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000179129 0247_ $$2doi$$a10.1158/1055-9965.EPI-21-0747
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000179129 1001_ $$aSchmid, Sabine$$b0
000179129 245__ $$aAccounting for EGFR Mutations in Epidemiologic Analyses of Non-Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data.
000179129 260__ $$aPhiladelphia, Pa.$$bAACR$$c2022
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000179129 520__ $$aSomatic EGFR mutations define a subset of non-small cell lung cancers (NSCLC) that have clinical impact on NSCLC risk and outcome. However, EGFR-mutation-status is often missing in epidemiologic datasets. We developed and tested pragmatic approaches to account for EGFR-mutation-status based on variables commonly included in epidemiologic datasets and evaluated the clinical utility of these approaches.Through analysis of the International Lung Cancer Consortium (ILCCO) epidemiologic datasets, we developed a regression model for EGFR-status; we then applied a clinical-restriction approach using the optimal cut-point, and a second epidemiologic, multiple imputation approach to ILCCO survival analyses that did and did not account for EGFR-status.Of 35,356 ILCCO patients with NSCLC, EGFR-mutation-status was available in 4,231 patients. A model regressing known EGFR-mutation-status on clinical and demographic variables achieved a concordance index of 0.75 (95% CI, 0.74-0.77) in the training and 0.77 (95% CI, 0.74-0.79) in the testing dataset. At an optimal cut-point of probability-score = 0.335, sensitivity = 69% and specificity = 72.5% for determining EGFR-wildtype status. In both restriction-based and imputation-based regression analyses of the individual roles of BMI on overall survival of patients with NSCLC, similar results were observed between overall and EGFR-mutation-negative cohort analyses of patients of all ancestries. However, our approach identified some differences: EGFR-mutated Asian patients did not incur a survival benefit from being obese, as observed in EGFR-wildtype Asian patients.We introduce a pragmatic method to evaluate the potential impact of EGFR-status on epidemiological analyses of NSCLC.The proposed method is generalizable in the common occurrence in which EGFR-status data are missing.
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000179129 7001_ $$00000-0002-7393-2664$$aJiang, Mei$$b1
000179129 7001_ $$aBrown, M Catherine$$b2
000179129 7001_ $$aFares, Aline$$b3
000179129 7001_ $$aGarcia, Miguel$$b4
000179129 7001_ $$aSoriano, Joelle$$b5
000179129 7001_ $$00000-0003-1735-5361$$aDong, Mei$$b6
000179129 7001_ $$aThomas, Sera$$b7
000179129 7001_ $$00000-0002-5371-706X$$aKohno, Takashi$$b8
000179129 7001_ $$aLeal, Leticia Ferro$$b9
000179129 7001_ $$00000-0003-1741-0244$$aDiao, Nancy$$b10
000179129 7001_ $$aXie, Juntao$$b11
000179129 7001_ $$00000-0002-0136-1911$$aWang, Zhichao$$b12
000179129 7001_ $$aZaridze, David$$b13
000179129 7001_ $$aHolcatova, Ivana$$b14
000179129 7001_ $$aLissowska, Jolanta$$b15
000179129 7001_ $$00000-0003-3757-3868$$aŚwiątkowska, Beata$$b16
000179129 7001_ $$00000-0002-6219-9807$$aMates, Dana$$b17
000179129 7001_ $$aSavic, Milan$$b18
000179129 7001_ $$00000-0001-8669-8240$$aWenzlaff, Angela S$$b19
000179129 7001_ $$aHarris, Curtis C$$b20
000179129 7001_ $$aCaporaso, Neil E$$b21
000179129 7001_ $$aMa, Hongxia$$b22
000179129 7001_ $$00000-0002-7680-158X$$aFernandez-Tardon, Guillermo$$b23
000179129 7001_ $$00000-0002-1028-1091$$aBarnett, Matthew J$$b24
000179129 7001_ $$aGoodman, Gary$$b25
000179129 7001_ $$00000-0002-7609-4977$$aDavies, Michael P A$$b26
000179129 7001_ $$aPérez-Ríos, Mónica$$b27
000179129 7001_ $$aTaylor, Fiona$$b28
000179129 7001_ $$00000-0001-5256-0163$$aDuell, Eric J$$b29
000179129 7001_ $$0P:(DE-HGF)0$$aSchoettker, Ben$$b30
000179129 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b31$$udkfz
000179129 7001_ $$aAndrew, Angeline$$b32
000179129 7001_ $$00000-0002-5138-1099$$aCox, Angela$$b33
000179129 7001_ $$00000-0001-9927-7453$$aRuano-Ravina, Alberto$$b34
000179129 7001_ $$00000-0003-3951-6365$$aField, John K$$b35
000179129 7001_ $$aMarchand, Loic Le$$b36
000179129 7001_ $$aWang, Ying$$b37
000179129 7001_ $$aChen, Chu$$b38
000179129 7001_ $$00000-0001-5150-1209$$aTardon, Adonina$$b39
000179129 7001_ $$aShete, Sanjay$$b40
000179129 7001_ $$00000-0003-3241-3216$$aSchabath, Matthew B$$b41
000179129 7001_ $$aShen, Hongbing$$b42
000179129 7001_ $$aLandi, Maria Teresa$$b43
000179129 7001_ $$aRyan, Brid M$$b44
000179129 7001_ $$00000-0002-9525-1157$$aSchwartz, Ann G$$b45
000179129 7001_ $$aQi, Lihong$$b46
000179129 7001_ $$00000-0002-0900-5735$$aSakoda, Lori C$$b47
000179129 7001_ $$00000-0002-0518-8714$$aBrennan, Paul$$b48
000179129 7001_ $$00000-0002-8588-847X$$aYang, Ping$$b49
000179129 7001_ $$aZhang, Jie$$b50
000179129 7001_ $$00000-0002-0301-0242$$aChristiani, David C$$b51
000179129 7001_ $$00000-0002-9639-7940$$aReis, Rui Manuel$$b52
000179129 7001_ $$aShiraishi, Kouya$$b53
000179129 7001_ $$00000-0002-4486-7496$$aHung, Rayjean J$$b54
000179129 7001_ $$aXu, Wei$$b55
000179129 7001_ $$00000-0002-2603-7296$$aLiu, Geoffrey$$b56
000179129 773__ $$0PERI:(DE-600)2036781-8$$a10.1158/1055-9965.EPI-21-0747$$gVol. 31, no. 3, p. cebp.0747.2021 -$$n3$$pcebp.0747.2021 -$$tCancer epidemiology, biomarkers & prevention$$v31$$x1055-9965$$y2022
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