The Quality of Reporting of Measures of Precision in Animal Experiments in Implant Dentistry: A Methodological Study.
Information on precision of treatment-effect estimates is pivotal for understanding research findings. In animal experiments, which provide important information for supporting clinical trials in implant dentistry, inaccurate information may lead to biased clinical trials. The aim of this methodological study was to determine whether sample size calculation, standard errors, and confidence intervals for treatment-effect estimates are reported accurately in publications describing animal experiments in implant dentistry.



MEDLINE (via PubMed), Scopus, and SciELO databases were searched to identify reports involving animal experiments with dental implants published from September 2010 to March 2015. Data from publications were extracted into a standardized form with nine items related to precision of treatment estimates and experiment characteristics. Data selection and extraction were performed independently and in duplicate, with disagreements resolved by discussion-based consensus. The chi-square and Fisher exact tests were used to assess differences in reporting according to study sponsorship type and impact factor of the journal of publication.



The sample comprised reports of 161 animal experiments. Sample size calculation was reported in five (2%) publications. P values and confidence intervals were reported in 152 (94%) and 13 (8%) of these publications, respectively. Standard errors were reported in 19 (12%) publications. Confidence intervals were better reported in publications describing industry-supported animal experiments (P = .03) and with a higher impact factor (P = .02).



Information on precision of estimates is rarely reported in publications describing animal experiments in implant dentistry. This lack of information makes it difficult to evaluate whether the translation of animal research findings to clinical trials is adequate.
Clovis Mariano
CM
Faggion
Luisiana
L
Aranda
Karla Tatiana
KT
Diaz
Ming-Chieh
MC
Shih
Yu-Kang
YK
Tu
Marco Antonio
MA
Alarcón