RESUMO
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.
RESUMO
Background: Despite several programs and policies to turn down the burden of malnutrition in the country, the rank of India in the Global Hunger Index (GHI) is 102 among 117 countries, which indicates a serious hunger situation. It is essential to design more specific interventions by focusing on the key determinants that may directly or indirectly influence malnutrition in India. Methods: Utilizing data from the National Family and Health Survey-4 (NFHS) (2015-16), we developed a structural equation model to find the direct, indirect, and total effect of various determinants on stunting, wasting, and underweight. We used spatial analysis to identify local occurrences of factors that are critical in controlling malnutrition. A p-value of 0.05 was considered to be significant throughout the study. Analysis was performed using STATA (version 15.1MP) and GeoDa software (version 1.14). Results: A final sample of 90, 842 children of 0-24 months of age was selected for the analysis. The CFI and TLI values of 0.98 and 0.93, respectively, are indicative of a good fit model. Moran's I value of global spatial autocorrelation for the widespread presence of diarrhea, poor drinking water source, exclusive breastfeeding, low birth weight, no prenatal visits, poor toilet facility was observed to be 0.446, 0.638, 0.345, 0.439, 0.620, and 0.727, respectively. Conclusion: A robust direct relation was observed for diarrhea, exclusive breastfeeding, and children born with stunting, underweight, and wasting. The variables associated indirectly with the outcome variables were the education of the mother, residence, and desired pregnancy. The identification of hotspots through spatial analysis would help revive control strategies in the affected area according to geographical needs. It is extensively addressed that interventions related to health and nutrition during the first 1, 000 days of life is crucial to seize the upshoot of growth floundering among children.