A tool to measure complexity in public health interventions: Its statistical properties and meta-regression approach to adjust it in meta-analysis N.

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A tool to measure complexity in public health interventions: Its statistical properties and meta-regression approach to adjust it in meta-analysis N. Ravishankar, N. Sreekumaran Nair Clinical Epidemiology and Global Health Volume 4, Issue 1, Pages 33-39 (March 2016) DOI: 10.1016/j.cegh.2015.03.003 Copyright © 2015 INDIACLEN Terms and Conditions

Fig. 1 Histogram of complexity score of 71 studies. The distributions that could possibly fit the complexity score were anticipated by considering the shape of the histogram. Clinical Epidemiology and Global Health 2016 4, 33-39DOI: (10.1016/j.cegh.2015.03.003) Copyright © 2015 INDIACLEN Terms and Conditions

Fig. 2 Distribution fitting. Probability density curves of all the four anticipated distributions (Normal distribution, Gamma distribution, Lognormal distribution, Weibull distribution) that could fit the complexity score. Clinical Epidemiology and Global Health 2016 4, 33-39DOI: (10.1016/j.cegh.2015.03.003) Copyright © 2015 INDIACLEN Terms and Conditions

Fig. 3 Sampling distribution of the mean of complexity score. Histogram of sampling distribution of the mean of complexity score. Clinical Epidemiology and Global Health 2016 4, 33-39DOI: (10.1016/j.cegh.2015.03.003) Copyright © 2015 INDIACLEN Terms and Conditions

Fig. 4 Box–Cox transformation. Histogram of transformed complexity score. Clinical Epidemiology and Global Health 2016 4, 33-39DOI: (10.1016/j.cegh.2015.03.003) Copyright © 2015 INDIACLEN Terms and Conditions

Fig. 5 Forest Plot. Close comparison of unadjusted pooled estimate and the pooled estimate adjusted for complexity. Clinical Epidemiology and Global Health 2016 4, 33-39DOI: (10.1016/j.cegh.2015.03.003) Copyright © 2015 INDIACLEN Terms and Conditions