ijsmr
Abstract : Use of Geometric Mean in Bioequivalence Trials
|
|
Abstract: Bioequivalence data often do not follow the normality assumption on the linear (original) scale, therefore in that situation, the use of the logarithmic transformation is recommended. In the bioequivalence analysis, confusion arises about the use of geometric mean ratio when the logarithmic transformation is recommended by the regulatory authorities. The purpose of this research paper is to clear this confusion. Different average bioequivalence criteria are also reviewed in this paper. Keywords: Geometric mean, Average bioequivalence, logarithmic transformation, Bioequivalence ranges, normal and log-normal distribution.Download Full Article |
Abstract : Increasing Early Awareness of Hazard of Children with ADHD’s ODD and Aggression by Structural Equation Modeling (SEM)
|
|
Abstract: Background:The hazard of children with Attention Deficit Hyperactivity Disorder (ADHD) occurring Oppositional Defiant Disorder (ODD) (shorten as ADHD’s ODD) and aggressionis not well understood. This study employs structural equation modeling (SEM) to operationalize aggression as joined symptoms on children with ADHD’s ODD by analyzing how aggression symptom transact the symptom severity of ADHD’s ODD. Methods:ADHD children and adolescents received clinical diagnosis and inattention (ADHD-I), hyperactivity/impulsivity (ADHD-H/I), and ODD subscale of Swanson, Nolan, and Pelham, version IV scale (SNAP-IV-C) and child behaviour check list (CBCL). SEM was applied to associate ADHD-I, ADHD-H/I, and ODD subscale toaggression.
Results:Significantly aggressive symptom on CBCL interact with symptom of ADHD, ODD on SNAP; the standardized direct effect of ADHD symptom by SNAP on behavior symptom by CBCL is 0.57 and the standardized total (direct and indirect) effect of ODD symptom on behavior symptom is 0.34. Children with ADHD’s ODD symptom share similar characteristic symptom as symptom of ADHD children with deficient emotional self-regulation (DESR). The aggression is highly correlated with ODD (0.607).
Conclusions:On ADHD symptom, the likelihood of symptom severity is predicted by the symptom of ADHD-I, ADHD-H/I, and ODD. On ODD symptom, ODD is associated with aggression and anxiety/depression symptom. There is a need to regard child with symptom of ADHD’s ODD and aggression as a child with heavy genetic loading and predictor of disruptive behavior disorder. Keywords: ADHD, ODD, Aggression, DESR, SEM. Download Full Article |
Abstract : A Robust Parameterization for Unbounded Covariates Within the Cox Proportional Hazards Model
|
|
Abstract: The Cox proportional hazards model is widely used in the analysis of medical data either for survival or time to a particular event. Factors and continuous covariates can be easily incorporated into the model and hazard ratios calculated. The model can however be distorted when extreme value observations occur within a continuous covariate and the hazard ratio can become extremely large. To overcome this, transformations of the covariate are often made, which can be simple, e.g. log, or more sophisticated such as the fitting of a fractional polynomial. This paper takes a different approach and makes a transformation based on the logistic function that has the property that the hazard ratio is bounded. The models are introduced and discussed. Model diagnostics based on Schoenfeld residuals and the influence function are established and then data from a pancreatic cancer trial are used to illustrate the model. Keywords: ESPAC 3 trial, hazard ratio, influence function, logistic function, Schoenfeld residuals.Download Full Article |
Abstract : Application of Survival Tree Based on Texture Features Obtained through MRI of Patients with Brain Metastases from Breast Cancer
|
|
Abstract: The information obtained by magnetic resonance imaging (MRI) is considered to possess great potential for providing the prognosis of cancer patients, although not been established. The goal of this study was to evaluate the covariates of the texture patterns obtained from MRI scans of patients with breast cancer brain metastases, which influence the survival time prognosis. The data of forty patients were analyzed using 29 covariates. Twenty-six covariates, which are focused on the texture patterns, were calculated from the gray-level co-occurrence matrix and wavelet coefficients obtained by transform of preoperative T1-weighted MRI scans. The remaining three covariates were age, Karnofsky Performance Scale, and the indicator of whether solitary or multiple metastases were present. These covariates are commonly used as the prognostic factors in medical research. The tree structure prognosis models were constructed by applying the survival tree method to these covariates. The obtained survival trees separated the patients into two or three groups between which there was a statistically significant distance. For the purpose of comparison, Cox regression analyses were performed to determine which covariates showed significant predictive values. All the covariates selected in the Cox analysis and survival tree method were texture features only. In particular, the energy of the gray-level co-occurrence matrix and wavelet coefficients showed a high performance in tree structure analysis. From these results, we conclude that the features obtained from simple medical images can be used to estimate the prognosis of brain metastases patients. Keywords: GLCM, wavelet transformation, recursive partitioning, binary tree, prognosis modeling, image analysis.Download Full Article |
