Rationale: Methodological sources of bias and suboptimal reporting contribute to irreproducibility in preclinical science and may negatively affect research translation. Randomization, blinding, sample size estimation, and considering sex as a biological variable are deemed crucial study design elements to maximize the quality and predictive value of preclinical experiments.
Objective: To examine the prevalence and temporal patterns of recommended study design element implementation in preclinical cardiovascular research.
Methods and results: All articles published over a 10-year period in 5 leading cardiovascular journals were reviewed. Reports of in vivo experiments in nonhuman mammals describing pathophysiology, genetics, or therapeutic interventions relevant to specific cardiovascular disorders were identified. Data on study design and animal model use were collected. Citations at 60 months were additionally examined as a surrogate measure of research impact in a prespecified subset of studies, stratified by individual and cumulative study design elements. Of 28 636 articles screened, 3396 met inclusion criteria. Randomization was reported in 21.8%, blinding in 32.7%, and sample size estimation in 2.3%. Temporal and disease-specific analyses show that the implementation of these study design elements has overall not appreciably increased over the past decade, except in preclinical stroke research, which has uniquely demonstrated significant improvements in methodological rigor. In a subset of 1681 preclinical studies, randomization, blinding, sample size estimation, and inclusion of both sexes were not associated with increased citations at 60 months.
Conclusions: Methodological shortcomings are prevalent in preclinical cardiovascular research, have not substantially improved over the past 10 years, and may be overlooked when basing subsequent studies. Resultant risks of bias and threats to study validity have the potential to hinder progress in cardiovascular medicine as preclinical research often precedes and informs clinical trials. Stroke research quality has uniquely improved in recent years, warranting a closer examination for interventions to model in other cardiovascular fields.
Keywords: animal models; bias; biomedical research; cardiovascular diseases; reproducibility of results.
© 2017 The Authors.