Analysis of Factors Affecting the Body Mass Index in a National Sample of Iranian Children and Adolescents: Bootstrapping Regression

Abstract

Background:This study aims to evaluate the effect of some factors, including birth weight, sex, age, waist circumference, family history of obesity, as well as some lifestyle factors as frequency of breakfast days and physical activity, on the body mass index among a nationally representative sample of Iranian children and adolescents by using bootstrapping regression.Materials and Methods:This study was conducted as the third survey of a school-based surveillance system (CASPIAN-III study). Total participants were 5570 school students, aged 10-18 years, selected by multistage random cluster sampling from urban and rural areas of 27 provinces of Iran. Multiple linear regressions was used to evaluate the effect of various factors on obesity, but in our data the assumptions of this model violated, and possible solutions were not appropriate, therefore the bootstrapping regression based on the observations and errors resampling approaches was used as an alternative.Results:The tests of significance showed that the effects of sex, age, waist circumference, family history obesity and frequency of breakfast days were clearly significant (P < 0.001). The effect of vigorous level of physical activity was significant in comparison to mild physical activity (P = 0.01). In comparison to low birth weight, medium and high birth weight had significant effect on obesity.Conclusions:Bootstrap method is preferable in linear regression because of some theoretical properties like having any distributional assumptions on the residuals and hence allows for inference even if the errors do not follow normal distribution or constant error variance.

The psychological impact of COVID-19 and restrictive measures in the world

“…Since the normality assumptions necessary to perform the analysis of the covariance (ANCOVA) were not respected, the techniques of bootstrap with the accidental simulation of Mersenne Twister were also considered ( Delucchi and Bostrom, 2004 ; Hosseini et al., 2017 ; Wehrens et al., 2000 ; Wright et al., 2011 ); literature indicates that a number of 500 bootstrap extractions are sufficient for correctness of the resample ( Deng et al., 2013 ).…”

Prevalence and Family Determinants of Geriatric Depression Among Elderly People in Elderly Support Groups in Rwanda

“…59 Fortunately, there are estimation techniques other than normal theory, such as bootstrapping, an alluring estimation technique that requires few assumptions and deals with non-normally distributed data. [60][61][62][63] Hence, a bootstrapped independent t-test, correlation, and multiple linear regressions with 5000 bootstrap samples were used to make the right decision on the use of predicting analysis for determinants of geriatric depression. It helped to calculate statistical significance using 95% confidence intervals.…”

“…The Self-Rating Depression Scale (SDS) consists of 20 items to screen for geriatric depression symptoms "over the past several days" on a 4-point Likert-type scale ranging from 1 (little or none of the time) to 4 (most or all of the time). 52 The plausible scores range from 20 through 80, where the score classifications are as follows: normal (<50), mild depression (50 −59), moderate to marked major depression (60)(61)(62)(63)(64)(65)(66)(67)(68)(69), and severe to extreme major depression (>70). 52 In this study, the Cronbach's alpha coefficient was 0.67.…”

“…Consequently, using the bootstrapping technique was a suitable solution for further analysis. [60][61][62][63] Rate of Geriatric Depression Among the Participants…”