49 research outputs found
Subgroup identification in individual patient data meta-analysis using model-based recursive partitioning
Model-based recursive partitioning (MOB) can be used to identify subgroups
with differing treatment effects. The detection rate of treatment-by-covariate
interactions and the accuracy of identified subgroups using MOB depend strongly
on the sample size. Using data from multiple randomized controlled clinical
trials can overcome the problem of too small samples. However, naively pooling
data from multiple trials may result in the identification of spurious
subgroups as differences in study design, subject selection and other sources
of between-trial heterogeneity are ignored. In order to account for
between-trial heterogeneity in individual participant data (IPD) meta-analysis
random-effect models are frequently used. Commonly, heterogeneity in the
treatment effect is modelled using random effects whereas heterogeneity in the
baseline risks is modelled by either fixed effects or random effects. In this
article, we propose metaMOB, a procedure using the generalized mixed-effects
model tree (GLMM tree) algorithm for subgroup identification in IPD
meta-analysis. Although the application of metaMOB is potentially wider, e.g.
randomized experiments with participants in social sciences or preclinical
experiments in life sciences, we focus on randomized controlled clinical
trials. In a simulation study, metaMOB outperformed GLMM trees assuming a
random intercept only and model-based recursive partitioning (MOB), whose
algorithm is the basis for GLMM trees, with respect to the false discovery
rates, accuracy of identified subgroups and accuracy of estimated treatment
effect. The most robust and therefore most promising method is metaMOB with
fixed effects for modelling the between-trial heterogeneity in the baseline
risks
Optimal designs for dose finding studies with an active control
Dose finding studies often compare several doses of a new compound with a marketed
standard treatment as an active control. In the past, however, research has
focused mostly on experimental designs for placebo-controlled dose finding studies. To
the best of our knowledge, optimal designs for dose finding studies with an active control
have not been considered so far. As the statistical analysis for an active controlled
dose finding study can be formulated in terms of a mixture of two regression models,
the related design problem is different to what has been investigated before in the
literature. We present a rigorous approach to the problem of determining optimal designs
for estimating the smallest dose achieving the same treatment effect as the active
control. We determine explicitly the locally optimal designs for a broad class of models
employed in such studies. We also discuss robust design strategies and determine
related Bayesian and standardized minimax optimal designs. We illustrate the results
by investigating alternative designs for a clinical trial which has recently appeared in
a consulting project of one of the authors
Methods for non-proportional hazards in clinical trials: A systematic review
For the analysis of time-to-event data, frequently used methods such as the
log-rank test or the Cox proportional hazards model are based on the
proportional hazards assumption, which is often debatable. Although a wide
range of parametric and non-parametric methods for non-proportional hazards
(NPH) has been proposed, there is no consensus on the best approaches. To close
this gap, we conducted a systematic literature search to identify statistical
methods and software appropriate under NPH. Our literature search identified
907 abstracts, out of which we included 211 articles, mostly methodological
ones. Review articles and applications were less frequently identified. The
articles discuss effect measures, effect estimation and regression approaches,
hypothesis tests, and sample size calculation approaches, which are often
tailored to specific NPH situations. Using a unified notation, we provide an
overview of methods available. Furthermore, we derive some guidance from the
identified articles. We summarized the contents from the literature review in a
concise way in the main text and provide more detailed explanations in the
supplement (page 29)
Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.
The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs
Recent advances in methodology for clinical trials in small populations : the InSPiRe project
Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.
The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods
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Blood-based systems biology biomarkers for next-generation clinical trials in Alzheimer's disease
Alzheimerâs disease (AD)âa complex disease showing multiple pathomechanistic alterationsâis triggered by nonlinear dynamic interactions of genetic/epigenetic and environmental risk factors, which, ultimately, converge into a biologically heterogeneous disease. To tackle the burden of AD during early preclinical stages, accessible blood-based biomarkers are currently being developed. Specifically, next-generation clinical trials are expected to integrate positive and negative predictive blood-based biomarkers into study designs to evaluate, at the individual level, target druggability and potential drug resistance mechanisms. In this scenario, systems biology holds promise to accelerate validation and qualification for clinical trial contexts of useâincluding proof-of-mechanism, patient selection, assessment of treatment efficacy and safety rates, and prognostic evaluation. Albeit in their infancy, systems biology-based approaches are poised to identify relevant AD âsignaturesâ through multifactorial and interindividual variability, allowing us to decipher disease pathophysiology and etiology. Hopefully, innovative biomarker-drug codevelopment strategies will be the road ahead towards effective disease-modifying drugs.Includes MRC fundin