Bayesian Analysis for a Single 2×2 Table

Hashemi, Lobat; Nandram, Balgobin; Goldberg, Robert J.

doi:10.1002/(sici)1097-0258(19970630)16:12<1311::aid-sim568>3.0.co;2-3

Cited by 44 publications

(35 citation statements)

References 2 publications

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“…has the details. Clearly, the hypothesis of independence (between gender and receipt of lidocaine therapy) is synonymous to that of equality of the success probabilities of two binomial distributions corresponding to males and females respectively (receiving therapy being success and vice versa) (Hashemi et al, 1997;.…”

Section: Multinomial Distributionmentioning

confidence: 99%

Tests of Independence for a $2 \times 2$ Contingency Table with Random Margins

Yu¹,

Bhadra²,

Nandram³

2017

IJSP

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Fisher's exact test is commonly used for testing the hypothesis of independence between the row and column variables in a r × c contingency table. It is a "small-sample" test since it is used when the sample size is not large enough for the Pearsonian chi-square test to be valid. Fisher's exact test conditions on both margins of a 2 × 2 table leading to a hypergeometric distribution of the cell counts under independence. Moreover, it is conservative in the sense that its actual significance level falls short of the nominal level. In this paper, we modify Fisher's exact test by lifting the restriction of fixed margins and allow the margins to be random. In doing so, we propose two new tests -a likelihood ratio test in a frequentist framework and a Bayes factor test in a Bayesian framework, both of which are based on a new multinomial distributional framework. We apply the three tests on data from the Worcester Heart Attack study and compare their power functions in assessing gender difference in the therapeutic management of patients with acute myocardial infarction (AMI).

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Section: Multinomial Distributionmentioning

confidence: 99%

Tests of Independence for a $2 \times 2$ Contingency Table with Random Margins

Yu¹,

Bhadra²,

Nandram³

2017

IJSP

Self Cite

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“…A HPD interval is a region that contains parameters with highest posterior density values (Hashemi et al, 1997).…”

Section: (X)mentioning

confidence: 99%

Bayesian Credible Intervals for Maize Grain Yields of the Maintenance Varieties Evaluated in Sudan

Omer

Abdalla

Alzain³

et al. 2017

Int J Appl Sci Biotechnol

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Improved of maintenance crop varieties developed at agricultural research corporation, Wad median, Sudan is intended for seed foundation which is recently established. This study was undertaken to establish statistical investigation using Bayesian estimation for credible interval or posterior interval as a Bayesian strategy for a maintenance variety. Data on grain yield (kg/ha) on a maize crop variety were used. Bayesian posterior information can be annoying to investigate but are important in maintenance varieties that foundational claims are used to make general recommendations for practice. Half normal informative priors set were used. The heritability of yield (varieties)) was (h = 0.75). Predicted posterior means of varieties were shown with a Bayesian interval for scientific inference in the maintenance maize grain yield. Bayesian approach is useful for reducing uncertainty on decisions based on economic evaluation of new maize varieties in Sudan, the use of credible intervals for grain yield allow for early decisions.

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“…Conventional territory was revisited from a Bayesian perspective, including sample size for experiments [274], analysis of 2 × 2 tables using relative risk, odds ratio and attributable risk [275] and the proportional hazards model [276]. Work on diagnosis using independent Bayes compared it with the more recent Classification and Regression Tree methodology [277].…”

Section: Statistics In Medicinementioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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Bayesian Analysis for a Single 2×2 Table

Cited by 44 publications

References 2 publications

Tests of Independence for a $2 \times 2$ Contingency Table with Random Margins

Tests of Independence for a $2 \times 2$ Contingency Table with Random Margins

Bayesian Credible Intervals for Maize Grain Yields of the Maintenance Varieties Evaluated in Sudan

Bayesian statistics in medicine: a 25 year review

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