95,104 research outputs found
Recommended from our members
On optimal designs for clinical trials: An updated review
Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development
Sequential stopping for high-throughput experiments
In high-throughput experiments, the sample size is typically chosen informally. Most formal sample-size calculations depend critically on prior knowledge. We propose a sequential strategy that, by updating knowledge when new data are available, depends less critically on prior assumptions. Experiments are stopped or continued based on the potential benefits in obtaining additional data. The underlying decision-theoretic framework guarantees the design to proceed in a coherent fashion. We propose intuitively appealing, easy-to-implement utility functions. As in most sequential design problems, an exact solution is prohibitive. We propose a simulation-based approximation that uses decision boundaries. We apply the method to RNA-seq, microarray, and reverse-phase protein array studies and show its potential advantages. The approach has been added to the Bioconductor package gaga
Stochastic Approximation and Modern Model-Based Designs for Dose-Finding Clinical Trials
In 1951 Robbins and Monro published the seminal article on stochastic
approximation and made a specific reference to its application to the
"estimation of a quantal using response, nonresponse data." Since the 1990s,
statistical methodology for dose-finding studies has grown into an active area
of research. The dose-finding problem is at its core a percentile estimation
problem and is in line with what the Robbins--Monro method sets out to solve.
In this light, it is quite surprising that the dose-finding literature has
developed rather independently of the older stochastic approximation
literature. The fact that stochastic approximation has seldom been used in
actual clinical studies stands in stark contrast with its constant application
in engineering and finance. In this article, I explore similarities and
differences between the dose-finding and the stochastic approximation
literatures. This review also sheds light on the present and future relevance
of stochastic approximation to dose-finding clinical trials. Such connections
will in turn steer dose-finding methodology on a rigorous course and extend its
ability to handle increasingly complex clinical situations.Comment: Published in at http://dx.doi.org/10.1214/10-STS334 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Handling Covariates in the Design of Clinical Trials
There has been a split in the statistics community about the need for taking
covariates into account in the design phase of a clinical trial. There are many
advocates of using stratification and covariate-adaptive randomization to
promote balance on certain known covariates. However, balance does not always
promote efficiency or ensure more patients are assigned to the better
treatment. We describe these procedures, including model-based procedures, for
incorporating covariates into the design of clinical trials, and give examples
where balance, efficiency and ethical considerations may be in conflict. We
advocate a new class of procedures, covariate-adjusted response-adaptive (CARA)
randomization procedures that attempt to optimize both efficiency and ethical
considerations, while maintaining randomization. We review all these
procedures, present a few new simulation studies, and conclude with our
philosophy.Comment: Published in at http://dx.doi.org/10.1214/08-STS269 the Statistical
Science (http://www.imstat.org/sts/) 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
- …