A penalized likelihood approach for arbitrarily censored and truncated data: application to age-specific incidence of dementia.
The Cos model is the model of choice when analyzing survival data presenting only right censoring and left truncation. There is a need for methods that can accommodate more complex observation schemes involving general censoring and truncation. In addition, it is important in many epidemiological applications to have a smooth estimate of the hazard function. We show that the penalized likelihood approach gives a solution to these problems. The solution of the maximum of the penalized likelihood is approximated on a basis of splines. The smoothing parameter is estimated using approximate cross-validation; confidence bands can be given. A simulation study shows that this approach gives better results than the smoothed Nelson-Aalen estimator. We apply this method to the analysis of data from a large cohort study on cerebral aging. The age-specific incidence of dementia is estimated and risk factors of dementia studied.
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INSERM U 330, Université de Bordeaux II, France. Daniel.Commenges@bordeaux.inserm.fr
Commenges
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Letenneur
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D000367:Age Factors; D000368:Aged; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D015331:Cohort Studies; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003704:Dementia / Q000453:epidemiology*; D015340:Epidemiologic Research Design; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors