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Case-Only Designs in Pharmacoepidemiology: A Systematic Review

BACKGROUND: Case-only designs have been used since late 1980’s. In these, as opposed to case-control or cohort studies for instance, only cases are required and are self-controlled, eliminating selection biases and confounding related to control subjects, and time-invariant characteristics. The obje...

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Detalles Bibliográficos
Autores principales: Nordmann, Sandra, Biard, Lucie, Ravaud, Philippe, Esposito-Farèse, Marina, Tubach, Florence
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500300/
https://www.ncbi.nlm.nih.gov/pubmed/23166668
http://dx.doi.org/10.1371/journal.pone.0049444
Descripción
Sumario:BACKGROUND: Case-only designs have been used since late 1980’s. In these, as opposed to case-control or cohort studies for instance, only cases are required and are self-controlled, eliminating selection biases and confounding related to control subjects, and time-invariant characteristics. The objectives of this systematic review were to analyze how the two main case-only designs – case-crossover (CC) and self-controlled case series (SCCS) – have been applied and reported in pharmacoepidemiology literature, in terms of applicability assumptions and specificities of these designs. METHODOLOGY/PRINCIPAL FINDINGS: We systematically selected all reports in this field involving case-only designs from MEDLINE and EMBASE up to September 15, 2010. Data were extracted using a standardized form. The analysis included 93 reports 50 (54%) of CC and 45 (48%) SCCS, 2 reports combined both designs. In 12 (24%) CC and 18 (40%) SCCS articles, all applicable validity assumptions of the designs were fulfilled, respectively. Fifty (54%) articles (15 CC (30%) and 35 (78%) SCCS) adequately addressed the specificities of the case-only analyses in the way they reported results. CONCLUSIONS/SIGNIFICANCE: Our systematic review underlines that implementation of CC and SCCS designs needs to be more rigorous with regard to validity assumptions, as well as improvement in results reporting.