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Articles 1 - 5 of 5
Full-Text Articles in Medical Biomathematics and Biometrics
Multinomial Logistic Regression: An Application To Estimating Performance Of A Multiple Screening Test For Bowel Cancer When Negatives Are Unverified., Chris Lloyd, Don Frommer
Multinomial Logistic Regression: An Application To Estimating Performance Of A Multiple Screening Test For Bowel Cancer When Negatives Are Unverified., Chris Lloyd, Don Frommer
Chris J. Lloyd
This paper describes a method of estimating the performance of a multiple screening test where those who test negative do not have their true disease status determined. The methodology is motivated by a dataset on 49,927 subjects who were given K=6 binary tests for bowel cancer. A complicating factor is that individuals may have polyps present in the bowel, a condition that the screening test is not designed to detect but which may be worth diagnosing. The methodology is based on a multinomial logit model for Pr(S|R_6), the probability distribution of patient status S (healthy, polyps or diseased) conditional on …
A New Exact And More Powerful Unconditional Test Of No Treatment Effect From Binary Matched Pairs, Chris Lloyd
A New Exact And More Powerful Unconditional Test Of No Treatment Effect From Binary Matched Pairs, Chris Lloyd
Chris J. Lloyd
We consider the problem of testing for a difference in the probability of success from matched binary pairs. Starting with three standard inexact tests, the nuisance parameter is first estimated and then the residual dependence is eliminated by maximisation, producing what I call an E+M P-value. The E+M P-value based on McNemar's statistic is shown numerically to dominate previous suggestions, including partially maximised P-values as described in Berger and Sidik (2003). The latter method however may have computational advantages for large samples.
Exact One-Sided Confidence Limits For The Difference Between Two Correlated Proportions, Chris Lloyd, Max V. Moldovan
Exact One-Sided Confidence Limits For The Difference Between Two Correlated Proportions, Chris Lloyd, Max V. Moldovan
Chris J. Lloyd
We construct exact and optimal one-sided upper and lower confidence bounds for the difference between two probabilities based on matched binary pairs using well-established optimality theory of Buehler (1957). Starting with five different approximate loer and upper limits, we adjust them to have coverage probability exactly equal to the desired nominal level and then compare the resulting exact limits by their mean size. Exact limits based on the signed root likelihood ratio statistic are preferred and recommended for practical use.
Unconditional Efficient One-Sided Confidence Limits For The Odds Ratio Based On Conditional Likelihood, Chris Lloyd, Max Moldovan
Unconditional Efficient One-Sided Confidence Limits For The Odds Ratio Based On Conditional Likelihood, Chris Lloyd, Max Moldovan
Chris J. Lloyd
We compare various one-sided confidence limits for the odds ratio in a 2x2 table. The first group of limits relies on first order asymptotic approximations and includes limits based on the (signed) likelihood ratio, score and Wald statistics. The second group of limits is based on the conditional tilted hypergeometric distribution, with and without mid-P correction. All these limits have poor unconditional coverage properties and so we apply the general transformation of Buehler (1957) to obtain limits which are unconditionally exact. The performance of these competing exact limits is assessed across a range of sample sizes and parameter values by …
Efficient And Exact Tests Of The Risk Ratio In A Correlated 2x2 Table With Structural Zero, Chris Lloyd
Efficient And Exact Tests Of The Risk Ratio In A Correlated 2x2 Table With Structural Zero, Chris Lloyd
Chris J. Lloyd
For a correlated 2x2 table where the (01) cell is empty by design, the parameter of interest is typically the ratio of the probability of secondary response conditional on primary response to the probability of primary response, also known as a risk ratio. It is common to test whether or not the risk ratio equals one. One method of obtaining an exact P-value is to maximise the tail probability of the test statistic over the nuisance parameter. It is argued that better results are obtained by first replacing the nuisance parameter by its profile estimate in the calculation of its …