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Showing 2 results for Faradmal

A Biderafsh, M Karami, J Faradmal, J Poorolajal, N Esmailnasab,
Volume 10, Issue 3 (Vol 10, No.3 2014)
Abstract

  Background & Objectives : Considering the high prevalence of hypertension and its increasing trend in Iran as the most important known modifiable risk factor of stroke, this study was performed to determine the pattern of hypertension and the related population attributable fraction (PAF) of stroke in Hamadan Province.

  Methods: Blood pressure data of over-19-year-old population of Hamadan Province from 2005 to 2009 was extracted from the non-communicable diseases risk factors surveillance system . The point prevalence and 95% confidence interval (95% CI) of hypertension was reported in the study population according to sex and age groups. The trend of hypertension was depicted using a line plot. To calculate PAF, the data of the prevalence of hypertension in 2009 was used. Corresponding hazard ratio was obtained from the available literature.

  Results : The prevalence of hypertension was 9.4 (8.2 – 10.6) in 2005, 7.5 (5.9 – 9.3) in 2006, 14.2
(12.1 – 16.5) in 2007, 13.8 (11.7 – 16.1) in 2008, and 12.2 (10.3- 14.5) in 2009. The mean and standard deviation of systolic blood pressure was 120.88 (19.73) and 124.19 (19.24) in the participants in 2005 and 2009, respectively. The study results showed that 19.84% of the strokes in Hamadan Province were due to hypertension .

  Conclusion: The results of this study confirmed the considerable contribution of hypertension to stroke in Hamadan Province. Accordingly, policy makers are strongly recommended to consider controlling and preventive strategies for hypertension as a priority .


S Hamzeh, Ar Soltanian, J Faradmal,
Volume 12, Issue 4 (Vol.12, No.4 2017)
Abstract

Background and Objectives: When computing a confidence interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the confidence intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin transformation in terms of maintaining a constant nominal type I error.

Methods: Simulations were used to compare four methods of estimating a confidence interval, including the Clopper-Pearson, Wald, Wilson, and double ArcSic. The data were generated from the binomial and Poison distribution with parameters p, n and µ=np, 1000 were produced . Type I error of each method was calculated per simulation. The above methods were used to estimate confidence intervals in a meta-analysis study.

Results: The results of the simulation study showed that double ArcSin keep confidence interval at [0,1], but for some proportion has high type I error or low coverage probability. The Clopper–Pearson interval guarantees that the coverage probability is always equal to or above the nominal confidence level for any fixed p.

Conclusion: This study showed that confidence interval estimations the Clopper-Pearson than other methods of calculating the type I error fixed and smaller.



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