115 research outputs found
Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations
During drug development, evidence can emerge to suggest a treatment is more
effective in a specific patient subgroup. Whilst early trials may be conducted
in biomarker-mixed populations, later trials are more likely to enrol
biomarker-positive patients alone, thus leading to trials of the same treatment
investigated in different populations. When conducting a meta-analysis, a
conservative approach would be to combine only trials conducted in the
biomarker-positive subgroup. However, this discards potentially useful
information on treatment effects in the biomarker-positive subgroup concealed
within observed treatment effects in biomarker-mixed populations. We extend
standard random-effects meta-analysis to combine treatment effects obtained
from trials with different populations to estimate pooled treatment effects in
a biomarker subgroup of interest. The model assumes a systematic difference in
treatment effects between biomarker-positive and biomarker-negative subgroups,
which is estimated from trials which report either or both treatment effects.
The estimated systematic difference and proportion of biomarker-negative
patients in biomarker-mixed studies are used to interpolate treatment effects
in the biomarker-positive subgroup from observed treatment effects in the
biomarker-mixed population. The developed methods are applied to an
illustrative example in metastatic colorectal cancer and evaluated in a
simulation study. In the example, the developed method resulted in improved
precision of the pooled treatment effect estimate compared to standard
random-effects meta-analysis of trials investigating only biomarker-positive
patients. The simulation study confirmed that when the systematic difference in
treatment effects between biomarker subgroups is not very large, the developed
method can improve precision of estimation of pooled treatment effects while
maintaining low bias
PRM108 Bivariate Indirect Comparison Meta-Analysis Model in Economic Evaluation of Cancer Treatments
Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.
A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression
A novel approach to bivariate meta-analysis of binary outcomes and its application in the context of surrogate endpoints
Bivariate meta-analysis provides a useful framework for combining information
across related studies and has been widely utilised to combine evidence from
clinical studies in order to evaluate treatment efficacy. Bivariate
meta-analysis has also been used to investigate surrogacy patterns between
treatment effects on the surrogate and the final outcome. Surrogate endpoints
play an important role in drug development when they can be used to measure
treatment effect early compared to the final clinical outcome and to predict
clinical benefit or harm. The standard bivariate meta-analytic approach models
the observed treatment effects on the surrogate and final outcomes jointly, at
both the within-study and between-studies levels, using a bivariate normal
distribution. For binomial data a normal approximation can be used on log odds
ratio scale, however, this method may lead to biased results when the
proportions of events are close to one or zero, affecting the validation of
surrogate endpoints. In this paper, two Bayesian meta-analytic approaches are
introduced which allow for modelling the within-study variability using
binomial data directly. The first uses independent binomial likelihoods to
model the within-study variability avoiding to approximate the observed
treatment effects, however, ignores the within-study association. The second,
models the summarised events in each arm jointly using a bivariate copula with
binomial marginals. This allows the model to take into account the within-study
association through the copula dependence parameter. We applied the methods to
an illustrative example in chronic myeloid leukemia to investigate the
surrogate relationship between complete cytogenetic response (CCyR) and
event-free-survival (EFS).Comment: 20 pages, 6 figure
Creation of solitons and vortices by Bragg reflection of Bose-Einstein condensates in an optical lattice
We study the dynamics of Bose-Einstein condensates in an optical lattice and
harmonic trap. The condensates are set in motion by displacing the trap and
initially follow simple semiclassical paths, shaped by the lowest energy band.
Above a critical displacement, the condensate undergoes Bragg reflection. For
high atom densities, the first Bragg reflection generates a train of solitons
and vortices, which destabilize the condensate and trigger explosive expansion.
At lower densities, soliton and vortex formation requires multiple Bragg
reflections, and damps the center-of-mass motion.Comment: 5 pages including 5 figures (for higher resolution figures please
email the authors
Bivariate network meta-analysis for surrogate endpoint evaluation
Surrogate endpoints are very important in regulatory decision-making in
healthcare, in particular if they can be measured early compared to the
long-term final clinical outcome and act as good predictors of clinical
benefit. Bivariate meta-analysis methods can be used to evaluate surrogate
endpoints and to predict the treatment effect on the final outcome from the
treatment effect measured on a surrogate endpoint. However, candidate surrogate
endpoints are often imperfect, and the level of association between the
treatment effects on the surrogate and final outcomes may vary between
treatments. This imposes a limitation on the pairwise methods which do not
differentiate between the treatments. We develop bivariate network
meta-analysis (bvNMA) methods which combine data on treatment effects on the
surrogate and final outcomes, from trials investigating heterogeneous treatment
contrasts. The bvNMA methods estimate the effects on both outcomes for all
treatment contrasts individually in a single analysis. At the same time, they
allow us to model the surrogacy patterns across multiple trials (different
populations) within a treatment contrast and across treatment contrasts, thus
enabling predictions of the treatment effect on the final outcome for a new
study in a new population or investigating a new treatment. Modelling
assumptions about the between-studies heterogeneity and the network
consistency, and their impact on predictions, are investigated using simulated
data and an illustrative example in advanced colorectal cancer. When the
strength of the surrogate relationships varies across treatment contrasts,
bvNMA has the advantage of identifying treatments for which surrogacy holds,
thus leading to better predictions
Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions.
Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain
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