogenous steroid hormones and breast cancer risk: results from the Nurses' Health Study II. Breast Cancer Res. 2013;15(2):R19. doi:10.1186/bcr3394.
    1. Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol. 1985;122(5):904–914.
    1. USA: National Center for Health Statistics, Centers for Disease Control and Prevention. . Accessed 17 July 2018.
    1. Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, Benichou J, Gail MH. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98(17):1215–1226.
    1. Anothaisintawee T, Teerawattananon Y, Wiratkapun C, Kasamesup V, Thakkinstian A. Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat. 2012;133(1):1–10.
    1. Tworoger SS, Zhang X, Eliassen AH, Qian J, Colditz GA, Willett WC, Rosner BA, Kraft P, Hankinson SE. Inclusion of endogenous hormone levels in risk prediction models of postmenopausal breast cancer. J Clin Oncol. 2014;32(28):3111–3117.
    1. Wacholder S, Hartge P, Prentice R, Garcia-Closas M, Feigelson HS, Diver WR, Thun MJ, Cox DG, Hankinson SE, Kraft P, et al. Performance of common genetic variants in breast-cancer risk models. N Engl J Med. 2010;362(11):986–993.
    1. Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, Tice JA, Buist DS, Geller BM, Rosenberg R, et al. Prospective breast cancer risk prediction model for women undergoing screening mammography. J Natl Cancer Inst. 2006;98(17):1204–1214.
    1. Hüsing A, Fortner RT, Kühn T, Overvad K, Tjønneland A, Olsen A, Boutron-Ruault M-C, Severi G, Fournier A, Boeing H, et al. Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: results from the EPIC cohort. Clin Cancer Res. 2017;
    1. Mackarem G, Roche CA, Hughes KS. The effectiveness of the Gail model in estimating risk for development of breast cancer in women under 40 years of age. Breast J. 2001;7(1):34–39.
    1. Dartois L, Gauthier É, Heitzmann J, Baglietto L, Michiels S, Mesrine S, Boutron-Ruault M-C, Delaloge S, Ragusa S, Clavel-Chapelon F, et al. A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort. Breast Cancer Res Treat. 2015;150(2):415–426.
    1. Dite GS, MacInnis RJ, Bickerstaffe A, Dowty JG, Allman R, Apicella C, Milne RL, Tsimiklis H, Phillips KA, Giles GG, et al. Breast cancer risk prediction using clinical models and 77 independent risk-associated SNPs for women aged under 50 years: Australian Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev. 2016;25(2):359–365.
    1. Ueda K, Tsukuma H, Tanaka H, Ajiki W, Oshima A. Estimation of individualized probabilities of developing breast cancer for Japanese women. Breast Cancer. 2003;10(1):54–62.
    1. Lukanova A, Lundin E, Zeleniuch-Jacquotte A, Muti P, Mure A, Rinaldi S, Dossus L, Micheli A, Arslan A, Lenner P, et al. Body mass index, circulating levels of sex-steroid hormones, IGF-I and IGF-binding protein-3: a cross-sectional study in healthy women. Eur J Endocrinol. 2004;150(2):161–171.
    1. Shaw CM, Stanczyk FZ, Egleston BL, Kahle LL, Spittle CS, Godwin AK, Brinton LA, Dorgan JF. Serum antimullerian hormone in healthy premenopausal women. Fertil Steril. 2011;95(8):2718–2721.
    1. Nichols HB, Baird DD, Stanczyk FZ, Steiner AZ, Troester MA, Whitworth KW, Sandler DP. Anti-Mullerian hormone concentrations in premenopausal women and breast cancer risk. Cancer Prev Res (Phila) 2015;8(6):528–534.
    1. Dorgan JF, Stanczyk FZ, Egleston BL, Kahle LL, Shaw CM, Spittle CS, Godwin AK, Brinton LA. Prospective case-control study of serum mullerian inhibiting substance and breast cancer risk. J Natl Cancer Inst. 2009;101(21):1501–1509.
    1. Tice JA, Cummings SR, Ziv E, Kerlikowske K. Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005;94(2):115–122.
    1. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148(5):337–347.
    1. Endogenous H, Breast Cancer Collaborative G, Key TJ, Appleby PN, Reeves GK, Travis RC, Alberg AJ, Barricarte A, Berrino F, Krogh V, et al. Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of individual participant data from seven prospective studies. Lancet Oncol. 2013;14(10):1009–1019.
    1. Mealiffe ME, Stokowski RP, Rhees BK, Prentice RL, Pettinger M, Hinds DA. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. 2010;102(21):1618–1627.
    1. Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst. 2008;100(14):1037–1041.
    1. Dite GS, Mahmoodi M, Bickerstaffe A, Hammet F, Macinnis RJ, Tsimiklis H, Dowty JG, Apicella C, Phillips KA, Giles GG, et al. Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model. Breast Cancer Res Treat. 2013;139(3):887–896.
    1. Allman R, Dite GS, Hopper JL, Gordon O, Starlard-Davenport A, Chlebowski R, Kooperberg C. SNPs and breast cancer risk prediction for African American and Hispanic women. Breast Cancer Res Treat. 2015;154(3):583–589.
    1. Darabi H, Czene K, Zhao W, Liu J, Hall P, Humphreys K. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res. 2012;14(1):R25.
    1. Lee CP, Choi H, Soo KC, Tan MH, Chay WY, Chia KS, Liu J, Li J, Hartman M. Mammographic breast density and common genetic variants in breast cancer risk prediction. PLoS One. 2015;10(9):e0136650.
    1. McCarthy AM, Armstrong K, Handorf E, Boghossian L, Jones M, Chen J, Demeter MB, McGuire E, Conant EF, Domchek SM. Incremental impact of breast cancer SNP panel on risk classification in a screening population of white and African American women. Breast Cancer Res Treat. 2013;138(3):889–898.
    1. Husing A, Canzian F, Beckmann L, Garcia-Closas M, Diver WR, Thun MJ, Berg CD, Hoover RN, Ziegler RG, Figueroa JD, et al. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status. J Med Genet. 2012;49(9):601–608.
    1. Ganna A, Reilly M, de Faire U, Pedersen N, Magnusson P, Ingelsson E. Risk prediction measures for case-cohort and nested case-control designs: an application to cardiovascular disease. Am J Epidemiol. 2012;175(7):715–724.
    1. Samuelsen S. A pseudolikelihood approach to analysis of nested case-control studies. Biometrika. 1997;84(2):379–394.
    1. Jung S, Allen N, Arslan AA, Baglietto L, Brinton LA, Egleston BL, Falk R, Fortner RT, Helzlsouer KJ, Idahl A, et al. Demographic, lifestyle, and other factors in relation to anti-Müllerian hormone levels in mostly late premenopausal women. Fertil Steril. 2017;107(4):1012–1022.e1012.
    1. Birch Petersen K, Hvidman HW, Forman JL, Pinborg A, Larsen EC, Macklon KT, Sylvest R, Andersen AN. Ovarian reserve assessment in users of oral contraception seeking fertility advice on their reproductive lifespan. Hum Reprod. 2015;30(10):2364–2375.
    1. Gail MH. Twenty-five years of breast cancer risk models and their applications. J Natl Cancer Inst. 2015;107(5):djv042.
    1. Garcia-Closas M, Gunsoy NB, Chatterjee N. Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer. J Natl Cancer Inst. 2014;106(11):dju305.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa