Purpose. To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentratio...
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Purpose. To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentration, and the truncation error in the area under the curve. Methods. Bias in the Wagner-Nelson estimate was derived as a function of post-dosing time (t), k, ratio of absorption rate constant to k (r), and the coefficient of variation for estimates of k (CVk), or CVc for the observed concentration, by assuming a one-compartment model and using an independent estimate of k. The derived functions were used for evaluating the bias with r = 0.5, 3, or 6;k = 0.1 or 0.2;CVc = 0.2 or 0.4;and CVk =0.2 or 0.4;for t = 0 to 30 or 60. Results. Estimation error of k resulted in an upward bias in the Wagner-Nelson estimate that could lead to the estimate of the fraction absorbed being greater than unity. The bias resulting from the estimation error of k inflates the fraction of absorption vs. time profiles mainly in the early post-dosing period. The magnitude of the bias in the Wagner-Nelson estimate resulting from estimation error of k was mainly determined by CVk. The bias in the Wagner-Nelson estimate resulting from to estimation error in k can be dramatically reduced by use of the mean of several independent estimates of k, as in studies for development of an in vivo-in vitro correlation. The truncation error in the area under the curve can introduce a negative bias in the Wagner-Nelson estimate. This can partially offset the bias resulting from estimation error of k in the early post-dosing period. Measurement error of concentration does not introduce bias in the Wagner-Nelson estimate. Conclusions. Estimation error of k results in an upward bias in the Wagner-Nelson estimate, mainly in the early drug absorption phase. The truncation error in AUC can result in a downward bias, which may partially offset the upward bias due to estimation error of k in the early abso
Induction of cytochrome P450 3A4 (CYP3A4) is determined typically by employing primary culture of human hepatocytes and measuring CYP3A4 mRNA, protein and microsomal activity. Recently a pregnane X receptor (PXR) repo...
Induction of cytochrome P450 3A4 (CYP3A4) is determined typically by employing primary culture of human hepatocytes and measuring CYP3A4 mRNA, protein and microsomal activity. Recently a pregnane X receptor (PXR) reporter gene assay was established to screen CYP3A4 inducers. To evaluate results from the PXR reporter gene assay with those from the aforementioned conventional assays, 14 drugs were evaluated for their ability to induce CYP3A4 and activate PXR. Sandwiched primary cultures of human hepatocytes from six donors were used and CYP3A4 activity was assessed by measuring microsomal testosterone 6beta-hydroxylase activity. Hepatic CYP3A4 mRNA and protein levels were also analyzed using branched DNA technology/Northern blotting and Western blotting, respectively. In general, PXR activation correlated with the induction potential observed in human hepatocyte cultures. Clotrimazole, phenobarbital, rifampin, and sulfinpyrazone highly activated PXR and increased CYP3A4 activity;carbamazepine, dexamethasone, dexamethasone-t-butylacetate, phenytoin, sulfadimidine, and taxol weakly activated PXR and induced CYP3A4 activity, and methotrexate and probenecid showed no marked activation in either system. Ritonavir and troleandomycin showed marked PXR activation but no increase (in the case of troleandomycin) or a significant decrease (in the case of ritonavir) in microsomal CYP3A4 activity. It is concluded that the PXR reporter gene assay is a reliable and complementary method to assess the CYP3A4 induction potential of drugs and other xenobiotics.
The determination of the appropriate sample size is an important aspect of planning a clinical trial. In recent years, procedures for estimation of a nuisance parameter to adjust the sample size if necessary have been...
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The determination of the appropriate sample size is an important aspect of planning a clinical trial. In recent years, procedures for estimation of a nuisance parameter to adjust the sample size if necessary have been examined. Here, it is assumed that the clinical trial is conducted for the comparison, of two treatments, where the observations are assumed to have normal distributions with a common unknown variance. For sample size determination, the variance is assumed known and the resulting sample size is sensitive to misspecification of the variance. An estimate of the variance, obtained while the clinical trial is ongoing, can often be used to assess the appropriateness of the assumed variance. The use of a blinded estimate of the variance to potentially adjust the sample size is examined.
We provide a simple and good approximation of power of the unconditional test for two correlated binary variables. Suissa and Shuster (1991) described the exact unconditional test. The most commonly used statistical t...
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We provide a simple and good approximation of power of the unconditional test for two correlated binary variables. Suissa and Shuster (1991) described the exact unconditional test. The most commonly used statistical test in this setting McNemar's test, is exact conditional on the sum of the discordant pairs. Although asymptotically the conditional and unconditional versions coincide, a long-standing debate surrounds the choice between them. Several power approximations have been studied for both methods (Miettinen, 1968;Bennett and Underwood, 1970;Connett, Smith, and McHugh, 1987;Conner, 1987, Suissa and Shuster, 1991;Lachenbruch, 1992;Lachin, 1992). For the unconditional approach most existing power approximations use the Gaussian distribution, while the accurate ("exact") method is computationally burdensome. A new approximation uses the F statistic corresponding to a paired-data T test computed from the difference scores of the binary outcomes. Enumeration of all possible 2 x 2 tables for small sample sizes allowed evaluation of both test size and power. The new approximation compares favorably to others due to the combination of ease of use and accuracy.
If the purpose of a clinical study is not only to test the null hypothesis but also to estimate the magnitude of the treatment effect, the study design should ensure not only that the study will have adequate power bu...
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If the purpose of a clinical study is not only to test the null hypothesis but also to estimate the magnitude of the treatment effect, the study design should ensure not only that the study will have adequate power but also that it will enable the researcher to report the relevant parameters with an appropriate level of precision. This paper discusses the factors that control precision in survival studies and shows how a computer program may be used to address these issues. The program allows the user to systematically modify assumptions about the population (e.g. the magnitude of the hazard ratio or the attrition rate) and elements of the study design (e.g. sample size and trial duration), quickly identify the impact of these factors on the study's precision, and modify the study design accordingly. The program may also be used to compute power for a planned study, and confidence intervals for a completed study.
Computer programs for statistical power analysis typically require the user to provide a series of values and respond by reporting the corresponding power. These programs provide essentially the same functions as a pu...
Computer programs for statistical power analysis typically require the user to provide a series of values and respond by reporting the corresponding power. These programs provide essentially the same functions as a published text, albeit in a more convenient form. In this paper, we describe a program that instead uses innovative graphic techniques to provide insight into the interaction among the factors that determine power. For example, for t tests, the means and standard deviations of the two distributions, sample sizes, and alpha are displayed as bar graphs. As the researcher modifies these values, the corresponding values of beta (also displayed as a bar graph) and power are updated and displayed immediately. By displaying all of the factors that are instrumental in determining power, the program ensures that each will be addressed By allowing the user to determine the impact that any modifications will have on power, the program encourages an appropriate balance between alpha and beta while working within the constraints imposed by a limited sample size. The program also allows the user to generate tables and graphs to document the impact of the various factors on power. In addition, the program enables the user to run on-screen Monte Carlo simulations to demonstrate the importance of adequate statistical power, and as such, it can serve as a unique educational tool.
To facilitate the computation of statistical power for analysis of variance, Cohen developed the index of effect sizef, defined as theSD between groups divided by theSD within groups. A microcomputer program for stati...
To facilitate the computation of statistical power for analysis of variance, Cohen developed the index of effect sizef, defined as theSD between groups divided by theSD within groups. A microcomputer program for statistical power allows the user to compute the value off in any of several ways: by specifying the mean andSD for every cell in the ANOVA; by specifying the mean value for the two extreme cells and the pattern of dispersion for the remaining cells; by estimating the proportion of variance in the dependent variable that will be explained by group membership; and/or with reference to conventions for small, medium, and large effects. The program will compute power for any single set of parameters; it will also allow the user to generate tables and graphs showing how power will vary as a function of effect size, sample size, andα.
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