Heterogeneity Models of Disease Susceptibility, with Application to Diabetic Nephropathy

It is not, in general, possible to include all relevant risk factors in a model of survival or disease incidence. This heterogeneity must be accounted for in the interpretation, as it can imply otherwise unexpected results. This is illustrated by diabetic nephropathy, a serious complication experien...

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Bibliographic Details
Published in:Biometrics 1994-12, Vol.50 (4), p.1178-1188
Main Authors: Hougaard, Philip, Myglegaard, Per, Borch-Johnsen, Knut
Format: Article
Language:English
Subjects:
Adult
Age of Onset
Biometrics
Biometry - methods
Censorship
Diabetes
Diabetes complications
Diabetes Mellitus, Type 1 - mortality
Diabetes Mellitus, Type 1 - physiopathology
Diabetic nephropathies
Diabetic Nephropathies - epidemiology
Disease models
Disease Susceptibility
Female
Humans
Incidence
Longitudinal Studies
Male
Models, Statistical
Models, Theoretical
Musical intervals
Parametric models
Poisson Distribution
Proportional Hazards Models
Regression Analysis
Risk Factors
Sex Characteristics
Sex Factors
The Consultant's Forum
Time Factors
Type 1 diabetes mellitus
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Summary:It is not, in general, possible to include all relevant risk factors in a model of survival or disease incidence. This heterogeneity must be accounted for in the interpretation, as it can imply otherwise unexpected results. This is illustrated by diabetic nephropathy, a serious complication experienced by some diabetic patients. A mathematical model with varying susceptibility can explain that the incidence increases until 20 years duration of diabetes and later decreases. The hospital-based data cover patients diagnosed during 1933-1972. They are interval censored, because early detection of nephropathy requires chemical analysis of urine samples. The data are consistent with a model where less than half of the patients are susceptible, and for each of these the hazard is increasing. The estimated degree of heterogeneity markedly depends on the assumed model. The dependence on age at onset and calendar time of onset is examined. The highest risk is seen at onset age 13-17 years, and the risk decreases with calendar time. The effect of covariates on the hazard is markedly different for the various models, but this is partly a matter of parametrization, as the disagreement is reduced by a reparametrization inspired by accelerated failure time models.
ISSN:0006-341X
1541-0420
DOI:10.2307/2533456