Self-Controlled Case Series Analysis With Event-Dependent Observation Periods


Farrington, CP; Anaya-Izquierdo, K; Whitaker, HJ; Hocine, MN; Douglas, I; Smeeth, L; (2011) Self-Controlled Case Series Analysis With Event-Dependent Observation Periods. Journal of the American Statistical Association, 106 (494). pp. 417-426. ISSN 0162-1459 DOI: https://doi.org/10.1198/jasa.2011.ap10108

Full text not available from this repository. (Request a copy)

Abstract

The self-controlled case series method may be used to study the association between a time-varying exposure and a health event. It is based only on cases, and it controls for fixed confounders. Exposure and event histories are collected for each case over a predefined observation period. The method requires that observation periods should be independent of event times. This requirement is violated when events increase the mortality rate, since censoring of the observation periods is then event dependent. In this article, the case series method for rare nonrecurrent events is extended to remove this independence assumption, thus introducing an additional term in the likelihood that depends on the censoring process. In order to remain within the case series framework in which only cases are sampled, the model is reparameterized so that this additional term becomes estimable from the distribution of intervals from event to end of observation. The exposure effect of primary interest may be estimated unbiasedly. The age effect, however, takes on a new interpretation, incorporating the effect of censoring. The model may be fitted in standard loglinear modeling software; this yields conservative standard errors. We describe a detailed application to the study of antipsychotics and stroke. The estimates obtained from the standard case series model are shown to be biased when event-dependent observation periods are ignored. When they are allowed for, antipsychotic use remains strongly positively associated with stroke in patients with dementia, but not in patients without dementia. Two detailed simulation studies are included as Supplemental Material.

Item Type: Article
Keywords: Censoring, Conditional Poisson regression, Epidemiologic study, models
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Research Centre: Centre for Global Non-Communicable Diseases (NCDs)
Centre for Statistical Methodology
Web of Science ID: 293113300004
URI: http://researchonline.lshtm.ac.uk/id/eprint/142

Statistics


Download activity - last 12 months
Downloads since deposit
0Downloads
348Hits
Accesses by country - last 12 months
Accesses by referrer - last 12 months
Impact and interest
Additional statistics for this record are available via IRStats2

Actions (login required)

Edit Item Edit Item