A simple approach to power and sample size calculations in logistic regression and Cox regression models

2004 ◽  
Vol 23 (11) ◽  
pp. 1781-1792 ◽  
Author(s):  
Michael Væth ◽  
Eva Skovlund
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Regression Models ◽  
Cox Regression ◽  
Simple Approach ◽  
Sample Size Calculations

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

scholarly journals Power and sample size for multivariate logistic modeling of unmatched case-control studies

2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Case Control ◽  
Simulation Methods ◽  
Case Control Studies ◽  
Control Data ◽  
Logistic Analysis ◽  
Sample Size Calculations ◽  
Control Designs ◽  
Univariate Analyses

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

scholarly journals Hemodynamic–morphological discriminant models for intracranial aneurysm rupture remain stable with increasing sample size

2014 ◽  
Vol 8 (1) ◽  
pp. 104-110 ◽  
Author(s):  
Jianping Xiang ◽  
Jihnhee Yu ◽  
Kenneth V Snyder ◽  
Elad I Levy ◽  
Adnan H Siddiqui ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Intracranial Aneurysm ◽  
Sample Size ◽  
Regression Models ◽  
Prediction Models ◽  
Flow Simulation ◽  
Aneurysm Rupture ◽  
Hemodynamic Parameters ◽  
Operating Characteristics

BackgroundWe previously established three logistic regression models for discriminating intracranial aneurysm rupture status based on morphological and hemodynamic analysis of 119 aneurysms. In this study, we tested if these models would remain stable with increasing sample size, and investigated sample sizes required for various confidence levels (CIs).MethodsWe augmented our previous dataset of 119 aneurysms into a new dataset of 204 samples by collecting an additional 85 consecutive aneurysms, on which we performed flow simulation and calculated morphological and hemodynamic parameters, as done previously. We performed univariate significance tests on these parameters, and multivariate logistic regression on significant parameters. The new regression models were compared against the original models. Receiver operating characteristics analysis was applied to compare the performance of regression models. Furthermore, we performed regression analysis based on bootstrapping resampling statistical simulations to explore how many aneurysm cases were required to generate stable models.ResultsUnivariate tests of the 204 aneurysms generated an identical list of significant morphological and hemodynamic parameters as previously (from the analysis of 119 cases). Furthermore, multivariate regression analysis produced three parsimonious predictive models that were almost identical to the previous ones, with model coefficients that had narrower CIs than the original ones. Bootstrapping showed that 10%, 5%, 2%, and 1% convergence levels of CI required 120, 200, 500, and 900 aneurysms, respectively.ConclusionsOur original hemodynamic–morphological rupture prediction models are stable and improve with increasing sample size. Results from resampling statistical simulations provide guidance for designing future large multi-population studies.

scholarly journals Sample size issues in multilevel logistic regression models

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0225427 ◽  
Author(s):  
Amjad Ali ◽  
Sabz Ali ◽  
Sajjad Ahmad Khan ◽  
Dost Muhammad Khan ◽  
Kamran Abbas ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Regression Models ◽  
Multilevel Logistic Regression ◽  
Logistic Regression Models

scholarly journals Sample size calculation to externally validate scoring systems based on logistic regression models

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176726 ◽  
Author(s):  
Antonio Palazón-Bru ◽  
David Manuel Folgado-de la Rosa ◽  
Ernesto Cortés-Castell ◽  
María Teresa López-Cascales ◽  
Vicente Francisco Gil-Guillén
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Regression Models ◽  
Sample Size Calculation ◽  
Scoring Systems ◽  
Logistic Regression Models

Power and sample size calculations for generalized regression models with covariate measurement error

2003 ◽  
Vol 22 (7) ◽  
pp. 1069-1082 ◽  
Author(s):  
Tor D. Tosteson ◽  
Jeffrey S. Buzas ◽  
Eugene Demidenko ◽  
Margaret Karagas
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Regression Models ◽  
Covariate Measurement Error ◽  
Sample Size Calculations ◽  
Generalized Regression
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