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198
Interval estimation for the difference between independent proportions: comparison of eleven methods
 Statistics in Medicine
, 1998
"... Several existing unconditional methods for setting confidence intervals for the difference between binomial proportions are evaluated. Computationally simpler methods are prone to a variety of aberrations and poor coverage properties. The closely interrelated methods of Mee and Miettinen and Nurmine ..."
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Cited by 94 (3 self)
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Several existing unconditional methods for setting confidence intervals for the difference between binomial proportions are evaluated. Computationally simpler methods are prone to a variety of aberrations and poor coverage properties. The closely interrelated methods of Mee and Miettinen and Nurminen perform well but require a computer program. Two new approaches which also avoid aberrations are developed and evaluated. A tail area profile likelihood based method produces the best coverage properties, but is difficult to calculate for large denominators. A method combining Wilson score intervals for the two proportions to be compared also performs well, and is readily implemented irrespective of sample size. � 1998 John Wiley & Sons, Ltd. 1.
On smallsample confidence intervals for parameters in discrete distributions
 Biometrics
, 2001
"... you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact inform ..."
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Cited by 25 (1 self)
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you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at.
Improved confidence intervals for the difference between binomial proportions based on paired data
 Statistics in Medicine 17
, 1998
"... Existing methods for setting confidence intervals for the difference � between binomial proportions based on paired data perform inadequately. The asymptotic method can produce limits outside the range of validity. The ‘exact ’ conditional method can yield an interval which is effectively only ones ..."
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Cited by 19 (0 self)
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Existing methods for setting confidence intervals for the difference � between binomial proportions based on paired data perform inadequately. The asymptotic method can produce limits outside the range of validity. The ‘exact ’ conditional method can yield an interval which is effectively only onesided. Both these methods also have poor coverage properties. Better methods are described, based on the profile likelihood obtained by conditionally maximizing the proportion of discordant pairs. A refinement (methods 5 and 6) which aligns 1! � with an aggregate of tail areas produces appropriate coverage properties. A computationally simpler method based on the score interval for the single proportion also performs well (method 10). � 1998 John Wiley & Sons, Ltd. 1.
Binomial Distribution Sample Confidence Intervals Estimation 10. Relative Risk Reduction and RRRlike Expressions
"... In trial, when the results are reported as dichotomies variables, the most important measure of effect are represented by the relative risk reduction, absolute risk reduction and number needed to treat, providing the basis for clinicians to balance the benefits and harms of therapy for their patient ..."
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Cited by 10 (3 self)
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In trial, when the results are reported as dichotomies variables, the most important measure of effect are represented by the relative risk reduction, absolute risk reduction and number needed to treat, providing the basis for clinicians to balance the benefits and harms of therapy for their patients. The relative risk reduction is a very useful parameter in assessment of a treatment effect if it is accompanied by confidence intervals. The only method used in medical article for computing the confidence intervals for relative risk reduction is the asymptotic method. The aim the research was to propose some new methods of computing confidence intervals for relative risk reduction and relative risk reduction like parameters and to compare these methods with the asymptotic one in order to assess their performance. In order to estimate the confidence intervals for relative risk reduction we proposed based on the literature definitions and on our experiences in confidence intervals five methods called here ARPWald, ARPAC, ARPWaldC1, ARPWaldC2, and ARPWaldC3. The criterions of assessment were represented by the upper and lower boundaries, the average of experimental errors and standard deviations, and the deviation relative to imposed significance level α
Confidence intervals for computational effort comparisons
 Genetic Programming. Proceedings of the 10th European Conference, EuroGP 2007
, 2007
"... Abstract. When researchers make alterations to the genetic programming algorithm they almost invariably wish to measure the change in performance of the evolutionary system. No one specific measure is standard, but Koza’s computational effort statistic is frequently used [8]. In this paper the use o ..."
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Abstract. When researchers make alterations to the genetic programming algorithm they almost invariably wish to measure the change in performance of the evolutionary system. No one specific measure is standard, but Koza’s computational effort statistic is frequently used [8]. In this paper the use of Koza’s statistic is discussed and a study is made of three methods that produce confidence intervals for the statistic. It is found that an approximate 95 % confidence interval can be easily produced. 1
The reliability of confidence intervals for computational effort comparisons
 in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
, 2007
"... This paper analyses the reliability of confidence intervals for Koza’s computational effort statistic. First, we conclude that dependence between the observed minimum generation and the observed cumulative probability of success leads to the production of more reliable confidence intervals for our p ..."
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This paper analyses the reliability of confidence intervals for Koza’s computational effort statistic. First, we conclude that dependence between the observed minimum generation and the observed cumulative probability of success leads to the production of more reliable confidence intervals for our preferred method. Second, we show that confidence intervals from 80 % to 95 % have appropriate levels of performance. Third, simulated data is used to consider the effect of large minimum generations and the confidence intervals are again found to be reliable. Finally, results from four large datasets collected from real genetic programming experiments are used to provide even more empirical evidence that the method for producing confidence intervals is reliable.
Reducing conservatism of exact smallsample methods of inference for discrete data
 TH SYMPOSIUM OF THE IASC, ROME 28 AUGUST  1
, 2006
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Adaptive choice of the number of bootstrap samples in largescale multiple testing. Statist
"... Abstract It is a common practice to use resampling methods such as the bootstrap for calculating the pvalue for each test when performing large scale multiple testing. The precision of the bootstrap pvalues and that of the false discovery rate (FDR) relies on the number of bootstraps used for tes ..."
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Cited by 6 (1 self)
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Abstract It is a common practice to use resampling methods such as the bootstrap for calculating the pvalue for each test when performing large scale multiple testing. The precision of the bootstrap pvalues and that of the false discovery rate (FDR) relies on the number of bootstraps used for testing each hypothesis. Clearly, the larger the number of bootstraps the better the precision. However, the required number of bootstraps can be computationally burdensome, and it multiplies the number of tests to be performed. Further adding to the computational challenge is that in some applications the calculation of the test statistic itself may require considerable computation time. As technology improves one can expect the dimension of the problem to increase as well. For instance, during the early days of microarray technology, the number of probes on a cDNA chip was less than 10,000. Now the Affymetrix chips come with over 50,000 probes per chip. Motivated by this important need, we developed a simple adaptive bootstrap methodology for large scale multiple testing, which reduces the total number of bootstrap calculations while ensuring the control of the FDR. The proposed algorithm results in a substantial reduction in the number of bootstrap samples. Based on a simulation study we found that, relative to the number of bootstraps required for the BenjaminiHochberg (BH) procedure, the standard FDR methodology which was the proposed methodology achieved a very substantial reduction in the number of bootstraps. In some cases the new algorithm required as little as 1/6 th the number of bootstraps as the conventional BH procedure. Thus, if the conventional BH procedure used 1,000 bootstraps, then the proposed method required only 160 bootstraps. This methodology has been implemented for timecourse/doseresponse data in our software, ORIOGEN, which is available from the authors upon request.
Exact probabilities and confidence limits for binomial samples: Applied to the difference between two proportions
 TheScientificWorldJOURNAL 2010
"... An exact probabilities method is proposed for computing the confidence limits of medical binomial parameters obtained based on the 2×2 contingency table. The developed algorithm was described and assessed for the difference between two binomial proportions (a bidimensional parameter). The behavior o ..."
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An exact probabilities method is proposed for computing the confidence limits of medical binomial parameters obtained based on the 2×2 contingency table. The developed algorithm was described and assessed for the difference between two binomial proportions (a bidimensional parameter). The behavior of the proposed method was analyzed and compared to four previously defined methods: Wald and Wilson, with and without continuity corrections. The exact probabilities method proved to be monotonic in computing the confidence limits. The experimental errors of the exact probabilities method applied to the difference between two proportions has never exceeded the imposed significance level of 5%.
The Welfare Effects
 of Restrictions on U.S. Trade.” BPEA
, 1972
"... of duplicate and screening isolates on surveillance of community and hospital antibiotic resistance ..."
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of duplicate and screening isolates on surveillance of community and hospital antibiotic resistance