Drug exposure (area under the concentration-time curve) is dose proportional for the dose range of 25 to 1,000 mg, and there is a 1.5- to three-fold drug accumulation at steady-state after once-daily dosing. Analysis of the relationship between PD (WBC reduction) and PK parameters at steady-state indicates that a dose of 400 mg or greater is required for maximal PD effect.

The ability to select a sensitive patient population may be crucial for the development of a targeted therapy. Identifying such a population with an acceptable level of confidence may lead to an inflation in development time and cost. We present an approach that allows to decrease these costs and to increase the reliability of the population selection. It is based on an actual adaptive phase II/III design and uses Bayesian decision tools to select the population of interest at an interim analysis. The primary endpoint is assumed to be the time to some event like e.g. progression. It is shown that the use of appropriately stratified logrank tests in the adaptive test procedure guarantees overall type I error control also when using information on patients that are censored at the adaptive interim analysis. The use of Bayesian decision tools for the population selection decision making is discussed. Simulations are presented to illustrate the operating characteristics of the study design relative to a more traditional development approach. Estimation of treatment effects is considered as well.

(Fearn, 1975), a linear growth relationship between the dental measurement and age was assumed. This is also assumed in our subsequent analysis, together with homoscedastic normal errors within each separate population (girls, boys). Let x j , Y j denote, respectively, the Jth time point (using age 11 as origin) and associated measurement on the ith individual ( -1,... . 11 for the population of girls, J -12,..., 27 for the population of boys, J -1,...,4). Table 1 Here ------------------- -------------------------- 3For both the girl and boy populations the first stage model of (1. 1) ( 1 ) = p2, so that the individual straight-line growth curves are, n affect, regarded as distributed around a 'mean' population growth curve, p + p 2 x with population variation described by the 2 x 2 covariance matrix Z.(We digress to note that since the 13 are positive In this case it might be more reasonable to assume log 1 to be normally distributed. Such a refinement is not, in fact, important In this example and so we shall not pursue it, In order to keep this Initial exposition as simple as possible.We shall illustrate such a transformation n our second, nonlinear, example.) In what follows, we shall denote p, by aG (ca) for the girl (boy) populations and u2 by 3 G (3B).Since information from Individuals within each population is effectively 'pooled' to give population 'mean' Inferences, it can be important in such studies to guard against an aberrant or 'outlying' Individual unduly influencing the population Inference.Proceeding naively, examining, for example, the pooled population of girls and boys. one might plot the leastsquares estimates of ntercepts and slopes, as in Figure 1. Should one conclude from the plot that the boy labelled 24 Is a 'slope outlier'? Or that the boy labelled 21 is an 'intercept outlier'? We seek a modelling analysis 4 strategy which will .provide both a coherent outlier detection diagnostic and direct inferences which accommodate the effect of any outliers present. The main inference questions in this study relate to differences in growth between the girl and boy populations. We shall provide illustrative analyses of this n Section 4, taking into account the outlier issue discussed above. Table 2 presents pharmacokinetic data on the plasma concentration of the drug Cadralazine in 10 cardiac failure patients at various times after the administration of a single dose of 30mg. where a , Pi (> 0) are, respectively, the volume of distribution and elimination rate for Individual 1. I -1,.., 10. A nonlinear povulation vharmacokinetlc example 5Measurement variance is certainly related to underlying concentration level in studies such as this, so that a simple additive homoscedastic normal error assumption for the first stage distribution is inappropriate. We shall illustrate possible models and their subsequent analyses by considering the following three intra-individual error structures.As a first possibility, lettingPi. i) and denoting the righthand-side of (2.1) by iij(01), we assume that log Yij "...

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AimsThis study was designed to investigate the biochemical and physiolog ical covariates or comedications that affect the pharmacokinetics of imatinib mesylate in patients with chronic-phase chronic myeloid leukaemia (CP CML). MethodsPharmacokinetic data were analyzed in 371 patients receiving 400 mg imatinib once daily during a phase III trial of imatinib vs interferon-alfa plus cytarabine for the treatment of newly diagnosed CP CML. Covariates included age, weight, sex, ethnicity, haemoglobin (Hb) concentration, white blood cell (WBC) count, liver function, and creatinine concentration. Blood samples for imatinib analysis were taken on treatment days 1 and 29. Nonlinear mixed effects modelling was used for the population pharmacokinetic analysis. ResultsPopulation mean estimates (95% confidence interval) at day 1 for apparent clearance (CL) and apparent volume of distribution ( V ) of imatinib were 14 (13-15) l h -1 and 252 (237-267) l, respectively. Modelling suggested that CL decreased by 4 (3-5) l h -1 from day 1 to day 29, whereas V remained unchanged. Interindividual variability in CL and V was 32% and 31%, respectively. Weight, Hb, and WBC count demonstrated small effects on CL and V . Doubling body weight or Hb or halving the WBC count was associated with a 12%, 86% and 8% increase in CL, respectively, and a 32%, 60% and 5% increase in V , respectively. Comedications showed no clear effects on imatinib CL. ConclusionsPopulation covariates and coadministered drugs minimally affected imatinib pharmacokinetics in newly diagnosed CP CML patients.

Integration of a phase II and a phase III clinical trial into a single confirmatory study aims to shorten the development time without compromising the chance of success for a development program. These seamless phase II/III trials involve complex adaptations at the interim analysis, such as treatment selection, sample size reassessment, and stopping for futility. Bayesian methods can support these interim adaptations, and make this decision process more transparent. Use of a frequentist combination test for the final evaluation ensures that the type I error is controlled regardless of the adaptation rule employed at the interim analysis. In this paper, an adaptive seamless phase II/III trial design is proposed for studies where the endpoint is survival up to some specified timepoint and where Bayesian predictive power (PP) guides interim adaptations. For the evaluation of PP at the interim analysis, the event time is modelled as a piecewise exponential distribution, with informative priors for the hazard rates. As an illustrative example, regimen selection at interim in a four-arm trial with an active control is considered, where both non-inferiority and superiority to the control arm are tested. Frequentist properties of the adaptation criterion based on Bayesian PP are assessed by simulations.

Nonlinear random effects models are considered from the Bayesian point of view. The method of analysis follows closely that of Lindley and Smith (1972, Journal of the Royal Statistical Society, Series B 34, 1-42). The numerical method is related to the EM algorithm.

Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from −90% to +30%, were reported in many countries following early COVID-19 pandemic response measures (‘lockdowns’). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95–0.98, P value <0.0001), second (0.96, 0.92–0.99, 0.03) and third (0.97, 0.94–1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96–1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88–1.14, 0.98), third (0.99, 0.88–1.12, 0.89) and fourth (1.01, 0.87–1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02–1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03–1.15, 0.002), third (1.10, 1.03–1.17, 0.003) and fourth (1.12, 1.05–1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.