Disease fatality and bias in survival cohorts
Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study. We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty ye...
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Published in | Environmental research Vol. 140; pp. 275 - 281 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
01.07.2015
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Abstract | Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study.
We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this ‘inception’ cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding ‘survivor’ (or ‘cross-sectional’) cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure–disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures.
There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure–response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure.
Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease.
•Exposure–disease associations in survivor cohorts can be biased.•Simulated cohorts were used to examine how different assumptions influenced bias.•There was no bias when fatality among diseased subjects was independent of exposure.•Interpretation of results from survivor cohort studies must include several factors. |
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AbstractList | Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study.We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this ‘inception’ cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding ‘survivor’ (or ‘cross-sectional’) cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure–disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures.There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure–response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure.Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease. Objectives Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study. Methods We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this 'inception' cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding 'survivor' (or 'cross-sectional') cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure-disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures. Results There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure-response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure. Conclusions Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease. Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study.OBJECTIVESSimulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study.We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this 'inception' cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding 'survivor' (or 'cross-sectional') cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure-disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures.METHODSWe simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this 'inception' cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding 'survivor' (or 'cross-sectional') cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure-disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures.There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure-response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure.RESULTSThere was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure-response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure.Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease.CONCLUSIONSSurvivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease. Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study. We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this ‘inception’ cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding ‘survivor’ (or ‘cross-sectional’) cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure–disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures. There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure–response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure. Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease. •Exposure–disease associations in survivor cohorts can be biased.•Simulated cohorts were used to examine how different assumptions influenced bias.•There was no bias when fatality among diseased subjects was independent of exposure.•Interpretation of results from survivor cohort studies must include several factors. Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an actual survivor cohort under study. We simulated a cohort of 50,000 subjects exposed to a disease-causing exposure over time and followed forty years, where disease incidence was the outcome of interest. We simulated this 'inception' cohort under different assumptions about the effect of exposure on disease occurrence and fatality after disease occurrence. We then created a corresponding 'survivor' (or 'cross-sectional') cohort, where cohort enrollment took place at a specific date after exposure began in the inception cohort; subjects dying prior to that enrollment date were excluded. The disease of interest caused all deaths in our simulations, but was not always fatal. In the survivor cohort, person-time at risk began before enrollment for all subjects who did not die prior to enrollment. We compared exposure-disease associations in each inception cohort to those in corresponding survivor cohorts to determine how different assumptions impacted bias in the survivor cohorts. All subjects in both inception and survivor cohorts were considered equally susceptible to the effect of exposure in causing disease. We used Cox proportional hazards regression to calculate effect measures. There was no bias in survivor cohort estimates when case fatality among diseased subjects was independent of exposure. This was true even when the disease was highly fatal and more highly exposed subjects were more likely to develop disease and die. Assuming a positive exposure-response in the inception cohort, survivor cohort rate ratios were biased downwards when case fatality was greater with higher exposure. Survivor cohort effect estimates for fatal outcomes are not always biased, although precision can decrease. |
Author | Winquist, Andrea Darrow, Lyndsey A. Barry, Vaughn Steenland, Kyle Klein, Mitchel |
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Cites_doi | 10.1016/S0029-7844(99)00531-1 10.1038/bjc.1972.19 10.1097/01.GIM.0000105751.71430.79 10.1177/096228029400300303 10.1016/j.ygyno.2004.10.037 10.1289/ehp.1206450 10.1136/oem.2007.033977 10.1111/ppe.12073 10.1093/aje/kwg231 10.1136/jech.2003.008466 10.1111/j.1365-2753.2005.00624.x 10.1186/1752-0509-8-S1-S3 10.1097/JTO.0b013e318221f701 10.1093/aje/kws171 10.1093/aje/kwk027 10.1097/EDE.0b013e31821d0879 10.1289/ehp.0800379 10.1371/journal.pmed.1000307 10.1097/EDE.0000000000000259 10.1093/jnci/djq201 10.1080/02841860701784544 10.1289/ehp.1306615 10.1136/oem.2004.014159 10.1097/01.ede.0000158224.50593.e3 10.1097/00001648-200207000-00016 10.1097/EDE.0b013e3181c1ea43 10.1038/nature07943 |
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References | Stratford, Bentrem, Anderson (bib31) 2010; 7 Stratton, Campbell, Futreal (bib30) 2009; 458 Khoury, Yang, Gwinn (bib17) 2004; 6 Limsui, Vierkant, Tillmans (bib19) 2010; 102 Aalen (bib2) 1994; 3 Steenland, Karnes, Darrow, Barry (bib29) 2015; 26 Hernán (bib13) 2010; 21 Steenland, Woskie (bib28) 2012; 176 Gong, Wu, Clarke (bib10) 2014; 8 Barry, Winquist, Steenland (bib4) 2013; 121 C8 Health Project. 2012. C8 Health Project website. Available Ray (bib26) 2003; 158 Marchbanks, Wilson, Bastos (bib20) 2010; 95 Schisterman, Cole, Ye, Platt (bib27) 2013; 27 Winquist, Lally, Shin (bib33) 2013; 121 Austin, Mamdani, van Walraven, Tu (bib3) 2006; 12 (accessed 5.02.13). Richardson, Loomis (bib24) 2004; 61 Russell, Wainer, Wright (bib25) 2011; 6 Kurian, Balise, McGuire (bib18) 2005; 96 Frisbee, Brooks, Maher (bib9) 2009; 117 B. MacMahon T.F. Pugh Causes and entities of disease In: Sander Greenland(Ed.), Evolution of Epidemiologic Ideas. Epidemiology Resources 1987 11 18. Christiani, Mehta, Yu (bib6) 2008; 65 Modugno, Ness, Cottreau (bib21) 2002; 13 Ji, Försti, Sundquist (bib16) 2008; 47 Hudson, Pope, Glynn (bib11) 2005; 16 Verhaak, Tamayo, Yang (bib32) 2013; 123 Applebaum, Malloy, Eisen truncation (bib1) 2011; 22 Champion, Wallace, Prescott (bib7) 1972; 26 Howards, Hertz-Picciotto, Poole (bib15) 2006; 165 Rothman, Greenland, Lash (bib23) 2008 Delgado-Rodríguez, Llorca, Bias. (bib8) 2004; 58 N. Howlader A.M. Noone M. Krapcho et al. SEER Cancer Statistics Review, 1975-2010 National Cancer Institute 2013.(accessed 15.03.15) Han, Bearss, Browne (bib14) 2002; 62 Steenland (10.1016/j.envres.2015.03.039_bib28) 2012; 176 Han (10.1016/j.envres.2015.03.039_bib14) 2002; 62 Aalen (10.1016/j.envres.2015.03.039_bib2) 1994; 3 Richardson (10.1016/j.envres.2015.03.039_bib24) 2004; 61 Khoury (10.1016/j.envres.2015.03.039_bib17) 2004; 6 Ray (10.1016/j.envres.2015.03.039_bib26) 2003; 158 Champion (10.1016/j.envres.2015.03.039_bib7) 1972; 26 10.1016/j.envres.2015.03.039_bib12 Howards (10.1016/j.envres.2015.03.039_bib15) 2006; 165 Hudson (10.1016/j.envres.2015.03.039_bib11) 2005; 16 Winquist (10.1016/j.envres.2015.03.039_bib33) 2013; 121 Kurian (10.1016/j.envres.2015.03.039_bib18) 2005; 96 Steenland (10.1016/j.envres.2015.03.039_bib29) 2015; 26 Limsui (10.1016/j.envres.2015.03.039_bib19) 2010; 102 Delgado-Rodríguez (10.1016/j.envres.2015.03.039_bib8) 2004; 58 Hernán (10.1016/j.envres.2015.03.039_bib13) 2010; 21 Ji (10.1016/j.envres.2015.03.039_bib16) 2008; 47 Rothman (10.1016/j.envres.2015.03.039_bib23) 2008 Verhaak (10.1016/j.envres.2015.03.039_bib32) 2013; 123 Russell (10.1016/j.envres.2015.03.039_bib25) 2011; 6 Modugno (10.1016/j.envres.2015.03.039_bib21) 2002; 13 Marchbanks (10.1016/j.envres.2015.03.039_bib20) 2010; 95 10.1016/j.envres.2015.03.039_bib5 Gong (10.1016/j.envres.2015.03.039_bib10) 2014; 8 Barry (10.1016/j.envres.2015.03.039_bib4) 2013; 121 Christiani (10.1016/j.envres.2015.03.039_bib6) 2008; 65 Applebaum (10.1016/j.envres.2015.03.039_bib1) 2011; 22 Frisbee (10.1016/j.envres.2015.03.039_bib9) 2009; 117 10.1016/j.envres.2015.03.039_bib22 Schisterman (10.1016/j.envres.2015.03.039_bib27) 2013; 27 Austin (10.1016/j.envres.2015.03.039_bib3) 2006; 12 Stratton (10.1016/j.envres.2015.03.039_bib30) 2009; 458 Stratford (10.1016/j.envres.2015.03.039_bib31) 2010; 7 |
References_xml | – volume: 21 start-page: 13 year: 2010 end-page: 15 ident: bib13 article-title: The hazards of hazard ratios publication-title: Epidemiology – volume: 121 start-page: 893 year: 2013 end-page: 899 ident: bib33 article-title: Design, methods, and population for a study of PFOA health effects among highly exposed mid-Ohio valley community residents and workers publication-title: Environ. Health Perspect. – volume: 65 start-page: 430 year: 2008 end-page: 436 ident: bib6 article-title: Genetic susceptibility to occupational exposures publication-title: Occup. Environ. Med. – volume: 26 start-page: 395 year: 2015 end-page: 401 ident: bib29 article-title: Attenuation of exposure–response rate ratios at higher exposures: a simulation study focusing on frailty and measurement error publication-title: Epidemiology – volume: 95 start-page: 255 year: 2010 end-page: 260 ident: bib20 article-title: Cigarette smoking and epithelial ovarian cancer by histologic type publication-title: Obstet. Gynecol. – volume: 158 start-page: 915 year: 2003 end-page: 920 ident: bib26 article-title: Evaluating medication effects outside of clinical trials: new-user designs publication-title: Am. J. Epidemiol. – start-page: 110 year: 2008 end-page: 111 ident: bib23 article-title: Modern Epidemiology – volume: 8 start-page: S3 year: 2014 ident: bib10 article-title: Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression publication-title: BMC Syst. Biol. – reference: N. Howlader A.M. Noone M. Krapcho et al. SEER Cancer Statistics Review, 1975-2010 National Cancer Institute 2013.(accessed 15.03.15) – volume: 3 start-page: 227 year: 1994 end-page: 243 ident: bib2 article-title: Effects of frailty in survival analysis publication-title: Stat. Methods Med. Res. – volume: 16 start-page: 355 year: 2005 end-page: 359 ident: bib11 article-title: The cross-sectional cohort study: an underutilized design publication-title: Epidemiology – volume: 13 start-page: 467 year: 2002 end-page: 471 ident: bib21 article-title: Cigarette smoking and the risk of mucinous and nonmucinous epithelial ovarian cancer publication-title: Epidemiology – volume: 458 start-page: 719 year: 2009 end-page: 724 ident: bib30 article-title: The cancer genome publication-title: Nature – volume: 22 start-page: 599 year: 2011 end-page: 606 ident: bib1 article-title: Left susceptibility, and bias in occupational cohort studies publication-title: Epidemiology – volume: 117 start-page: 1873 year: 2009 end-page: 1882 ident: bib9 article-title: The C8 health project: design, methods, and participants publication-title: Environ. Health Perspect. – reference: (accessed 5.02.13). – volume: 6 start-page: 1496 year: 2011 end-page: 1504 ident: bib25 article-title: Does lung adenocarcinoma subtype predict patient survival?: a clinicopathologic study based on the new international association for the study of Lung Cancer/American Thoracic Society/European respiratory society international multidisciplinary lung adenocarcinoma association publication-title: J. Thorac. Oncol. – volume: 165 start-page: 444 year: 2006 end-page: 452 ident: bib15 article-title: Conditions for bias from differential left truncation publication-title: Am. J. Epidemiol. – volume: 47 start-page: 1133 year: 2008 end-page: 1139 ident: bib16 article-title: Survival in ovarian cancer patients by histology and family history publication-title: Acta Oncol. – volume: 58 start-page: 635 year: 2004 end-page: 641 ident: bib8 publication-title: Epidemiol. Community Health – volume: 7 start-page: e1000307 year: 2010 ident: bib31 article-title: A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma publication-title: PLoS Med. – volume: 6 start-page: 38 year: 2004 end-page: 47 ident: bib17 article-title: An epidemiologic assessment of genomic profiling for measuring susceptibility to common diseases and targeting interventions publication-title: Genet. Med. – volume: 123 start-page: 517 year: 2013 end-page: 525 ident: bib32 article-title: Prognostically relevant gene signatures of high-grade serious ovarian carcinoma publication-title: J. Clin. Invest. – reference: B. MacMahon T.F. Pugh Causes and entities of disease In: Sander Greenland(Ed.), Evolution of Epidemiologic Ideas. Epidemiology Resources 1987 11 18. – volume: 121 start-page: 1313 year: 2013 end-page: 1318 ident: bib4 article-title: Perfluorooctanoic acid (PFOA) exposures and incident cancers among adults living near a chemical plant publication-title: Environ. Health Perspect. – volume: 176 start-page: 909 year: 2012 end-page: 917 ident: bib28 article-title: Cohort mortality study of workers exposed to perfluorooctanoic acid publication-title: Am. J. Epidemiol. – volume: 102 start-page: 1012 year: 2010 end-page: 1022 ident: bib19 article-title: Cigarette smoking and colorectal cancer risk by molecularly defined subtypes publication-title: J. Natl. Cancer Inst. – volume: 96 start-page: 520 year: 2005 end-page: 530 ident: bib18 article-title: Histologic types of epithelial ovarian cancer: have they different risk factors? publication-title: Gynecol. Oncol. – volume: 61 start-page: 930 year: 2004 end-page: 935 ident: bib24 article-title: The impact of exposure categorisation for grouped analyses of cohort data publication-title: Occup. Environ. Med. – volume: 62 start-page: 2890 year: 2002 end-page: 2896 ident: bib14 article-title: Identification of differentially expressed genes in pancreatic cancer cells using cDNA microarray publication-title: Cancer Res. – reference: C8 Health Project. 2012. C8 Health Project website. Available: 〈 – volume: 27 start-page: 491 year: 2013 end-page: 502 ident: bib27 article-title: Accuracy loss due to selection bias in cohort studies with left truncation publication-title: Paedoatr. Perinat. Epidemiol. – volume: 26 start-page: 129 year: 1972 end-page: 138 ident: bib7 article-title: Histology in breast cancer prognosis publication-title: Br. J. Cancer – volume: 12 start-page: 601 year: 2006 end-page: 612 ident: bib3 article-title: Quantifying the impact of survivor treatment bias in observational studies publication-title: J. Eval. Clin. Pract. – volume: 95 start-page: 255 issue: 2 year: 2010 ident: 10.1016/j.envres.2015.03.039_bib20 article-title: Cigarette smoking and epithelial ovarian cancer by histologic type publication-title: Obstet. Gynecol. doi: 10.1016/S0029-7844(99)00531-1 – volume: 26 start-page: 129 issue: 2 year: 1972 ident: 10.1016/j.envres.2015.03.039_bib7 article-title: Histology in breast cancer prognosis publication-title: Br. J. Cancer doi: 10.1038/bjc.1972.19 – volume: 6 start-page: 38 issue: 1 year: 2004 ident: 10.1016/j.envres.2015.03.039_bib17 article-title: An epidemiologic assessment of genomic profiling for measuring susceptibility to common diseases and targeting interventions publication-title: Genet. Med. doi: 10.1097/01.GIM.0000105751.71430.79 – volume: 3 start-page: 227 issue: 3 year: 1994 ident: 10.1016/j.envres.2015.03.039_bib2 article-title: Effects of frailty in survival analysis publication-title: Stat. Methods Med. Res. doi: 10.1177/096228029400300303 – volume: 96 start-page: 520 issue: 2 year: 2005 ident: 10.1016/j.envres.2015.03.039_bib18 article-title: Histologic types of epithelial ovarian cancer: have they different risk factors? publication-title: Gynecol. Oncol. doi: 10.1016/j.ygyno.2004.10.037 – volume: 121 start-page: 893 issue: 8 year: 2013 ident: 10.1016/j.envres.2015.03.039_bib33 article-title: Design, methods, and population for a study of PFOA health effects among highly exposed mid-Ohio valley community residents and workers publication-title: Environ. Health Perspect. doi: 10.1289/ehp.1206450 – volume: 65 start-page: 430 issue: 6 year: 2008 ident: 10.1016/j.envres.2015.03.039_bib6 article-title: Genetic susceptibility to occupational exposures publication-title: Occup. Environ. Med. doi: 10.1136/oem.2007.033977 – volume: 27 start-page: 491 issue: 5 year: 2013 ident: 10.1016/j.envres.2015.03.039_bib27 article-title: Accuracy loss due to selection bias in cohort studies with left truncation publication-title: Paedoatr. Perinat. Epidemiol. doi: 10.1111/ppe.12073 – volume: 158 start-page: 915 issue: 9 year: 2003 ident: 10.1016/j.envres.2015.03.039_bib26 article-title: Evaluating medication effects outside of clinical trials: new-user designs publication-title: Am. J. Epidemiol. doi: 10.1093/aje/kwg231 – volume: 58 start-page: 635 issue: 8 year: 2004 ident: 10.1016/j.envres.2015.03.039_bib8 publication-title: Epidemiol. Community Health doi: 10.1136/jech.2003.008466 – volume: 12 start-page: 601 issue: 6 year: 2006 ident: 10.1016/j.envres.2015.03.039_bib3 article-title: Quantifying the impact of survivor treatment bias in observational studies publication-title: J. Eval. Clin. Pract. doi: 10.1111/j.1365-2753.2005.00624.x – ident: 10.1016/j.envres.2015.03.039_bib5 – ident: 10.1016/j.envres.2015.03.039_bib12 – volume: 123 start-page: 517 issue: 1 year: 2013 ident: 10.1016/j.envres.2015.03.039_bib32 article-title: Prognostically relevant gene signatures of high-grade serious ovarian carcinoma publication-title: J. Clin. Invest. – volume: 8 start-page: S3 issue: Suppl 1 year: 2014 ident: 10.1016/j.envres.2015.03.039_bib10 article-title: Pathway-gene identification for pancreatic cancer survival via doubly regularized Cox regression publication-title: BMC Syst. Biol. doi: 10.1186/1752-0509-8-S1-S3 – volume: 6 start-page: 1496 issue: 9 year: 2011 ident: 10.1016/j.envres.2015.03.039_bib25 publication-title: J. Thorac. Oncol. doi: 10.1097/JTO.0b013e318221f701 – volume: 176 start-page: 909 issue: 10 year: 2012 ident: 10.1016/j.envres.2015.03.039_bib28 article-title: Cohort mortality study of workers exposed to perfluorooctanoic acid publication-title: Am. J. Epidemiol. doi: 10.1093/aje/kws171 – volume: 165 start-page: 444 issue: 4 year: 2006 ident: 10.1016/j.envres.2015.03.039_bib15 article-title: Conditions for bias from differential left truncation publication-title: Am. J. Epidemiol. doi: 10.1093/aje/kwk027 – ident: 10.1016/j.envres.2015.03.039_bib22 – volume: 22 start-page: 599 issue: 4 year: 2011 ident: 10.1016/j.envres.2015.03.039_bib1 article-title: Left susceptibility, and bias in occupational cohort studies publication-title: Epidemiology doi: 10.1097/EDE.0b013e31821d0879 – volume: 117 start-page: 1873 issue: 12 year: 2009 ident: 10.1016/j.envres.2015.03.039_bib9 article-title: The C8 health project: design, methods, and participants publication-title: Environ. Health Perspect. doi: 10.1289/ehp.0800379 – volume: 62 start-page: 2890 issue: 10 year: 2002 ident: 10.1016/j.envres.2015.03.039_bib14 article-title: Identification of differentially expressed genes in pancreatic cancer cells using cDNA microarray publication-title: Cancer Res. – volume: 7 start-page: e1000307 issue: 7 year: 2010 ident: 10.1016/j.envres.2015.03.039_bib31 article-title: A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma publication-title: PLoS Med. doi: 10.1371/journal.pmed.1000307 – volume: 26 start-page: 395 issue: 3 year: 2015 ident: 10.1016/j.envres.2015.03.039_bib29 article-title: Attenuation of exposure–response rate ratios at higher exposures: a simulation study focusing on frailty and measurement error publication-title: Epidemiology doi: 10.1097/EDE.0000000000000259 – volume: 102 start-page: 1012 issue: 14 year: 2010 ident: 10.1016/j.envres.2015.03.039_bib19 article-title: Cigarette smoking and colorectal cancer risk by molecularly defined subtypes publication-title: J. Natl. Cancer Inst. doi: 10.1093/jnci/djq201 – volume: 47 start-page: 1133 issue: 6 year: 2008 ident: 10.1016/j.envres.2015.03.039_bib16 article-title: Survival in ovarian cancer patients by histology and family history publication-title: Acta Oncol. doi: 10.1080/02841860701784544 – volume: 121 start-page: 1313 issue: 11-12 year: 2013 ident: 10.1016/j.envres.2015.03.039_bib4 article-title: Perfluorooctanoic acid (PFOA) exposures and incident cancers among adults living near a chemical plant publication-title: Environ. Health Perspect. doi: 10.1289/ehp.1306615 – volume: 61 start-page: 930 issue: 11 year: 2004 ident: 10.1016/j.envres.2015.03.039_bib24 article-title: The impact of exposure categorisation for grouped analyses of cohort data publication-title: Occup. Environ. Med. doi: 10.1136/oem.2004.014159 – volume: 16 start-page: 355 issue: 3 year: 2005 ident: 10.1016/j.envres.2015.03.039_bib11 article-title: The cross-sectional cohort study: an underutilized design publication-title: Epidemiology doi: 10.1097/01.ede.0000158224.50593.e3 – volume: 13 start-page: 467 issue: 4 year: 2002 ident: 10.1016/j.envres.2015.03.039_bib21 article-title: Cigarette smoking and the risk of mucinous and nonmucinous epithelial ovarian cancer publication-title: Epidemiology doi: 10.1097/00001648-200207000-00016 – volume: 21 start-page: 13 issue: 1 year: 2010 ident: 10.1016/j.envres.2015.03.039_bib13 article-title: The hazards of hazard ratios publication-title: Epidemiology doi: 10.1097/EDE.0b013e3181c1ea43 – start-page: 110 year: 2008 ident: 10.1016/j.envres.2015.03.039_bib23 – volume: 458 start-page: 719 issue: 7239 year: 2009 ident: 10.1016/j.envres.2015.03.039_bib30 article-title: The cancer genome publication-title: Nature doi: 10.1038/nature07943 |
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SubjectTerms | Bias Cohort Studies Cross sections Disease disease incidence disease occurrence dose response Environmental Exposure Estimates Exposure Fatal Fatalities Fatality Humans Mortality Probability Regression risk Simulation Survival Rate Survivor cohort |
Title | Disease fatality and bias in survival cohorts |
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