Self-controlled designs in pharmacoepidemiology involving electronic healthcare databases: a systematic review.
Observational studies are widely used in pharmacoepidemiology. Several designs can be used, in particular self-controlled designs (case-crossover and self-controlled case series). These designs offer the advantage of controlling for time-invariant confounders, which may not be collected in electronic healthcare databases. They are particularly useful in pharmacoepidemiology involving healthcare database. To be valid, they require the presence of some characteristics (key validity assumptions), and in such situations, these designs should be preferred. We aimed at describing the appropriate use and reporting of the key validity assumptions in self-controlled design studies.



Articles published between January 2011 and December 2014, and describing a self-controlled study design involving electronic healthcare databases were retrieved. The appropriate use (fulfilment of key assumptions) was studied in terms of major (abrupt onset event, rare or recurrent event, and intermittent exposure) and minor assumptions (those for which the design can be adapted).



Among the 107 articles describing a self-controlled design, 35/53 (66%) case-crossover studies, and 48/55 (87%) self-controlled case series fulfilled the major validity assumptions for use of the design; 4/35 and 14/48 respectively did not fulfill the minor assumptions. Overall, 31/53 (58%) case-crossover studies and 34/55 (62%) self-controlled case series fulfilled both major and minor assumptions. The reporting of the methodology or the results was appropriate, except for power calculation.



Self-controlled designs were not appropriately used in34% and 13% of the articles we reviewed that described a case-crossover or a self-controlled case series design, respectively. We encourage better use of these designs in situations in which major validity assumptions are fulfilled (i.e., for which they are recommended), accounting for situations for which the design can be adapted.
APHP, Département d'Epidémiologie Biostatistiques et Recherche Clinique, Hôpital Bichat, 75018, Paris, France. nathalie.gault@aphp.fr.|Université Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, 75018, Paris, France. nathalie.gault@aphp.fr.|INSERM CIC-EC 1425, Hôpital Bichat, 75018, Paris, France. nathalie.gault@aphp.fr.
Nathalie
0000-0002-9913-9760
N
Gault
Université Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, 75018, Paris, France.|APHP, Département Biostatistiques Santé Publique et Information Médicale, Centre de Pharmaco-épidémiologie de l'AP-HP, Hôpital Pitié-Salpétrière, 75013, Paris, France.
Johann
J
Castañeda-Sanabria
Université Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, 75018, Paris, France.|APHP, Département Biostatistiques Santé Publique et Information Médicale, Centre de Pharmaco-épidémiologie de l'AP-HP, Hôpital Pitié-Salpétrière, 75013, Paris, France.
Yann
Y
De Rycke
Université Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, 75018, Paris, France.|APHP, Département Biostatistiques Santé Publique et Information Médicale, Centre de Pharmaco-épidémiologie de l'AP-HP, Hôpital Pitié-Salpétrière, 75013, Paris, France.
Sylvie
S
Guillo
Biostatistics unit, Gustave Roussy, 94800, Villejuif, France.|CESP, Université Paris-Sud, UVSQ, INSERM, Université Paris-Saclay, 94800, Villejuif, France.
Stéphanie
S
Foulon
Université Paris Diderot, Sorbonne Paris Cité, UMR 1123 ECEVE, 75018, Paris, France.|APHP, Département Biostatistiques Santé Publique et Information Médicale, Centre de Pharmaco-épidémiologie de l'AP-HP, Hôpital Pitié-Salpétrière, 75013, Paris, France.|Université Pierre et Marie Curie, Sorbonne Universités, 75013, Paris, France.
Florence
F
Tubach
D018592:Cross-Over Studies; D016208:Databases, Factual / Q000706:statistics & numerical data*; D003695:Delivery of Health Care / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D017891:Pharmacoepidemiology / Q000379:methods / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data*
Databases; Observational studies; Pharmacoepidemiology; Self-controlled designs; Systematic review