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

VL - 9

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 5

M1 - e98498

ER -