Event dependent sampling of recurrent events.

The effect of event-dependent sampling of processes consisting of recurrent events is investigated when analyzing whether the risk of recurrence increases with event count. We study the situation where processes are selected for study if an event occurs in a certain selection interval. Motivation co...

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Publicado en:Lifetime Data Analysis Vol. 16; no. 4; pp. 580 - 599
Autores principales: Kvist K, Andersen PK, Angst J, Kessing LV, Kvist, Kajsa, Andersen, Per Kragh, Angst, Jules, Kessing, Lars Vedel
Formato: research Journal Article
Publicado: Springer Nature Sep2010
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2010
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      pub: Springer Nature
      place: New York, New York
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        2010798614
        10.1007/s10985-010-9172-y
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        atl: Event dependent sampling of recurrent events.
      aug:
        au:
          Kvist K
          Andersen PK
          Angst J
          Kessing LV
          Kvist, Kajsa
          Andersen, Per Kragh
          Angst, Jules
          Kessing, Lars Vedel
        affil: Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
      sug:
        subj:
          Data Analysis, Statistical
          Models, Statistical
          Affective Disorders Epidemiology
          Computer Simulation
          Hospitalization
          Recurrence
          Human
      ab: The effect of event-dependent sampling of processes consisting of recurrent events is investigated when analyzing whether the risk of recurrence increases with event count. We study the situation where processes are selected for study if an event occurs in a certain selection interval. Motivation comes from psychiatric epidemiology where repeated hospital admissions are studied for patients with affective disease, as seen in Kessing et al. (Acta Psychiatr Scand 109:339-344, 2004b). For the selected processes, either only disease course from selection and onwards is used in the analysis, or, both retrospective and prospective disease course histories are used. We examine two methods to correct for the selection depending on which data are used in the analysis. In the first case, the conditional distribution of the process given the pre-selection history is determined. In the second case, an inverse-probability-of-selection weighting scheme is suggested. The ability of the methods to correct for the bias due to selection is investigated with simulations. Furthermore, the methods are applied to affective disease data from a register-based study (Kessing et al. Br J Psychiatry 185:372-377, 2004a) and from a long-term clinical study (Kessing et al. Acta Psychiatr Scand 109:339-344, 2004b).
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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