53,840 research outputs found
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
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
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 efficient 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 misspecification
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
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
Design Issues for Generalized Linear Models: A Review
Generalized linear models (GLMs) have been used quite effectively in the
modeling of a mean response under nonstandard conditions, where discrete as
well as continuous data distributions can be accommodated. The choice of design
for a GLM is a very important task in the development and building of an
adequate model. However, one major problem that handicaps the construction of a
GLM design is its dependence on the unknown parameters of the fitted model.
Several approaches have been proposed in the past 25 years to solve this
problem. These approaches, however, have provided only partial solutions that
apply in only some special cases, and the problem, in general, remains largely
unresolved. The purpose of this article is to focus attention on the
aforementioned dependence problem. We provide a survey of various existing
techniques dealing with the dependence problem. This survey includes
discussions concerning locally optimal designs, sequential designs, Bayesian
designs and the quantile dispersion graph approach for comparing designs for
GLMs.Comment: Published at http://dx.doi.org/10.1214/088342306000000105 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Do Footprint-based CAFE Standards Make Car Models Bigger?
Corporate Average Fuel Economy (CAFE) standards have historically been set equal across all manufacturer fleets of the same type. Concerns about varying costs across firms and safety implications of standards that are set homogeneously across firms and models resulted in a policy shift towards footprint-based standards. Under this type of standard, individual car models face targets based on the size of the area between the wheelbase and wheel track, so that larger models face less stringent standards, and manufacturers who make, on average, larger cars will face a lighter fleet standard. Theoretical models have shown that this type of policy creates an incentive for firms to effectively lighten the standard they face, but no purely empirical study has tested this theoretical conclusion. I use a series of difference-in-difference estimations to test whether firms respond to the policy by increasing the footprint of individual models. I find some statistically significant evidence of an increase in footprint size in response to the policy when the treatment effect is assumed to increase by market share
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