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Abstract: Introduction:Imputation of missing data and selection of appropriate risk function are of importance . Sometimes a variable with continuous nature will be offered to the regression model as an ordinal variable. Our aim is to investigate whether to offer the continuous form of the variable to the imputation phase and its ordinal from to the modeling phase, or whether to offer the ordinal version to both phases. Material and Methods:The outcome and main variable of interest was use of diet as a body change approach, and Body Mass Index (BMI). We randomly deleted 10%, 20%, and 40% of BMI values. In strategies 1 and 2, BMI was offered to the imputation phase as a continuous (BMIC) and ordinal variable (BMIO). Missing data were imputed using linear and polytomous regression respectively. In strategy 1, after imputation, BMIC was categorized (named BMICO) and offered to the modeling phase. In strategy 2, after imputation of BMIO values, this variable was offered to the logistic model (named BMIOO). We compared two strategies at Event Per Variables (EPV) of 75, 10, and 5. Result:At EPVs of 75 and 10 no remarkable difference was seen. However, at EPV of 5, strategy 2 was superior. At 20% and 40% missing rates, strategy 1 was 2.21 and 3.67 times more likely to produce Severe Relative Bias. At high missing rate, power was higher in strategy2 (90% versus 83%). Conclusions:When EPV is low and missing rate is high, categorizing of variable before imputation of missing data produces less SRB and leads to higher power. Keywords: Missing data, risk function, transformation, Multiple Imputation.Download Full Article |
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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 |
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Abstract: A number of classification techniques are prevailing in literature. Of them, one of the most important techniques is the Receiver Operating Characteristic (ROC) curve. A multivariate extension of this technique is proposed in the recent years. This technique helps in classifying the objects/individuals into one of the two classes by considering two or more markers. The most important measure of an ROC curve is the Area Under the Curve (AUC) and it explains the accuracy and discriminating ability of the test under study. There are two intrinsic measures of ROC namely sensitivity (Sn) and specificity (Sp). Further, two ROC curves can be compared by comparing their measures. The practical application of the proposed inferential procedures is explained with the help of two real datasets namely, Indian Liver Patient (ILP) Dataset and Intra Uterine Growth Restricted Fetal Doppler Study (IUGRFDS) dataset. These inferential procedures are developed based on the measures of multivariate ROC (MROC) curve proposed by Sameera G, R Vishnu Vardhan and KVS Sarma [1]. Keywords: Multivariate Receiver Operating Characteristic Curve, Area Under the Curve, testing procedures.Download Full Article |
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Abstract: Approximately 75% of adults over the age of 65 years are affected by two or more chronic medical conditions. We provide a conceptual justification for individualized absolute risk calculators for competing patient-centered outcomes (PCO) (i.e. outcomes deemed important by patients) and patient reported outcomes (PRO) (i.e. outcomes patients report instead of physiologic test results). The absolute risk of an outcome is the probability that a person receiving a given treatment will experience that outcome within a pre-defined interval of time, during which they are simultaneously at risk for other competing outcomes. This allows for determination of the likelihood of a given outcome with and without a treatment. We posit that there are heterogeneity of treatment effects among patients with multiple chronic conditions (MCC) largely depends on those coexisting conditions. We outline the development of an individualized absolute risk calculator for competing outcomes using propensity score methods that strengthen causal inference for specific treatments. Innovations include the key concept that any given outcome may or may not concur with any other outcome and that these competing outcomes do not necessarily preclude other outcomes. Patient characteristics and MCC will be the primary explanatory factors used in estimating the heterogeneity of treatment effects on PCO and PRO. This innovative method may have wide-spread application for determining individualized absolute risk calculations for competing outcomes. Knowing the probabilities of outcomes in absolute terms may help the burgeoning population of patients with MCC who face complex treatment decisions. Keywords: Multiple chronic disease, heterogeneity, propensity scores, longitudinal study, absolute risk, competing outcomes, decision tools.Download Full Article |
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Abstract: Background: Experimental study design, randomization, blinding, control, and the analysis of such data according to the intention-to-treat (ITT) principle are de-facto “gold standards” in pharmacotherapy research. While external treatment allocation under conditions of medical practice is conceptually reflected by in-study randomization in randomized controlled trials (RCTs) of therapeutic drugs, actual product use is based on self-selection in a consumer product setting. Discussion: With in-market product allocation being consumer-internal, there is no standard against which protocol adherence can be attuned, and the question arises, as to whether compliance-based analysis concepts reflect the real-world effects of consumer products. Summary: The lack of correspondence between RCTs and consumer market conditions becomes evident by the fact that even if, theoretically, all data would be available from all members of the real-world target population, it would be impossible to calculate either an ITT or a per-protocol effect. This renders the calculation of such estimates meaningless in consumer product research contexts. Keywords: Randomization, self-selection, intention-to-treat, actual use, consumer products.Download Full Article |