286 research outputs found
Optimal designs for dose finding studies
Identifying the "right" dose is one of the most critical and difficult steps in the clinical development process of any medicinal drug. Its importance cannot be understated: selecting too high a dose can result in unacceptable toxicity and associated safety problems, while choosing too low a dose leads to smaller chances of showing sufficient efficacy in confirmatory trials, thus reducing the chance of approval for the drug. In this paper we investigate the problem of deriving e?cient designs for the estimation of the minimum effective dose (MED) by determining the appropriate number and actual levels of the doses to be administered to patients, as well as their relative sample size allocations. More specifically, we derive local optimal designs that minimize the asymptotic variance of the MED estimate under a particular dose response model. The small sample properties of these designs are investigated via simulation, together with their sensitivity to misspeciffication of the true parameter values and of the underlying dose response model. Finally, robust optimal designs are constructed, which take into account a set of potential dose response profiles within classes of models commonly used in practice. --minimum effective dose,c-optimal design,dose response,Elfving's theorem
FORTRAN 90 and SAS-IML Programs for Computation of Critical Values for Multiple Testing and Simultaneous Confidence Intervals
See paper for mathematical introduction.
Practical considerations for optimal designs in clinical dose finding studies
Determining an adequate dose level for a drug and, more broadly, characterizing its dose response relationship, are key objectives in the clinical development of any medicinal drug. If the dose is set too high, safety and tolerability problems are likely to result, while selecting too low a dose makes it difficult to establish adequate efficacy in the confirmatory phase, possibly leading to a failed program. Hence, dose finding studies are of critical importance in drug development and need to be planned carefully. In this paper we focus on practical considerations for establishing efficient study designs to estimate target doses of interest. We consider optimal designs for both the estimation of the minimum effective dose (MED) and the dose achieving 100p% of the maximum treatment effect (EDp). These designs are compared with D-optimal designs for a given dose response model. Extensions to robust designs accounting for model uncertainty are also discussed. A case study is used to motivate and illustrate the methods from this paper. --dose finding,robust designs,model uncertainty,minimum effective dose,dose response,target dose estimation,sample size
Model Selection versus Model Averaging in Dose Finding Studies
Phase II dose finding studies in clinical drug development are typically
conducted to adequately characterize the dose response relationship of a new
drug. An important decision is then on the choice of a suitable dose response
function to support dose selection for the subsequent Phase III studies. In
this paper we compare different approaches for model selection and model
averaging using mathematical properties as well as simulations. Accordingly, we
review and illustrate asymptotic properties of model selection criteria and
investigate their behavior when changing the sample size but keeping the effect
size constant. In a large scale simulation study we investigate how the various
approaches perform in realistically chosen settings. Finally, the different
methods are illustrated with a recently conducted Phase II dosefinding study in
patients with chronic obstructive pulmonary disease.Comment: Keywords and Phrases: Model selection; model averaging; clinical
trials; simulation stud
Adaptive designs based on the truncated product method
BACKGROUND: Adaptive designs are becoming increasingly important in clinical research. One approach subdivides the study into several (two or more) stages and combines the p-values of the different stages using Fisher's combination test. METHODS: Alternatively to Fisher's test, the recently proposed truncated product method (TPM) can be applied to combine the p-values. The TPM uses the product of only those p-values that do not exceed some fixed cut-off value. Here, these two competing analyses are compared. RESULTS: When an early termination due to insufficient effects is not appropriate, such as in dose-response analyses, the probability to stop the trial early with the rejection of the null hypothesis is increased when the TPM is applied. Therefore, the expected total sample size is decreased. This decrease in the sample size is not connected with a loss in power. The TPM turns out to be less advantageous, when an early termination of the study due to insufficient effects is possible. This is due to a decrease of the probability to stop the trial early. CONCLUSION: It is recommended to apply the TPM rather than Fisher's combination test whenever an early termination due to insufficient effects is not suitable within the adaptive design
Response-adaptive dose-finding under model uncertainty
Dose-finding studies are frequently conducted to evaluate the effect of
different doses or concentration levels of a compound on a response of
interest. Applications include the investigation of a new medicinal drug, a
herbicide or fertilizer, a molecular entity, an environmental toxin, or an
industrial chemical. In pharmaceutical drug development, dose-finding studies
are of critical importance because of regulatory requirements that marketed
doses are safe and provide clinically relevant efficacy. Motivated by a
dose-finding study in moderate persistent asthma, we propose response-adaptive
designs addressing two major challenges in dose-finding studies: uncertainty
about the dose-response models and large variability in parameter estimates. To
allocate new cohorts of patients in an ongoing study, we use optimal designs
that are robust under model uncertainty. In addition, we use a Bayesian
shrinkage approach to stabilize the parameter estimates over the successive
interim analyses used in the adaptations. This approach allows us to calculate
updated parameter estimates and model probabilities that can then be used to
calculate the optimal design for subsequent cohorts. The resulting designs are
hence robust with respect to model misspecification and additionally can
efficiently adapt to the information accrued in an ongoing study. We focus on
adaptive designs for estimating the minimum effective dose, although
alternative optimality criteria or mixtures thereof could be used, enabling the
design to address multiple objectives.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS445 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Assessing the similarity of dose response and target doses in two non-overlapping subgroups
We consider two problems that are attracting increasing attention in clinical
dose finding studies. First, we assess the similarity of two non-linear
regression models for two non-overlapping subgroups of patients over a
restricted covariate space. To this end, we derive a confidence interval for
the maximum difference between the two given models. If this confidence
interval excludes the equivalence margins, similarity of dose response can be
claimed. Second, we address the problem of demonstrating the similarity of two
target doses for two non-overlapping subgroups, using again a confidence
interval based approach. We illustrate the proposed methods with a real case
study and investigate their operating characteristics (coverage probabilities,
Type I error rates, power) via simulation.Comment: Keywords and Phrases: equivalence testing, multiregional trial,
target dose estimation, subgroup analyse
Optimal designs for active controlled dose finding trials with efficacy-toxicity outcomes
Nonlinear regression models addressing both efficacy and toxicity outcomes
are increasingly used in dose-finding trials, such as in pharmaceutical drug
development. However, research on related experimental design problems for
corresponding active controlled trials is still scarce. In this paper we derive
optimal designs to estimate efficacy and toxicity in an active controlled
clinical dose finding trial when the bivariate continuous outcomes are modeled
either by polynomials up to degree 2, the Michaelis- Menten model, the Emax
model, or a combination thereof. We determine upper bounds on the number of
different doses levels required for the optimal design and provide conditions
under which the boundary points of the design space are included in the optimal
design. We also provide an analytical description of the minimally supported
-optimal designs and show that they do not depend on the correlation between
the bivariate outcomes. We illustrate the proposed methods with numerical
examples and demonstrate the advantages of the -optimal design for a trial,
which has recently been considered in the literature.Comment: Keywords and Phrases: Active controlled trials, dose finding, optimal
design, admissible design, Emax model, Equivalence theorem, Particle swarm
optimization, Tchebycheff syste
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