A Systematic Review of the Statistical Methods Adopted for Analyzing Follow-Up Data in Cohort Multiple Randomized Controlled Trial.

BACKGROUND:The cohort multiple randomized controlled trial (cmRCT) can tackle some of the weaknesses of an RCT which has triggered the interest of researchers considerably over time. Several challenges persist regarding the methods of analyzing such valued data. The paucity of international recommendations concerning the statistical methods for analyzing trial data has led to a variety of strategies further complicating the result comparison. Our aim was to review the different cmRCT analysis methods since cmRCT was first proposed in 2010. METHODOLOGY:A search for full-length studies presenting statistical analysis of the data collected adopting a cmRCT design was conducted on PubMed, Cochrane Library, EMBASE, JSTOR, Scopus, MEDLINE, and ClinicalTrials.gov. RESULTS:Out of 186 studies screened, we selected 22 for the full-text screening and 11 were found eligible for data extraction. All 11 studies were conducted in high-income countries, reflecting the design being underutilized in other settings. All of the studies were found to have used intention-to-treat (ITT) analysis with four of them utilizing instrumental variables (IV) analysis or a complier average causal effect (CACE). Randomization was noted often to be interchangeably used for random selection. Sample size calculation was not clearly specified in the majority of the studies. CONCLUSION:Clarity regarding the distinction between an RCT and a cmRCT is warranted. The fundamental difference in design, which leads to certain biases that need to be taken care of by adopting IV or CACE analysis, has to be understood before taking up a cmRCT.