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Abstract : Graphical Investigation of Threshold Choice Effect on Odds Ratio Related to Prognostic Factors in Stroke Recovery
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Abstract: The aim of this study was to assess the effects of the arbitrary choices of threshold-values for dichotomizing not binary factors on the computation of odds ratio (OR) for the identification of prognostic factors, in particular of motor recovery after stroke. Data of a sample of 1000 patients with subacute stroke have been analysed. We considered as dependent variable the effectiveness of neurorehabilitation (i.e. the achieved level of independency in activities of daily living, measured using the Barthel Index, expressed in percentage of the maximum achievable improvement), and as independent variables age, time between stroke acute event and beginning of neurorehabilitation, gender, type of stroke (ischemic vs. haemorragic) and side of hemiparesis. We performed univariate analyses for computing OR with respect to different choices of threshold for dichotomizing age and time from stroke. In this analysis median value of effectiveness was used for dichotomizing subjects in good and poor responders. Then these analyses were repeated also varying the threshold-value of effectiveness. Finally multivariate analyses based on forward binary logistic regression were performed varying at the same time the thresholds of age and time from stroke. With respect to threshold choice, OR-values of age resulted stable, but those of time from stroke resulted more variable. Variability increased when also the threshold chosen for dichotomizing the independent variable was changed. Multivariate analyses showed that these choices could even make not statistically significant the effect of a binary prognostic factor such as gender. In conclusion, OR-values resulted affected by threshold choices. It can increase the difficulties in marking predictions of outcomes after stroke. In this study we reported a possible graphical evaluation of the variability of OR-values with respect of threshold choice, that can be helpful whenever threshold is arbitrary chosen. Keywords: Odds ratio, logistic regression, prognostic factor, probability, stroke.Download Full Article |
Abstract : Estimating the Population Standard Deviation with Confidence Interval: A Simulation Study under Skewed and Symmetric Conditions
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Abstract: This paper investigates the performance of ten methods for constructing a confidence interval estimator for the population standard deviation by a simulation study. Since a theoretical comparison among the interval estimators is not possible, a simulation study has been conducted to compare the performance of the selected interval estimators. Data were randomly generated from several distributions with a range of sample sizes. Various evaluation criterions are considered for performance comparison. Two health related data have been analyzed to illustrate the application of the proposed confidence intervals. Based on simulation results, some intervals with the best performance have been recommended for practitioners. Keywords: Bootstrapping, Coverage probability, Interval estimator, Kurtosis, Robustness, Scale estimator, Skewed Distribution.Download Full Article |
Abstract : Determinants of Wasting Among Under-Five Children in Ethiopia: (A Multilevel Logistic Regression Model Approach)
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Abstract: Child malnutrition in Ethiopia is one of the most serious public health problems and the highest in the world. Wasting refers to low weight-for-height and measures the body’s mass in relation to body length. The objective of this study was to identify determinants of wasting among under-five children in Ethiopia. The study used data collected in the Ethiopian Demographic and Health Survey in 2010/2011. A total of 9611 under-five age children were included in the present study. To analyze the data descriptive statistics and multilevel binary logistic regression techniques were employed. The descriptive statistics results indicate that about 11.7 % of under-five children in Ethiopia were wasted. The results of study indicated that the risk of wasting was highest among male children, small size at birth, children whose parents resided in rural areas, children’s of illiterate mothers, children whose mother’s body mass index was low, children from poor families and children who had diarrhea and fever two weeks before the date of the survey. The multilevel model also showed the existence of significant variations in the prevalence of wasting among the regions in Ethiopia. Keywords: Children, Malnutrition, Wasting, Multilevel, Logistic.Download Full Article |
Abstract : Imputation of Missing Data for a Continuous Variable with an Ordinal form of Risk Function: When to Apply the Transformation?
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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 |
