Zobrazeno 1 - 10
of 96
pro vyhledávání: '"John B, Copas"'
Publikováno v:
Community Health, 1976 Jul 01. 8(1), 55-55.
Externí odkaz:
https://www.jstor.org/stable/45159830
Autor:
Millard, David
Publikováno v:
The British Journal of Social Work, 1975 Jan 01. 5(3), 377-377.
Externí odkaz:
https://www.jstor.org/stable/23693856
Autor:
Philip R. A. May
Publikováno v:
Psychiatric Services. 26:531-532
Autor:
Shinto Eguchi, John B. Copas
Publikováno v:
Annals of the Institute of Statistical Mathematics. 72:329-352
Most statistical methods are based on models, but most practical applications ignore the fact that the results depend on the model as well as on the data. This paper examines the size of this model dependence, and finds that there can be very conside
Autor:
David Abrahamson
Publikováno v:
British Journal of Psychiatry. 126:588-588
Autor:
May, Philip R. A.
Publikováno v:
Psychiatric Services: A Journal of the American Psychiatric Association; August 1975, Vol. 26 Issue: 8 p531-532, 2p
Publikováno v:
Journal of the Royal Statistical Society. Series C, Applied Statistics
Summary Univariate meta‐analysis concerns a single outcome of interest measured across a number of independent studies. However, many research studies will have also measured secondary outcomes. Multivariate meta‐analysis allows us to take these
Publikováno v:
Statistical Methods in Medical Research. 26:2853-2868
Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new math
Publikováno v:
Statistical Methods in Medical Research
Outcome reporting bias occurs when outcomes in research studies are selectively reported, the selection being influenced by the study results. For benefit outcomes, we have shown how risk assessments using the Outcome Reporting Bias in Trials risk cl
Publikováno v:
Biometrics. 71:404-416
Summary In the classic discriminant model of two multivariate normal distributions with equal variance matrices, the linear discriminant function is optimal both in terms of the log likelihood ratio and in terms of maximizing the standardized differe