title: Use and misuse of multivariable approaches in interventional cardiology studies on drug-eluting stents: a systematic review.
abstract: OBJECTIVE
  Randomized clinical trials (RCTs) are the most reliable evidence, even if they require important resource and logistic efforts. Large, cost-free and real-world datasets may be easily accessed yielding to observational studies, but such analyses often lead to problematic results in the absence of careful methods, especially from a statistic point of view. We aimed to appraise the performance of current multivariable approaches in the estimation of causal treatment and effects in studies focusing on drug-eluting stents (DES).
  
  
  RESULTS
  Pertinent studies published in the literature were searched, selected, abstracted, and appraised for quality and validity features. Six studies with a logistic regression were included, all of them reporting more than 10 events for covariates and different length of follow-up, with an overall low risk of bias. Most of the 15 studies with a Cox proportional hazard analysis had a different follow-up, with less than 10 events for covariates, yielding an overall low or moderate risk of bias. Sixteen studies with propensity score were included: the most frequent method for variable selection was logistic regression, with underlying differences in follow-up and less than 10 events for covariate in most of them. Most frequently, calibration appraisal was not reported in the studies, on the contrary of discrimination appraisal, which was more frequently performed. In seventeen studies with propensity and matching, the latter was most commonly performed with a nearest neighbor-matching algorithm yet without appraisal in most of the studies of calibration or discrimination. Balance was evaluated in 46% of the studies, being obtained for all variables in 48% of them.
  
  
  CONCLUSIONS
  Better exploitation and methodological appraisal of multivariable analysis is needed to improve the clinical and research impact and reliability of nonrandomized studies.
authors.affiliation: Division of Cardiology, University of Turin, Turin, Italy. fabrizio.dascenzo@gmail.com
authors.forename: Fabrizio
authors.initials: F
authors.lastname: D'Ascenzo
authors.forename: Erika
authors.initials: E
authors.lastname: Cavallero
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Biondi-Zoccai
authors.forename: Claudio
authors.initials: C
authors.lastname: Moretti
authors.forename: Pierluigi
authors.initials: P
authors.lastname: Omedè
authors.forename: Mario
authors.initials: M
authors.lastname: Bollati
authors.forename: Davide
authors.initials: D
authors.lastname: Castagno
authors.forename: Maria Grazia
authors.initials: MG
authors.lastname: Modena
authors.forename: Fiorenzo
authors.initials: F
authors.lastname: Gaita
authors.forename: Imad
authors.initials: I
authors.lastname: Sheiban
mesh_terms: D003627:Data Interpretation, Statistical; D054855:Drug-Eluting Stents; D006801:Humans; D015999:Multivariate Analysis; D057216:Propensity Score; D016016:Proportional Hazards Models; D012107:Research Design