Can linear regression model give reliable conclusions about the postoperative recovery? Bootstrap for case study in Mitic <i>et al.</i> 2007

Benth, Jūratė Šaltytė; Dahl, Fredrik A.; Mitic, S.

doi:10.1111/j.1749-4486.2008.01661.x

Cited by 4 publications

(6 citation statements)

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Measurementsmentioning

confidence: 99%

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

Section: Descriptive Analysis Of Study Variablesmentioning

confidence: 99%

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

Nshimyumuremyi¹,

Muziki²,

Harerimana³

et al. 2023

PRBM

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Background:The 1994 Tutsi genocide in Rwanda significantly impacted family structure, with many people growing old alone and lacking social bonds and connections with family members. However, little is known about the contribution of the family environment to geriatric depression which was highlighted by WHO as a psychological problem with a 10% to 20% prevalence rate among the elderly worldwide. This study aims to investigate geriatric depression and associated family determinants among the elderly in Rwanda.Methods: With a community-based cross-sectional study design, we assessed geriatric depression (GD), quality-of-life enjoyment and satisfaction (QLES), family support (FS), loneliness, neglect, and attitude toward grief in a convenience sample of 107 participants (M=72.32, SD=8.79) aged between 60 and 95 years who were recruited from three groups of elderly people supported by the NSINDAGIZA organization in Rwanda. SPSS (version 24) was used for statistical data analysis; differences across various sociodemographic variables were tested for significance by an independent t-test; the relationship between study variables was tested by Pearson correlation analysis; and multiple regression analysis was performed to model the contribution of independent variables to dependent variables. Results: A total of 64.5% of the elderly scored above the threshold of the normal range of geriatric depression (SDS>49), with higher symptoms in women than in men. Multiple regression analysis indicated that family support and quality-of-life enjoyment and satisfaction were contributors to geriatric depression in the participants. Conclusion: Geriatric depression was relatively common in our participants. It is associated with the quality of life and family support received. Hence, adequate family-based interventions are needed to improve the well-being of geriatric people in their respective families.

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Section: Discussionmentioning

confidence: 99%

Section: Measurementsmentioning

confidence: 99%

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

Nshimyumuremyi¹,

Muziki²,

Harerimana³

et al. 2023

PRBM

View full text Add to dashboard Cite

show abstract

“…Bootstrapping is a statistical method that uses random sampling with replacement. It is an alternative to traditional hypothesis tests that does not require these assumptions ( Šaltytė Benth et al, 2008 ). The achieved significance level (i.e., p -value) was estimated and reported by calculating the proportion of the value of test statistics from bootstrapping that was greater than or equal to the original value of test statistics ( Walters & Campbell, 2004 ).…”

Section: Methodsmentioning

confidence: 99%

Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

Zhang

2022

Hum Factors

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Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

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“…When inferences are based on certain parametric assumptions, which may seem tentative, or parametric inference is impossible, the bootstrap method is often used as an alternative, because this method does not need these assumptions. [ 22 ] Therefore, our study aims to evaluate the effect of some factors on the BMI among a nationally-representative sample of Iranian children and adolescents by using bootstrap resampling technique in multiple regression analysis and shows that the conclusions drawn about the BMI of children and adolescent are reliable without assumptions about the distribution of the linear regression model.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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Can linear regression model give reliable conclusions about the postoperative recovery? Bootstrap for case study in Mitic et al. 2007

Cited by 4 publications

References 5 publications

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

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

Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

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

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