IntroductionSystematic reviews (SRs) are today's cornerstone of evidence-based medicine. However, their risk of bias (ROB) may critically impact their findings. Hence, an impartial assessment of their ROB is paramount to their interpretation. The objective of this study is to evaluate the potential association between the results of the ROB assessment for a series of SRs and their corresponding journal's impact factor as determined by the citation reports.MethodsA sample of over 500 SRs and their corresponding ROB will be employed in this study. The source for these data will be the database KSR Evidence. The corresponding impact factor (IF) for the publishing journal as reported by the Science Citation Index will also be retrieved. The total of ROBIS signaling questions answered as ‘yes’ or ‘probably yes’ will be used to approximate the awarded quality (Quality) for each systematic review. An analysis of the potential correlation between Quality and the IF will be performed with a simple linear regression.ResultsResults will be presented in tables and figures. Preliminary results confirm that a statistically significant association between the suggested variables exists, though this is of low magnitude.ConclusionsFindings confirm that the ROB of an SR and the IF of the publishing journal are correlated.

INTRODUCTION:The expanding range of disease modifying therapies (DMT) for relapsing-remitting multiple sclerosis (RRMS) has led to increased interest in the relative effects of different DMTs. Previous mixed treatment comparisons (MTCs) have used different methods to address similar questions highlighting the need for a consistent approach to the assessment of treatments in RRMS.METHODS:We compared the methodology of six published MTCs of DMTs for RRMS identified by a systematic search of the literature. We assessed sources of evidence, DMTs included, outcomes reported and methods of data synthesis.RESULTS:All six MTCs were based on systematic reviews that included randomized controlled trials (RCTs). MS relapse was reported as the rate ratio based on annualised relapse rates (four MTCs) and as odds ratios or relative risk (one MTC each) based on the proportion with relapse. The analysis of relapse included between sixteen and twenty-seven RCTs and seven to twenty DMTs in different MTCs. One MTC reported both disability progression confirmed after three months (CDP3M) and disability progression confirmed after six months (CDP6M) as hazard ratios. One MTC combined CDP3M and CDP6M as a single outcome. One MTC reported only CDP3M based on hazard ratios. Two MTCs reported only CDP6M as either odds ratios or risk ratios (one MTC each). In one MTC the definition of disability progression was not reported. The analysis of disability included between seven and twenty-six RCTs and between six and nineteen DMTs in different MTCs. All six MTCs fitted a random effects MTC model using either Bayesian (four MTCs) or frequentist (two MTCs) methods.CONCLUSIONS:There is substantial heterogeneity between published MTCs in RRMS with regard to inclusion criteria, outcome definitions, effect measures and statistical methods. There is a clear requirement for a consistent approach to health technology assessment of DMTs for RRMS.