The usual approach to determine bioequivalence for highly variable drugs is scaled average bioequivalence, which is based on expanding the limits as a function of the within-subject variability in the reference formulation. This requires separately estimating this variability and thus using replicated or semireplicated crossover designs. On the other hand, regulations also allow using common 2 × 2 crossover designs based on two-stage adaptive approaches with sample size reestimation at an interim analysis. The choice between scaled or two-stage designs is crucial and must be fully described in the protocol. Using Monte Carlo simulations, we show that both methodologies achieve comparable statistical power, though the scaled method usually requires less sample size, but at the expense of each subject being exposed more times to the treatments. With an adequate initial sample size (not too low, eg, 24 subjects), two-stage methods are a flexible and efficient option to consider: They have enough power (eg, 80%) at the first stage for non-highly variable drugs, and, if otherwise, they provide the opportunity to step up to a second stage that includes additional subjects.
This is the peer reviewed version of the following article: Molins, E., Cobo, E., Ocaña, J. Two-stage designs versus European scaled average designs in bioequivalence studies for highly variable drugs: which to choose?. "Statistics in medicine", Desembre 2017, vol. 36, núm. 30, p. 4777-4788, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/sim.7452/pdf. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Armero, C.; Forné, C.; Rué, M.; Forte, A.; Perpiñán, H.; Gomez, G.; Bare, M. Statistics in medicine Vol. 35, num. 28, p. 5267-5282 DOI: 10.1002/sim.7065 Data de publicació: 2016-12-10 Article en revista
When comparing two treatment groups in a time-to-event analysis, it is common to use a composite event
consisting of two or more distinct outcomes. The goal of this paper is to develop a statistical methodology to
derive efficiency guidelines for deciding whether to expand a study primary endpoint from E1 (for example,
non-fatal myocardial infarction and cardiovascular death) to the composite of E1 and E2 (for example, non-fatal
myocardial infarction, cardiovascular death or revascularisation). We investigate this problem by considering
the asymptotic relative efficiency of a log-rank test for comparing treatment groups with respect to a primary
relevant endpoint E1 versus the composite primary endpoint, say E , of E1 and E2, where E2 is some additional
Several studies have assessed the association between air pollution and hospital admissions or emergency room visits for asthma. Because of both the presence of missing data and the small number of observations, the relationship between air pollution and mortality for respiratory causes has been rarely analysed, and
when it has, the results are very inconclusive or even inconsistent. The objective of this study is to assess the
relation between levels of air pollutants (black smoke, sulphur dioxide, nitrogen dioxide and ozone), meteorological variables (24th average temperature and relative humidity) and daily mortality for asthma
(ICD-9 493, 2 to 45 years old) in Barcelona, Spain, during the period 1986}1989. Since the range of daily mortality for asthma (2 to 45 years old) during the period 1986}1989 was 0}1), we have preferred to consider
this variable as dichotomous. First, the relationship between air pollutants, meteorological variables and daily mortality (controlled for the occurrence of asthma epidemics) was estimated using logistic regression models. As was expected, the residuals from this regression were autocorrelated, showing a complex moving
average (MA) structure. If covariates were not time dependent the so-called generalized linear mixed models, could be applied. In our case the covariates vary. As a consequence the likelihood is numerically intractable because it involves the evaluation of n-fold integral. An alternative method that avoids these numerical problems is the generalized estimating equations method(GEE). It is a multivariate analogue of quasilikelihood
estimation. In the absence of a likelihood function the parameters can be estimated by solving a multivariate analogue of the quasi score function. We have modi"ed the GEE method in this paper, allowing for a di!erent structure in the error covariance matrix (MA). Both air pollutants and meteorological variables are related with the occurrence of a death for asthma. In this sense, nitrogen dioxide, NO2 (β=0·037, p<0·05), ozone, O3(β=0·021, p<0·06) and high temperature (the β's were in the range
(0·098-0·182), p<0·05) increased the probability of dying for asthma in Barcelona during the period 1986-1989. Copyright 1999 John Wiley & Sons, Ltd.