Details
| Original language | English |
|---|---|
| Pages (from-to) | 782-792 |
| Number of pages | 11 |
| Journal | Biometrical Journal |
| Volume | 50 |
| Issue number | 5 |
| Early online date | 17 Oct 2008 |
| Publication status | Published - Oct 2008 |
Abstract
Simultaneous confidence intervals for contrasts of means in a one-way layout with several independent samples are well established for Gaussian distributed data. Procedures addressing different hypotheses are available, such as all pairwise comparisons or comparisons to control, comparison with average, or different tests for order-restricted alternatives. However, if the distribution of the response is not Gaussian, corresponding methods are usually not available or not implemented in software. For the case of comparisons among several binomial proportions, we extended recently proposed confidence interval methods for the difference of two proportions or single contrasts to multiple contrasts by using quantiles of the multivariate normal distribution, taking the correlation into account. The small sample performance of the proposed methods was investigated in simulation studies. The simple adjustment of adding 2 pseudo-observations to each sample estimate leads to reasonable coverage probabilities. The methods are illustrated by the evaluation of real data examples of a clinical trial and a toxicological study. The proposed methods and examples are available in the R package MCPAN.
Keywords
- Multiple inference, Multivariate normal, Simple adjustment, Small sample
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
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In: Biometrical Journal, Vol. 50, No. 5, 10.2008, p. 782-792.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Approximate simultaneous confidence intervals for multiple contrasts of binomial proportions
AU - Schaarschmidt, Frank
AU - Sill, Martin
AU - Hothorn, Ludwig A.
PY - 2008/10
Y1 - 2008/10
N2 - Simultaneous confidence intervals for contrasts of means in a one-way layout with several independent samples are well established for Gaussian distributed data. Procedures addressing different hypotheses are available, such as all pairwise comparisons or comparisons to control, comparison with average, or different tests for order-restricted alternatives. However, if the distribution of the response is not Gaussian, corresponding methods are usually not available or not implemented in software. For the case of comparisons among several binomial proportions, we extended recently proposed confidence interval methods for the difference of two proportions or single contrasts to multiple contrasts by using quantiles of the multivariate normal distribution, taking the correlation into account. The small sample performance of the proposed methods was investigated in simulation studies. The simple adjustment of adding 2 pseudo-observations to each sample estimate leads to reasonable coverage probabilities. The methods are illustrated by the evaluation of real data examples of a clinical trial and a toxicological study. The proposed methods and examples are available in the R package MCPAN.
AB - Simultaneous confidence intervals for contrasts of means in a one-way layout with several independent samples are well established for Gaussian distributed data. Procedures addressing different hypotheses are available, such as all pairwise comparisons or comparisons to control, comparison with average, or different tests for order-restricted alternatives. However, if the distribution of the response is not Gaussian, corresponding methods are usually not available or not implemented in software. For the case of comparisons among several binomial proportions, we extended recently proposed confidence interval methods for the difference of two proportions or single contrasts to multiple contrasts by using quantiles of the multivariate normal distribution, taking the correlation into account. The small sample performance of the proposed methods was investigated in simulation studies. The simple adjustment of adding 2 pseudo-observations to each sample estimate leads to reasonable coverage probabilities. The methods are illustrated by the evaluation of real data examples of a clinical trial and a toxicological study. The proposed methods and examples are available in the R package MCPAN.
KW - Multiple inference
KW - Multivariate normal
KW - Simple adjustment
KW - Small sample
UR - http://www.scopus.com/inward/record.url?scp=54549121604&partnerID=8YFLogxK
U2 - 10.1002/bimj.200710465
DO - 10.1002/bimj.200710465
M3 - Article
C2 - 18932137
AN - SCOPUS:54549121604
VL - 50
SP - 782
EP - 792
JO - Biometrical Journal
JF - Biometrical Journal
SN - 0323-3847
IS - 5
ER -