Sample size determination for 2-step studies with dichotomous response
TL;DR: In this article, two kinds of approximate sample size formulas, based on rate ratio, for comparison of the marginal and conditional probabilities in a correlated 2 × 2 table with structural zero were proposed.
About: This article is published in Journal of Statistical Planning and Inference.The article was published on 2006-03-01. It has received 9 citations till now. The article focuses on the topics: Sample size determination & Wald test.
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TL;DR: This article investigates the determination of sample sizes for disease prevalence studies with partially validated data and uses two approaches to find sample sizes that can achieve a pre-specified power of a statistical test at a chosen significance level.
Abstract: SummaryDisease prevalence is an important topic in medical research, and its study is based on data that are obtained by classifying subjects according to whether a disease has been contracted. Cla...
10 citations
Cites background from "Sample size determination for 2-ste..."
...The approximate sample size N that is required to achieve the desired power of 1 at level with 1⁄4 1 can be obtained by solving equation (19) or equation (20)....
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TL;DR: In this paper, approximate sample size formulas for testing difference between two proportions for bilateral studies with binary outcomes are derived to achieve a prespecified power of a statistical test at a prechosen significance level.
Abstract: In this article, we consider approximate sample size formulas for testing difference between two proportions for bilateral studies with binary outcomes. Sample size formulas are derived to achieve a prespecified power of a statistical test at a prechosen significance level. Four statistical tests are considered. Simulation studies are conducted to investigate the accuracy of various formulas. In general, the sample size formula for Rosner's statistic based on the dependence assumption is highly recommended in the sense that its actual power is satisfactorily close to the desired power level. An example from an otolaryngological study is used to demonstrate the proposed methodologies.
10 citations
TL;DR: Buehler limits based on the signed root likelihood ratio statistic are found to have the best performance and recommended for practical use.
Abstract: This paper examines exact one-sided confidence limits for the risk ratio in a 2 x 2 table with structural zero. Starting with four approximate lower and upper limits, we adjust each using the algorithm of Buehler (1957) to arrive at lower (upper) limits that have exact coverage properties and are as large (small) as possible subject to coverage, as well as an ordering, constraint. Different Buehler limits are compared by their mean size, since all are exact in their coverage. Buehler limits based on the signed root likelihood ratio statistic are found to have the best performance and recommended for practical use.
8 citations
TL;DR: This paper develops and evaluates the large sample confidence intervals of RR, and proposes a confidence interval based on Rao's score test for rate ratio in a 2x2 table with structural zero, an application in assessing false-negative rate ratio when combining two diagnostic tests.
8 citations
TL;DR: In this article, the exact posterior distribution of the risk difference is derived under the Dirichlet prior distribution, and a tail-based interval is used to construct the Bayesian confidence interval.
Abstract: This article studies the construction of a Bayesian confidence interval for risk difference in a 2×2 table with structural zero. The exact posterior distribution of the risk difference is derived under the Dirichlet prior distribution, and a tail-based interval is used to construct the Bayesian confidence interval. The frequentist performance of the tail-based interval is investigated and compared with the score-based interval by simulation. Our results show that the tail-based interval at Jeffreys prior performs as well as or better than the score-based confidence interval.
6 citations
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TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Abstract: categorical data analysis , categorical data analysis , کتابخانه مرکزی دانشگاه علوم پزشکی تهران
10,964 citations
TL;DR: Criteria appropriate to the evaluation of various proposed methods for interval estimate methods for proportions include: closeness of the achieved coverage probability to its nominal value; whether intervals are located too close to or too distant from the middle of the scale; expected interval width; avoidance of aberrations such as limits outside [0,1] or zero width intervals; and ease of use.
Abstract: Simple interval estimate methods for proportions exhibit poor coverage and can produce evidently inappropriate intervals. Criteria appropriate to the evaluation of various proposed methods include: closeness of the achieved coverage probability to its nominal value; whether intervals are located too close to or too distant from the middle of the scale; expected interval width; avoidance of aberrations such as limits outside [0,1] or zero width intervals; and ease of use, whether by tables, software or formulae. Seven methods for the single proportion are evaluated on 96,000 parameter space points. Intervals based on tail areas and the simpler score methods are recommended for use. In each case, methods are available that aim to align either the minimum or the mean coverage with the nominal 1 -alpha.
3,825 citations
TL;DR: Two new approaches which also avoid aberrations are developed and evaluated, and a tail area profile likelihood based method produces the best coverage properties, but is difficult to calculate for large denominators.
Abstract: 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.
1,634 citations
TL;DR: This work considers the problem of equivalence test with a relative risk endpoint in matched-pair studies with binary outcomes, and develops several score and Wald-type statistics for testing a hypothesis of non-unity relative risk.
Abstract: Matched-pair design is often used in clinical trials to increase the efficiency of treatment comparison. We consider the problem of equivalence test with a relative risk endpoint in matched-pair studies with binary outcomes, and develop several score and Wald-type statistics for testing a hypothesis of non-unity relative risk. Examples from an assessment of HIV screening test and a cross-over clinical trial of soft contact lenses are used to illustrate the proposed methods. Through simulations we compare the empirical performance of these tests with the test proposed by Lachenbruch and Lynch. We show that a score test based on a reparameterized multinomial model by Tango performs best in the sense that the test satisfactorily controls the type I error rate and its empirical type I error rates are generally much closer to the prespecified nominal significance level than those of the other tests.
92 citations
TL;DR: In this paper, the authors compare sample size determination with use of the length of the confidence interval with that obtained by controlling the power of a statistical test at an appropriate alternative, even those statisticians who recommend the use of confidence intervals for inference.
Abstract: Although estimation and confidence intervals have become popular alternatives to hypothesis testing and p-values, statisticians usually determine sample sizes for randomized clinical trials by controlling the power of a statistical test at an appropriate alternative, even those statisticians who recommend the use of confidence intervals for inference. There is merit in achieving consistency in the techniques for data analysis and sample size determination. To that end, this paper compares sample size determination with use of the length of the confidence interval with that obtained by control of power.
57 citations