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 inEnvironmental research Vol. 140; pp. 275 - 281
Main Authors Barry, Vaughn, Klein, Mitchel, Winquist, Andrea, Darrow, Lyndsey A., Steenland, Kyle
Format Journal Article
LanguageEnglish
Published Netherlands 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.
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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Keywords Fatality
Simulation
Bias
Survivor cohort
Language English
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Snippet Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated by an...
Objectives Simulate how the effect of exposure on disease occurrence and fatality influences the presence and magnitude of bias in survivor cohorts, motivated...
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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
URI https://dx.doi.org/10.1016/j.envres.2015.03.039
https://www.ncbi.nlm.nih.gov/pubmed/25880887
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Volume 140
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