Mitigating bias and dealing with multiple time scales in cohort studies

studying medications and complications of diabetes

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

Cohort studies are an important and powerful tool of epidemiologic research. When based on a representative cohort, observational cohort studies provide results of a high external validity given that the internal validity is not impaired by bias. Diabetes and cancer represent two prevalent, complex, diverse and potentially fatal chronic diseases, occurs more often than could be expected by chance only. Cancer and diabetes share common risk factors, such as obesity and smoking, but also antidiabetic medications may play a role. I studied the relationship between the use of antidiabetic medications and cancer risk in two retrospectively conducted observational cohort studies, when mitigating bias through the study design and analytical methods. In Study I, I studied the risk of cancer in 23 394 individuals from the National FINRISK cohorts. I assessed the risk along time since the initiation of anti-diabetic medication and adjusted for several risk factors, including smoking and body mass index. I found no association between cancer risk and the use of antidiabetic medication. In Study II on the CARING (CAncer Risk and INsulin Analogues) five-country (Denmark, Finland, Norway, Sweden, UK) cohort of 327 112 new insulin users, the risks of ten site-specific cancers and any cancer were scrutinized by contrasting the cumulative exposures to human insulin and insulin analogues glargine and detemir, when mitigating biases involved in previous observational studies. We found no consistent differences in the studied risks as assessed for insulin glargine or insulin detemir use relative to use of human insulin. Due to their longitudinal nature, cohort studies involve at least one time scale at which the time-dependent dynamics of a phenomenon can be studied. There are often several relevant time scales, but the traditional statistical methods of survival analysis, such as Cox’s proportional hazards model, rely on a single time scale. In the methodological part of this thesis, I addressed the issue of multiple time scales. In Study III, I addressed the issue of multiple time scales in cohort studies by introducing and evaluating a nonparametric Bayesian model for estimation of intensity on two time scales jointly. Because of the built-in smoothing and borrowing of strength in two dimensions, the model outperformed two other methods when applied to simulated data. In Study IV, I used the Bayesian intensity model to explore the time-dependent dynamics of end-stage-renal-disease and death without end-stage-renal disease in 11 810 individuals with type 1 diabetes from the nation-wide FinDM study. The time-dependent dynamics of these outcomes were modelled on two and three time scales jointly, including age, diabetes duration and calendar time. The study demonstrated that the estimation of multidimensional hazard allows for addressing both empirical and methodological questions.
Original languageEnglish
Awarding Institution
  • University of Helsinki
Supervisors/Advisors
  • Haukka, Jari, Supervisor
  • Härkänen, Tommi, Supervisor, External person
Award date30 Nov 2018
Place of PublicationHelsinki
Publisher
Print ISBNs978-951-51-4718-9
Electronic ISBNs978-951-51-4719-6
Publication statusPublished - 2018
MoE publication typeG5 Doctoral dissertation (article)

Fields of Science

  • Hypoglycemic Agents
  • Diabetes Complications
  • Time Factors
  • Bias
  • 3142 Public health care science, environmental and occupational health

Cite this

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title = "Mitigating bias and dealing with multiple time scales in cohort studies: studying medications and complications of diabetes",
abstract = "Cohort studies are an important and powerful tool of epidemiologic research. When based on a representative cohort, observational cohort studies provide results of a high external validity given that the internal validity is not impaired by bias. Diabetes and cancer represent two prevalent, complex, diverse and potentially fatal chronic diseases, occurs more often than could be expected by chance only. Cancer and diabetes share common risk factors, such as obesity and smoking, but also antidiabetic medications may play a role. I studied the relationship between the use of antidiabetic medications and cancer risk in two retrospectively conducted observational cohort studies, when mitigating bias through the study design and analytical methods. In Study I, I studied the risk of cancer in 23 394 individuals from the National FINRISK cohorts. I assessed the risk along time since the initiation of anti-diabetic medication and adjusted for several risk factors, including smoking and body mass index. I found no association between cancer risk and the use of antidiabetic medication. In Study II on the CARING (CAncer Risk and INsulin Analogues) five-country (Denmark, Finland, Norway, Sweden, UK) cohort of 327 112 new insulin users, the risks of ten site-specific cancers and any cancer were scrutinized by contrasting the cumulative exposures to human insulin and insulin analogues glargine and detemir, when mitigating biases involved in previous observational studies. We found no consistent differences in the studied risks as assessed for insulin glargine or insulin detemir use relative to use of human insulin. Due to their longitudinal nature, cohort studies involve at least one time scale at which the time-dependent dynamics of a phenomenon can be studied. There are often several relevant time scales, but the traditional statistical methods of survival analysis, such as Cox’s proportional hazards model, rely on a single time scale. In the methodological part of this thesis, I addressed the issue of multiple time scales. In Study III, I addressed the issue of multiple time scales in cohort studies by introducing and evaluating a nonparametric Bayesian model for estimation of intensity on two time scales jointly. Because of the built-in smoothing and borrowing of strength in two dimensions, the model outperformed two other methods when applied to simulated data. In Study IV, I used the Bayesian intensity model to explore the time-dependent dynamics of end-stage-renal-disease and death without end-stage-renal disease in 11 810 individuals with type 1 diabetes from the nation-wide FinDM study. The time-dependent dynamics of these outcomes were modelled on two and three time scales jointly, including age, diabetes duration and calendar time. The study demonstrated that the estimation of multidimensional hazard allows for addressing both empirical and methodological questions.",
keywords = "Hypoglycemic Agents, Diabetes Complications, Time Factors, Bias, 3142 Public health care science, environmental and occupational health",
author = "Anna But",
note = "M1 - 90 s. + liitteet",
year = "2018",
language = "English",
isbn = "978-951-51-4718-9",
series = "Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis",
publisher = "Helsingin yliopisto",
number = "96/2018",
address = "Finland",
school = "University of Helsinki",

}

Mitigating bias and dealing with multiple time scales in cohort studies : studying medications and complications of diabetes. / But, Anna.

Helsinki : Helsingin yliopisto, 2018. 90 p.

Research output: ThesisDoctoral ThesisCollection of Articles

TY - THES

T1 - Mitigating bias and dealing with multiple time scales in cohort studies

T2 - studying medications and complications of diabetes

AU - But, Anna

N1 - M1 - 90 s. + liitteet

PY - 2018

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N2 - Cohort studies are an important and powerful tool of epidemiologic research. When based on a representative cohort, observational cohort studies provide results of a high external validity given that the internal validity is not impaired by bias. Diabetes and cancer represent two prevalent, complex, diverse and potentially fatal chronic diseases, occurs more often than could be expected by chance only. Cancer and diabetes share common risk factors, such as obesity and smoking, but also antidiabetic medications may play a role. I studied the relationship between the use of antidiabetic medications and cancer risk in two retrospectively conducted observational cohort studies, when mitigating bias through the study design and analytical methods. In Study I, I studied the risk of cancer in 23 394 individuals from the National FINRISK cohorts. I assessed the risk along time since the initiation of anti-diabetic medication and adjusted for several risk factors, including smoking and body mass index. I found no association between cancer risk and the use of antidiabetic medication. In Study II on the CARING (CAncer Risk and INsulin Analogues) five-country (Denmark, Finland, Norway, Sweden, UK) cohort of 327 112 new insulin users, the risks of ten site-specific cancers and any cancer were scrutinized by contrasting the cumulative exposures to human insulin and insulin analogues glargine and detemir, when mitigating biases involved in previous observational studies. We found no consistent differences in the studied risks as assessed for insulin glargine or insulin detemir use relative to use of human insulin. Due to their longitudinal nature, cohort studies involve at least one time scale at which the time-dependent dynamics of a phenomenon can be studied. There are often several relevant time scales, but the traditional statistical methods of survival analysis, such as Cox’s proportional hazards model, rely on a single time scale. In the methodological part of this thesis, I addressed the issue of multiple time scales. In Study III, I addressed the issue of multiple time scales in cohort studies by introducing and evaluating a nonparametric Bayesian model for estimation of intensity on two time scales jointly. Because of the built-in smoothing and borrowing of strength in two dimensions, the model outperformed two other methods when applied to simulated data. In Study IV, I used the Bayesian intensity model to explore the time-dependent dynamics of end-stage-renal-disease and death without end-stage-renal disease in 11 810 individuals with type 1 diabetes from the nation-wide FinDM study. The time-dependent dynamics of these outcomes were modelled on two and three time scales jointly, including age, diabetes duration and calendar time. The study demonstrated that the estimation of multidimensional hazard allows for addressing both empirical and methodological questions.

AB - Cohort studies are an important and powerful tool of epidemiologic research. When based on a representative cohort, observational cohort studies provide results of a high external validity given that the internal validity is not impaired by bias. Diabetes and cancer represent two prevalent, complex, diverse and potentially fatal chronic diseases, occurs more often than could be expected by chance only. Cancer and diabetes share common risk factors, such as obesity and smoking, but also antidiabetic medications may play a role. I studied the relationship between the use of antidiabetic medications and cancer risk in two retrospectively conducted observational cohort studies, when mitigating bias through the study design and analytical methods. In Study I, I studied the risk of cancer in 23 394 individuals from the National FINRISK cohorts. I assessed the risk along time since the initiation of anti-diabetic medication and adjusted for several risk factors, including smoking and body mass index. I found no association between cancer risk and the use of antidiabetic medication. In Study II on the CARING (CAncer Risk and INsulin Analogues) five-country (Denmark, Finland, Norway, Sweden, UK) cohort of 327 112 new insulin users, the risks of ten site-specific cancers and any cancer were scrutinized by contrasting the cumulative exposures to human insulin and insulin analogues glargine and detemir, when mitigating biases involved in previous observational studies. We found no consistent differences in the studied risks as assessed for insulin glargine or insulin detemir use relative to use of human insulin. Due to their longitudinal nature, cohort studies involve at least one time scale at which the time-dependent dynamics of a phenomenon can be studied. There are often several relevant time scales, but the traditional statistical methods of survival analysis, such as Cox’s proportional hazards model, rely on a single time scale. In the methodological part of this thesis, I addressed the issue of multiple time scales. In Study III, I addressed the issue of multiple time scales in cohort studies by introducing and evaluating a nonparametric Bayesian model for estimation of intensity on two time scales jointly. Because of the built-in smoothing and borrowing of strength in two dimensions, the model outperformed two other methods when applied to simulated data. In Study IV, I used the Bayesian intensity model to explore the time-dependent dynamics of end-stage-renal-disease and death without end-stage-renal disease in 11 810 individuals with type 1 diabetes from the nation-wide FinDM study. The time-dependent dynamics of these outcomes were modelled on two and three time scales jointly, including age, diabetes duration and calendar time. The study demonstrated that the estimation of multidimensional hazard allows for addressing both empirical and methodological questions.

KW - Hypoglycemic Agents

KW - Diabetes Complications

KW - Time Factors

KW - Bias

KW - 3142 Public health care science, environmental and occupational health

M3 - Doctoral Thesis

SN - 978-951-51-4718-9

T3 - Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis

PB - Helsingin yliopisto

CY - Helsinki

ER -

But A. Mitigating bias and dealing with multiple time scales in cohort studies: studying medications and complications of diabetes. Helsinki: Helsingin yliopisto, 2018. 90 p. (Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis; 96/2018 ).