1,721,088 research outputs found
Different methods to analyze clinical experiments with multiple endpoints: a comparison on real data
No full textMicroembolic signals and carotid plaque morphology: a study of 71 patients with moderate or high grade carotid stenosis
No full textConfronto delle tecniche di SPSS ed R per il calcolo dei gradi di libertà dei test per gli effetti fissi nei modelli lineari misti
No full textValutazione tramite simulazioni della significatività del test Wilcoxon-Mann-Whitney, quando è violata l'assunzione di distribuzioni di forma identica nei campioni confrontati
No full textPrevalence-Value-Accuracy plots vs. ROC analysis to assess the diagnostic value of different ultrasound colour mappings of the carotid plaque, for a cerebrovascular condition
No full text
Are car drivers holding a motorcycle licence less responsible for motorcycle-car crash occurrence? A non-parametric approach
No full textThe purpose of thiswork is to evaluate the effect of a specific motorcycle licence, held by car drivers, in responsibility for motorcycle–car crashes.
The data were provided by a multicentric case–control study (MAIDS) regarding the risk of crash and serious injuries of motorcyclists. A nonparametric
method, classification and regression tree (CART), was used to accomplish the objective, and then compared to standard unconditional
logistic regression. Drivers owning a motorcycle licence turned out to be less responsible for motorcycle–car crashes than drivers who do not have
one; both types of analysis are consistent with this result. It is reasonable to assume that car drivers who hold a motorcycle licence have acquired
more ability in riding and controlling two wheeled vehicles than drivers without a licence, and this may help them in predicting motorcycles
manoeuvres
Statistical Analysis of the clinical trial of a therapy for Alzheimer's disease. Univariate tests and logistic regression.
No full textA comparison of methods for the analysis of binomial clustered outcomes in behavioral research
No full textBACKGROUND: In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed.
METHOD: We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments.
RESULTS: Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference.
COMPARISON WITH EXISTING METHODS: GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data.
CONCLUSIONS: We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample size
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