2,362 research outputs found
Continual Reassessment and Related Dose-Finding Designs
During the last twenty years there have been considerable methodological
developments in the design and analysis of Phase 1, Phase 2 and Phase 1/2
dose-finding studies. Many of these developments are related to the continual
reassessment method (CRM), first introduced by O'Quigley, Pepe and Fisher
(\citeyearQPF1990). CRM models have proven themselves to be of practical use
and, in this discussion, we investigate the basic approach, some connections to
other methods, some generalizations, as well as further applications of the
model. We obtain some new results which can provide guidance in practice.Comment: Published in at http://dx.doi.org/10.1214/10-STS332 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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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
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
Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents
Drug combination trials are increasingly common nowadays in clinical
research. However, very few methods have been developed to consider toxicity
attributions in the dose escalation process. We are motivated by a trial in
which the clinician is able to identify certain toxicities that can be
attributed to one of the agents. We present a Bayesian adaptive design in which
toxicity attributions are modeled via Copula regression and the maximum
tolerated dose (MTD) curve is estimated as a function of model parameters. The
dose escalation algorithm uses cohorts of two patients, following the continual
reassessment method (CRM) scheme, where at each stage of the trial, we search
for the dose of one agent given the current dose of the other agent. The
performance of the design is studied by evaluating its operating
characteristics when the underlying model is either correctly specified or
misspecified. We show that this method can be extended to accommodate discrete
dose combinations
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