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Editor’s Choice : Modified Kaplan-Meier Estimator Based on Competing Risks for Heavy Censoring Data
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Abstract: Most follow-up studies are conducted to determine the survival rates of subjects affected by a specific risk. These subjects are also exposed to other risks. Every subject in a medical follow-up is exposed not only to the risk of dying, but also to the risk of being censored. In case of heavy censoring, the Kaplan-Meier estimates are biased and overestimate the survival distribution. A new methodology based on competing risks is proposed to estimate the survival function by using net and crude probabilities. These estimates reduce the bias and overestimation of the survival distribution noted in Kaplan-Meier estimators. In this study, the method of modified Kaplan-Meier (MKM) is compared with the Kaplan-Meier (KM), Huang’s method and also the two other methods namely Weighted Kaplan-Meier (WKM) and Modified Weighted Kaplan-Meier (MWKM). Either of the weighted methods depends heavily on the event times and censoring distributions. Due to this fact, the weighted methods can have misleading results when the censoring patterns are different in the individual samples. The results showed that the MKM estimator considers not only the problem of heavy censoring but also the problem of weighted methods and competing risks in complicated data. In this study “Stanford Heart Transplant Data” was used to investigate the effectiveness of the proposed methods. Keywords: Competing risks, Kaplan-Meier estimator, Heavy Censoring, Net and Crude probabilities.Download Full Article |
Editor’s Choice : A Dynamical Study of Risk Factors in Intracerebral Hemorrhage using Multivariate Approach
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Abstract: The purpose of this study is to investigate the effects of clinical covariates to the outcome of Intracerebral Hemorrhage (ICH) patients in terms of best fitted and excellent discriminate model of binary response variable. Clinical data of 985 patients with ICH have collected using the International classification of diseases, Ninth revision codes. The diagnosis of ICH was confirmed by neuro-imaging in all patients. Univariate analysis revealed that out of 88 covariates 46 were found to be significant (p<0.05). The multivariable analysis using multiple logistic regressions, exhibited a significant negative relationship between ICH and hypertension. The improvement among ICH patients having hypertension was 0.5 (p=0.001, ARR=0.5, 95% C.I. 0.3 – 0.8). The improvement among ICH patients using antihypertensive medicine was 1.3 (p = 0.016, ARR=1.3, 95% C.I. 1.1 – 1.5). Thus present study showed that ICH has strong relationship with use of antihypertensive medicine. The improvement of patients who were using antihypertensive medicine at the time of discharge was 3.0 times (p < 0.0001, ARR=3.0, 95% C.I. 2.7 – 3.2) as compared to those who did not use antihypertensive medicine. The change in ARR from 1.3 to 3.0 times shows that the use of antihypertensive medicine and ICH outcome variable are positively associated. The change in ARR of hypertensive range of SBP also indicates that the blood pressure range and ICH outcome variable are negatively associated. The neurological symptomatology, slurred speech and double vision are important factors of proposed statistical models. Moreover, a clear decrease was found in mental status from normal to coma in applicable model. Surgery is an important part of recovery, and estimated that the improvement among the ICH patients, who were treated with surgery, was 1.4 times with significant p-value in best fitted models. The complication of pneumonia during treatment of ICH subjects has highly significant negative association with outcome variable. Present Model has 0.892 area under the curve with sensitivity (0.852), specificity (0.793) and p-value (0.204). This indicates that the model gives the impression to fit quite well for predictive performance of the ICH outcome variable and the model is excellent model. Keywords: Intracerebral Hemorrhage, clinical covariates, multivariable analysis, logistic regression, discriminate model, sensitivity and specificity.Download Full Article |
Editor’s Choice : Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients
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Abstract: Background: Boarding, or patients waiting to be admitted to hospital, has been shown as a significant contributing factor at overcrowding in emergency departments (ED). Predicting hospital admission at triage has been proposed as having the potential to help alleviate ED overcrowding. The objective of this paper is to develop and validate a model to predict hospital admission at triage to help alleviate ED overcrowding. Methods: Administrative records between April 1, 2010 and November 31, 2010 in an adult ED were used to derive and validate two prediction models, one based on Coxian phase type distribution (the PH model), the other based on logistic regression. Separate data sets were used for model development (data between April 1, 2010 and July 31, 2010) and validation (data between August 1, 2010 and November 31, 2010). Results: There were a total of 14,542 ED visits and 2,602 (17.89%) hospital admissions in the derivation cohort. In both models, acuity levels, model of arrival, and main reason of the visit are strong predictors of hospital admission; number of patients at the ED, as well as gender, are also predictors, albeit with ORs closer to 1. Patient age and timing of visits are not strong predictors. The PH model has an AUC of 0.89 compared with AUC of 0.83 for logistic regression model; with a cut- off value of 0.50, the PH model correctly predicted 86.3% of visits, compared to 84.4% for the logistic regression model. Results of the validation cohort were similar: the PH model has an AUC of 0.88, compared to AUC of 0.83 for the logistic model. Conclusions: PH and logistic models can be used to provide reasonably accurate prediction of hospital admission for ED patients, with the PH model offering more accurate predictions. Keywords: Hospital admission, Emergency department, Wait times, Overcrowding, Coxian phase type distribution.Download Full Article |
Editor’s Choice : A Nonparametric Bayesian Approach to Estimating Malaria Prophylactic Effect After Two Treatments
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Abstract: Two treatment regimens for malaria are compared in their abilities to cure and combat reinfection. Bayesian analysis techniques are used to compare two typical treatment therapies for uncomplicated malaria in children under five years, not only in their power to resist recrudescence, but also how long they can postpone recrudescence or reinfection in case of failure. We present a new way of analysing this type of data using Markov Chain Monte Carlo techniques. This is done using data from clinical trials at two different centres. The results which give the full posterior distributions show that artemisinin-based combination therapy is more efficacious than sulfadoxine-pyrimethamine. It both reduced the risk of recrudescence and delayed the time until recrudescence. Keywords: Bayesian clinical trial, conditional survival posterior, drug resistance, efficacy, recurrence time, uncomplicated malaria, sulfadoxine-pyrimethamine.Download Full Article |
