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Abstract: Child mortality is a factor that is associated with the well-being of a population and it is taken as an indicator of health development and socioeconomic status. According to the 2011 UN report during the last 10 years, the death rate for children under five has decreased by 35% worldwide. UNICEF in 2008 reported that Ethiopia has reduced under-five mortality by 40 percent over the past 15 years. From the EDHS 2011 report child mortality rate in Ethiopia was reduced from 50/1000 deaths in 2005 to 31/1000 deaths in 2011. The Ethiopian Demographic and Health Survey data are used for the study. In this paper we have attempted to find out the impact of socioeconomic, demographic and environmental factors in the context of under five mortality. In this attempt we first analyzed our data using Kaplan-Meier non-parametric method of estimation of survival function and also using lifetable. We have also used Log-Rank test to compare different survival functions and found that sex, type of birth, religion, mothers’ education, birth order, maternity age, source of drinking water and region have statistically significant difference in the under five survival time. We have also used Cox proportional hazard model to identify the covariates which influence the under five mortality. But we found that our data do not fulfill the proportionality assumption of Cox proportional model in case of infant and child mortality. Then we applied stratified Cox proportional model to our data to find out the potential covariates which influence under five mortality and found birth order, mothers’ education level, sex, type of birth and the interaction of birth order and sex as vital factors for the deaths occurring under the age of five. The Cox proportional hazard models which were used separately for each stratum also identified mothers’ educational level, sex, type of birth, and the interaction of sex and water supply as the risk factors for the death of infants. Whereas for child stratum; type of birth, mothers’ education, sex and the interaction of water supply and sex were the risk factors associated with the death of children. Keywords: Under five mortality, maternal, socioeconomic and environmental factor.Download Full Article |
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Abstract: Objective: To demonstrate the use of robust Cox regression in estimating adjusted relative risks (and confidence intervals) when all participants with an identical follow-up time and when a common outcome is investigated. Methods: In this paper, we propose an alternative statistical method, robust Cox regression, to estimate adjusted relative risks in prospective studies. We use simulated cohort data to examine the suitability of robust Cox regression. Results: Robust Cox regression provides estimates that are equivalent to those of modified Poisson regression: regression coefficients, relative risks, 95% confidence intervals, P values. It also yields reasonable probabilities (bounded by 0 and 1). Unlike modified Poisson regression, robust Cox regression allows for four automatic variable selection methods, it directly computes adjusted relative risks for continuous variables, and is able to incorporate time-dependent covariates. Conclusion: Given the popularity of Cox regression in the medical and epidemiological literature, we believe that robust Cox regression may gain wider acceptance and application in the future. We recommend robust Cox regression as an alternative analytical tool to modified Poisson regression. In this study we demonstrated its utility to estimate adjusted relative risks for common outcomes in prospective studies with two or three waves of data collection (spaced similarly). Keywords: Robust Cox regression, Modified Poisson regression, Logistic regression, Relative risk, Odds ratio.Download Full Article |
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Abstract: Principal component analysis (PCA) has been successfully applied to gait data; however, interpretation of the components is challenging. An alternative is to use a graphical display called biplot that gives insights into relationships and trends of data sets. Our goal was to demonstrate the sensitivity of gait variables to aging in elderly women with PCA-biplot. One hundred fifty-one elderly females (71.6±5.0 yrs), 152 adults (44.7±5.4 yrs) and 150 young (21.7±4.1 yrs) participated in the study. Gait spatial and temporal parameters were collected using a computerized carpet. PCA-biplot, discriminant analysis and MANOVA were used in the analysis. PCA-biplot revealed that elderly females walked with lower velocity, shorter step length, reduced swing time, higher cadence, and increased double support time compared to the other two groups. The greatest distances between the groups were along the variable step length with the elderly group showing a decrease of 8.4 cm in relation to the younger group. The discriminant function confirmed the importance of principal component 2 for group separation. Because principal component 2 was heavily weighted by step length and swing time, it represents a measure of stability. As women age they seek a more stable gait by decreasing step length, swing time, and velocity. PCA-biplot highlighted the importance of the variable step length in distinguishing between women of different age groups. It is well-known that as we age we seek a more stable gait. The PCA-biplot emphasized that premise and gave further important insights into relationships and trends of this complex data set. Keywords: Gait, Principal Components Analysis, Biplot, Elderly, Balance, Step Length.Download Full Article |
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Abstract: The basket trial is a recent development in the clinical trial practice. It conducts the test of the same treatment on several different related diseases in a single trial, and has the advantage of reduced cost and enhanced efficiency. A natural question is how to assess the performance of the group sequential basket trial against the classical group sequential trial? To our knowledge, a formal assessment hasn’t been seen in the literature, and is the goal of this study. Specifically, we use the receiver operating characteristic curve to assess the performance of the mentioned two trials. We considered two cases, parametric and nonparametric settings. The former is efficient when the parametric model is correctly specified, but can bemis-leading if the model is incorrect; the latter is less efficient but is robust in that it cannot be wrong no matter what the true data generating model is. Simulation studies are conducted to evaluate the experiments, and it suggests that the group sequential basket trial generally outperforms the group sequential trial in either the parametric and nonparametric cases, and that the nonparametric method gives more accurate evaluation than the parametric one for moderate to large sample sizes. Keywords: Basket trial, group sequential clinical trial, nonparametric ROC curve, parametric ROC curve, phase II clinical trial. |
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Abstract: Background: Geriatric studies often miss data of frail participants. The aim of this paper is to explore which missing data methodologies have entered current practice and to discuss the potential impact of ignoring the issue. Methods: A Sample of 103 articles was drawn from key cohort studies: Health ABC, InCHIANTI, LASA, BLSA, EPESE, and KLoSHA. The studies wereclassified according to missing data methodologies used. Results: Seventy-seven percent described the selected analysis data set and only 28% used a method of handling all available observations per case. Missing data dedicated methods were rare (< 10%), applying single or multiple imputations for baseline variables. Studies with longer follow-up periods more often employed longitudinal analysis methodologies. Conclusions: Despite the recognition that missing data is a major problem in studies of older persons, few published studies account for missing data using limited methodologies; this could affect the validity of study conclusions. We propose researchers apply Joint Modeling of longitudinal and time-to-event data, using shared-parameter model. Keywords: Missing data, geriatric cohort studies, methodologies review, longitudinal analysis.Download Full Article |
