{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Phung MT"],"funding":["NCRR NIH HHS","Ovarian Cancer Association Consortium","National Institutes of Health","Ovarian Cancer Research Fund","Cancer Councils of New South Wales","National Health & Medical Research Council of Australia","National Human Genome Research Institute","Canadian Institutes of Health Research","Army Medical Research and Materiel Command","NHGRI NIH HHS","National Cancer Institute","NCI NIH HHS","AstraZeneca","Cancer Foundation of Western Australia","Wellcome Trust","NIH HHS"],"pagination":["1420-1426"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10637032"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["115(11)"],"pubmed_abstract":["Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pair"],"journal":["Journal of the National Cancer Institute"],"pubmed_title":["A framework for assessing interactions for risk stratification models: the example of ovarian cancer."],"pmcid":["PMC10637032"],"funding_grant_id":["400281","PPD/RPCI.07","P30 CA008748","P50 CA159981","K07 CA080668","R01-CA058598","R01-CA149429","R01 CA054419","R01 CA149429","400413","P50 CA105009","R01 CA058598","K07 CA095666","M01 RR000056","R01 CA095023","199600","R01 CA112523","MOP-86727","CA1X01HG007491-01","K22 CA138563","P30 CA072720","R01 CA126841","076113","R01 CA087538","U19-CA148112","P30 CA062203","DAMD17-01-1-0729","R01 CA058860","U19 CA148112","R01 CA083918"],"pubmed_authors":["Fortner RT","Hanley GE","Anton-Culver H","Webb PM","Lee AW","Terry KL","Bandera EV","Doherty JA","Mukherjee B","Titus LJ","Pharoah PDP","Jensen A","Pike MC","McLean K","Harris HR","Trabert B","Zeinomar N","Ziogas A","Chang-Claude J","Cramer DW","Moysich KB","Goodman MT","Modugno F","Wu AH","Carney ME","Phung MT","Cho KR","Meza R","Qin B","Pearce CL","Berchuck A"],"additional_accession":[]},"is_claimable":false,"name":"A framework for assessing interactions for risk stratification models: the example of ovarian cancer.","description":"Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pair","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Nov","modification":"2026-04-07T16:18:31.171Z","creation":"2025-04-07T12:01:20.901Z"},"accession":"S-EPMC10637032","cross_references":{"pubmed":["37436712"],"doi":["10.1093/jnci/djad137"]}}