TY - JOUR
T1 - Simple methods of determining confidence intervals for functions of estimates in published results
AU - Fitzmaurice, Garrett
AU - Lipsitz, Stuart
AU - Natarajan, Sundar
AU - Gawande, Atul
AU - Sinha, Debajyoti
AU - Greenberg, Caprice
AU - Giovannucci, Edward
PY - 2014/5/28
Y1 - 2014/5/28
N2 - Often, the reader of a published paper is interested in a comparison of parameters that has not been presented. It is not possible to make inferences beyond point estimation since the standard error for the contrast of the estimated parameters depends upon the (unreported) correlation. This study explores approaches to obtain valid confidence intervals when the correlation (ρ) is unknown. We illustrate three proposed approaches using data from the National Health Interview Survey. The three approaches include the Bonferroni method and the standard confidence interval assuming ρ = -1 (most conservative) or ρ = 0 (when the correlation is known to be non-negative). The Bonferroni approach is found to be the most conservative. For the difference in two estimated parameter, the standard confidence interval assuming ρ = -1 yields a 95% confidence interval that is approximately 12.5% narrower than the Bonferroni confidence interval; when the correlation is known to be positive, the standard 95% confidence interval assuming ρ = 0 is approximately 38% narrower than the Bonferroni. In summary, this article demonstrates simple methods to determine confidence intervals for unreported comparisons. We suggest use of the standard confidence interval assuming ρ = -1 if no information is available or ρ = 0 if the correlation is known to be non-negative.
AB - Often, the reader of a published paper is interested in a comparison of parameters that has not been presented. It is not possible to make inferences beyond point estimation since the standard error for the contrast of the estimated parameters depends upon the (unreported) correlation. This study explores approaches to obtain valid confidence intervals when the correlation (ρ) is unknown. We illustrate three proposed approaches using data from the National Health Interview Survey. The three approaches include the Bonferroni method and the standard confidence interval assuming ρ = -1 (most conservative) or ρ = 0 (when the correlation is known to be non-negative). The Bonferroni approach is found to be the most conservative. For the difference in two estimated parameter, the standard confidence interval assuming ρ = -1 yields a 95% confidence interval that is approximately 12.5% narrower than the Bonferroni confidence interval; when the correlation is known to be positive, the standard 95% confidence interval assuming ρ = 0 is approximately 38% narrower than the Bonferroni. In summary, this article demonstrates simple methods to determine confidence intervals for unreported comparisons. We suggest use of the standard confidence interval assuming ρ = -1 if no information is available or ρ = 0 if the correlation is known to be non-negative.
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U2 - 10.1371/journal.pone.0098498
DO - 10.1371/journal.pone.0098498
M3 - Article
C2 - 24869806
AN - SCOPUS:84901594084
SN - 1932-6203
VL - 9
JO - PloS one
JF - PloS one
IS - 5
M1 - e98498
ER -