Better confidence intervals for the population coefficient of variation

Author(s):  
Ivana Ivković ◽  
Vesna Rajić
2000 ◽  
Vol 92 (4) ◽  
pp. 985-992 ◽  
Author(s):  
Wei Lu ◽  
James M. Bailey

Background Many pharmacologic studies record data as binary yes-or-no variables, and analysis is performed using logistic regression. This study investigates the accuracy of estimation of the drug concentration associated with a 50% probability of drug effect (C50) and the term describing the steepness of the concentration-effect relation (gamma). Methods The authors developed a technique for simulating pharmacodynamic studies with binary yes-or-no responses. Simulations were conducted assuming either that each data point was derived from the same patient or that data were pooled from multiple patients in a population with log-normal distributions of C50 and gamma. Coefficients of variation were calculated. The authors also determined the percentage of simulations in which the 95% confidence intervals contained the true parameter value. Results The coefficient of variation of parameter estimates decreased with increasing n and gamma. The 95% confidence intervals for C50 estimation contained the true parameter value in more than 90% of the simulations. However, the 95% confidence intervals of gamma did not contain the true value in a substantial number of simulations of data from multiple patients. Conclusion The coefficient of variation of parameter estimates may be as large as 40-50% for small studies (n < or = 20). The 95% confidence intervals of C50 almost always contain the true value, underscoring the need for always reporting confidence intervals. However, when data from multiple patients is naively pooled, the estimates of gamma may be biased, and the 95% confidence intervals may not contain the true value.


Author(s):  
Takatsugu Hyodo

Summary This study focused on the variation in the yields of constituents in smoke from commercial cigarette brands available on the Japanese market. Nineteen commercial cigarette brands were sampled five times every two months from 2009 to 2010. The target constituents were benzo[a]-pyrene, 1,3-butadiene, benzene, formaldehyde, acetaldehyde, acrolein, N-nitrosonornicotine (NNN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), carbon monoxide, “tar”, and nicotine. The results of this study showed that the coefficient of variation (CV) values varied greatly by brands, constituents, and smoking regimes. The yields of NNN and NNK in the smoke were strongly correlated to their yields in the tobacco filler blend for most brands. The yields of benzo[a]pyrene under the International Organization for Standardization (ISO) and the Health Canada Intense (HCI) smoking regimes and 1,3-butadiene under the HCI smoking regime were found to be influenced by the measurement. It was shown that factors for variation were highly varied among constituents. The grand mean of CV values for NNN and formaldehyde associated with cigarette manufacturing over ten months and measurement at the JT laboratory under the HCI smoking regimes were 17.1% and 6.6% respectively. The grand mean of CV values for NNN and formaldehyde associated with both cigarette manufacturing over ten months and measurement at different laboratories under the HCI smoking regimes were 23.7% and 22.9% respectively. This is due to the fact that formaldehyde showed the highest CV values for reproducibility among the constituents. Thus, in order to set realistic and robust confidence intervals, it is very important to take into account the variations associated with cigarette manufacturing and measurement within and between laboratories.


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