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Abstract : Avoiding Inferential Errors in Public Health Research: The Statistical Modelling of Physical Activity Behavior
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Abstract: Background: A review of the health behavior literature on the statistical modeling of days of physical activity (PA) indicates that in many instances linear regression models have been used. It is inappropriate statistically to model a count dependent variable such as days of physical activity with Ordinary Least Squares (OLS). Many count variables have skewed distributions, and, also, have a preponderance of zeroes. Count variables should not be treated as continuous and unbounded. If OLS is used, estimations of the regression will frequently turn out to be inefficient, inconsistent and biased, and such outcomes could well have incorrect impacts on health programs and policies. Methods: We considered three statistical methods for modelling the distribution of days of PA data for respondents in the 2013 Health Information Trends Survey (HINTS). The three regression models analyzed were: Ordinary Least Squares (OLS), Negative Binomial (NBRM), and Zero-inflated Negative Binomial (ZINB). We used the exact same predictor variables in the three models. Our results illustrate the differences in the results. Results: Our analyses of the PA data demonstrated that the ZINB model fits the observed PA data better than either the OLS or the NBRM models. The coefficients and standard errors differed in the zero-inflated count models from the other models. For instance, the ZINB coefficient for the association between income and PA behavior was not statistically significant (p>0.05), whereas in the NBRM and in the OLS models, it was statistically significant (p<0.05). Conclusions: The inappropriate use of regression models could well lead to wrong statistical inferences. Our analyses of the number of days of moderate PA demonstrated that the ZINB count model fits the observed PA data much better than the OLS model and the NBRM. Keywords: Count Regression, Inference error, Measurement, physical activity, Health behavior.Download Full Article |
Abstract : LQAS in Health Monitoring – Insights from a Bayesian Perspective
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Abstract: Lot Quality Assurance Sampling (LQAS) is strongly advocated for use in monitoring the health status of populations, largely in the developing world. It is advocated both for the monitoring of small areas as well as for making global assessments of the health status of a larger region. This paper contrasts the interpretation offered by LQAS methods to that offered by Bayesian hierarchical models. It considers applications to previously reported local area data and presents a reanalysis of published data on vaccine coverage in Peru as well as HTLV-1 prevalence in Benin. The desirability of using Bayesian methods in the field may be challenged; nevertheless this work amplifies previously expressed concerns about the way the LQAS method can be used. It raises questions about the ability of the LQAS approach to make, sufficiently often, the correct decisions in order to be useful in monitoring health programmes at the local level. Keywords: Cluster Sampling, Bayesian Hierarchical Model, Overdisperson, Hypergeometric distribution, Classification.Download Full Article |
Abstract : Examining the Probabilities of Type I Error for Unadjusted All Pairwise Comparisons and Bonferroni Adjustment Approaches in Hypothesis Testing for Proportions
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Abstract: The aim of this study is to examine the association among the probabilities of Type I errorobtained by Unadjusted All Pairwise Comparisons (UAPC) and Bonferroni-adjustment approaches, the sample size and the frequency of occurrence of an event (prevalence, proportion) in hypothesis testing of difference among the proportions in studies. In the simulation experiment planned for this purpose, 4 groups were formed and the proportions in each group were chosen between 0.10 and 0.90 so that they will be equal at each experiment. Furthermore, the sample sizes were chosen from 20 to 1000. In accordance with these scenarios, the probabilities of Type I error were calculated by both of approaches. In each approach, a significant S-curve relationship was found between the probability of Type I error and sample size. However, a significant quadratic relationship was found between the probabilities of Type I error and the proportions in each group. Nonlinear functional relations were put forward in order to estimate the observed Type I errorrates obtained by the two different approaches where sample size and the proportion in each group are known. Furthermore, it was founded that Bonferroni-adjustment approach cannot always protect Type I error level. It was observed that the probability of Type I error estimated by the functional relation on Type I error rate for UAPC approach is lower than the values calculated using the formula in the literature. Keywords: Proportion comparison, type I error, bonferroniadjustment, unadjusted all pairwise comparisons.Download Full Article |
Abstract : Predictive Modelling of Patient Reported Radiotherapy-Related Toxicity by the Application of Symptom Clustering and Autoregression
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Abstract: Patient reported outcome measures (PROMs) are increasingly being used in research to explore experiences of cancer survivors. Techniques to predict symptoms, with the aim of providing triage care, rely on the ability to analyse trends in symptoms or quality of life and at present are limited. The secondary analysis in this study uses a statistical method involving the application of autoregression (AR) to PROMs in order to predict symptom intensity following radiotherapy, and to explore its feasibility as an analytical tool. The technique is demonstrated using an existing dataset of 94 prostate cancer patients who completed a validated battery of PROMs over time. In addition the relationship between symptoms was investigated and symptom clusters were identified to determine their value in assisting predictive modeling. Three symptom clusters, namely urinary, gastrointestinal and emotional were identified. The study indicates that incorporating symptom clustering into predictive modeling helps to identify the most informative predictor variables. The analysis also showed that the degree of rise of symptom intensity during radiotherapy has the ability to predict later radiotherapy-related symptoms. The method was most successful for the prediction of urinary and gastrointestinal symptoms. Quantitative or qualitative prediction was possible on different symptoms. The application of this technique to predict radiotherapy outcomes could lead to increased use of PROMs within clinical practice. This in turn would contribute to improvements in both patient care after radiotherapy and also strategies to prevent side effects. In order to further evaluate the predictive ability of the approach, the analysis of a larger dataset with a longer follow up was identified as the next step. Keywords: Predictive Modeling, Patient Reported Outcome Measures, Autoregression, Radiotherapy-Related Side Effects, Longitudinal Study.Download Full Article |
