-
Recursos bibliográficos en papel y digitales -
- libros, artículos de revistas,
ponencias de eventos, etc. -
» Resultado: 2 registros
| Registro 1 de 2 | |
| Autor: | Fay, Michael P. - Halloran, M. Elizabeth - Follmann, Dean A. |
| Título: | Accounting for variability in sample size estimation with applications to nonadherence and estimation of variance and effect size |
| Fuente: | Biometrics. v.63, n.2. International Biometric Society |
| Páginas: | pp. 465-474 |
| Año: | jun. 2007 |
| Resumen: | We consider sample size calculations for testing differences in means between two samples and allowing for different variances in the two groups. Typically, the power functions depend on the sample size and a set of parameters assumed known, and the sample size needed to obtain a prespecified power is calculated. Here, we account for two sources of variability: we allow the sample size in the power function to be a stochastic variable, and we consider estimating the parameters from preliminary data. An example of the first source of variability is nonadherence (noncompliance). We assume that the proportion of subjects who will adhere to their treatment regimen is not known before the study, but that the proportion is a stochastic variable with a known distribution. Under this assumption, we develop simple closed form sample size calculations based on asymptotic normality. The second source of variability is in parameter estimates that are estimated from prior data. For example, we account for variability in estimating the variance of the normal response from existing data which are assumed to have the same variance as the study for which we are calculating the sample size. We show that we can account for the variability of the variance estimate by simply using a slightly larger nominal power in the usual sample size calculation, which we call the calibrated power. We show that the calculation of the calibrated power depends only on the sample size of the existing data, and we give a table of calibrated power by sample size. Further, we consider the calculation of the sample size in the rarer situation where we account for the variability in estimating the standardized effect size from some existing data. This latter situation, as well as several of the previous ones, is motivated by sample size calculations for a Phase II trial of a malaria vaccine candidate. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 2 | |
| Autor: | Kim, Hyune-Ju - Fay, Michael P. - Yu, Binbing - Barrett, Michael J. - Feuer, Eric J. |
| Título: | Comparability of segmented line regression models |
| Fuente: | Biometrics. v.60, n.4. International Biometric Society |
| Páginas: | pp. 1005-1014 |
| Año: | dec. 2004 |
| Resumen: | Segmented line regression models, which are composed of continuous linear phases, have been applied to describe changes in rate trend patterns. In this article, we propose a procedure to compare two segmented line regression functions, specifically to test (i) whether the two segmented line regression functions are identical or (ii) whether the two mean functions are parallel allowing different intercepts. A general form of the test statistic is described and then the permutation procedure is proposed to estimate the p-value of the test. The permutation test is compared to an approximate F-test in terms of the p-value estimation and the performance of the permutation test is studied via simulations. The tests are applied to compare female lung cancer mortality rates between two registry areas and also to compare female breast cancer mortality rates between two states. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
*** No hay más registros para visualizar ***
>> Nueva búsqueda <<