A constrained optimum adaptive design for dose finding in early phase clinical trials.

Recently, interest has grown in the development of dose-finding methods that consider both toxicity and efficacy as endpoints. Along with responses on these, the incorporation of pharmacokinetic (PK) data can be beneficial in terms of patients' safety and can also increase the efficiency of the design for finding the best dose for the next phase. In this paper, the maximum concentration (Cmax) is used as the PK measure guiding the dose selection. The ethically attractive approach, which is based on the probability of efficacy, is used as a dose optimisation criterion. At each stage of an adaptive trial, that dose is selected for which the criterion is maximised, subject to the constraints imposed on the Cmax and the probability of toxicity. The inter-patient variability of the PK model parameters is considered, and population D-optimal sampling time points for measuring the concentration of a drug in the blood are calculated. The method is illustrated with a one-compartment PK model with first-order absorption, with the parameters being assumed to be random. The Cox model for bivariate binary responses is employed to model the dose-response outcomes. The results of a simulation study for several plausible dose-response scenarios show a significant gain in the efficiency of the design, as well as a reduction in the proportion of toxic responses.

Combined criteria for dose optimisation in early phase clinical trials.

This paper aims to investigate whether any bridge is possible between so-called best intention and D-optimum designs. It introduces combined criteria for dose optimisation in seamless phase I/II adaptive clinical trials. Each of the optimality criteria considers efficacy and toxicity as endpoints and is based on the probability of a successful outcome and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. In addition, one of the criteria incorporates penalties for choosing a toxic or inefficacious dose. Starting with the lowest dose, the adaptive design selects the dose for each subsequent cohort that maximises the respective defined criterion. The methodology is illustrated with a dose-response model that assumes trinomial responses. Simulation studies show that the method is capable of identifying the optimal dose accurately without exposing many patients to toxic doses.

Imbalance properties of centre-stratified permuted-block and complete randomisation for several treatments in a clinical trial.

Randomisation schemes are rules that assign patients to treatments in a clinical trial. Many of these schemes have the common aim of maintaining balance in the numbers of patients across treatment groups. The properties of imbalance that have been investigated in the literature are based on two treatment groups. In this paper, their properties for K > 2 treatments are studied for two randomisation schemes: centre-stratified permuted-block and complete randomisation. For both randomisation schemes, analytical approaches are investigated assuming that the patient recruitment process follows a Poisson-gamma model. When the number of centres involved in a trial is large, the imbalance for both schemes is approximated by a multivariate normal distribution. The accuracy of the approximations is assessed by simulation. A test for treatment differences is also considered for normal responses, and numerical values for its power are presented for centre-stratified permuted-block randomisation. To speed up the calculations, a combined analytical/approximate approach is used. Copyright © 2016 John Wiley & Sons, Ltd.

Inference following designs which adjust for imbalances in prognostic factors.

BACKGROUND: When the minimisation method due to Taves is used to balance treatment groups across prognostic factors, a problem arises at the time of analysing the results. Since minimisation is essentially a deterministic method, any statistical test based on the assumption of complete randomisation should not be used in the analysis. Previous articles have shown that analysis of covariance (ANOCOVA) produces valid tests. METHODS: In this article, these results are extended to trials with more than one prognostic factor and more than two treatments. An alternative design to minimisation which makes use of optimum design theory is also considered, with two choices of biased coin. Simulation is used to study the effect on the power and the coverage probabilities of the usual tests and confidence intervals when these different allocation methods are applied. The results are then illustrated using data from an actual clinical trial. RESULTS: Simulation shows that when ANOCOVA is used, it is sometimes more powerful with these designs than with minimisation and produces slightly conservative confidence intervals for the treatment mean differences. The increase in power and conservativeness is more pronounced when there are more prognostic factors. The possibility of treatment-covariate interactions is also addressed. LIMITATIONS: Results are only given when treatment responses are normally distributed. CONCLUSIONS: Under the simulated situations considered, when a covariate-adaptive design is used, the use of ANOCOVA yields a test which preserves the nominal significance level as compared to the conservativeness of analysis of variance.

The duplicate method of uncertainty estimation: are eight targets enough?

This paper presents methods for calculating confidence intervals for estimates of sampling uncertainty (s(samp)) and analytical uncertainty (s(anal)) using the chi-squared distribution. These uncertainty estimates are derived from application of the duplicate method, which recommends a minimum of eight duplicate samples. The methods are applied to two case studies--moisture in butter and nitrate in lettuce. Use of the recommended minimum of eight duplicate samples is justified for both case studies as the confidence intervals calculated using greater than eight duplicates did not show any appreciable reduction in width. It is considered that eight duplicates provide estimates of uncertainty that are both acceptably accurate and cost effective.

Sequential urn designs with elimination for comparing K > or =3 treatments.

A fully sequential procedure is proposed for comparing K > or =3 treatments with immediate binary responses. The procedure uses an adaptive urn design to randomize patients to the treatments and stopping rules are incorporated for eliminating less promising treatments. Simulation is used to assess the performance of the procedure for several adaptive urn designs, in terms of expected numbers of treatment failures and allocation proportions, and the effect on estimation at the end of the trial is also addressed. It is concluded that the drop-the-loser rule is more effective than equal allocation and all of the other designs considered. The practical benefits of the procedure are illustrated using the results of a three-treatment lung cancer study. It is then shown how the sequential elimination procedure may be used in dose-finding studies and its performance is compared with a recently proposed method. Several possible extensions to the work are briefly indicated.

The use of the triangular test with response-adaptive treatment allocation.

A clinical trial is considered in which two treatments with binary responses are to be compared. A popular sequential stopping rule, the triangular test, is studied when various response-adaptive treatment allocation rules are applied, such as the recently proposed drop-the-loser rule, an urn randomization scheme. The paper extends previous work by Coad and Rosenberger, who combined the triangular test with the randomized play-the-winner rule. The purpose of the paper is to investigate to what extent the variability of an adaptive design affects the overall performance of the triangular test. The adaptive rules under consideration are described and some of their asymptotic properties are summarized. Simulation is then used to assess the performance of the triangular test when combined with the various adaptive rules. The main finding is that the drop-the-loser rule is the most promising of the adaptive rules considered in terms of a less variable allocation proportion and a smaller number of treatment failures. The use of this rule with the triangular test is beneficial compared with the triangular test with equal allocation, since it yields fewer treatment failures on average while providing comparable power with similar expected sample size. The results of an AIDS trial are used to illustrate the performance of the triangular test when combined with the drop-the-loser rule.