Bayesian Nonparametric Estimation of Targeted Agent Effects on Biomarker Change to Predict Clinical Outcome

Graziani, Rebecca; Guindani, Michele; Thall, Peter F.

doi:10.1111/biom.12250

Cited by 10 publications

(9 citation statements)

References 43 publications

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“…The model also provided a framework for estimating how interactions between Δ and treatment may affect PFS, formalized in equation (7) of Graziani, et al [23]. The BNP analysis identified clusters of Δ values, shown in Figure 5.…”

Section: Estimating Targeted Agent Effects On Survivalmentioning

confidence: 99%

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Bayesian nonparametric statistics: A new toolkit for discovery in cancer research

Thall

Müeller

et al. 2017

Pharmaceutical Statistics

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Many commonly used statistical methods for data analysis or clinical trial design rely on incorrect assumptions, or assume an over-simplified framework that ignores important information. Such statistical practices may lead to incorrect conclusions about treatment effects, or clinical trial designs that are impractical or that do not accurately reflect the investigator’s goals. Bayesian nonparametric (BNP) models and methods are a very flexible new class of statistical tools that can overcome such limitations. This is because BNP models can accurately approximate any distribution or function, and can accommodate a broad range of statistical problems, including density estimation, regression, survival analysis, graphical modeling, neural networks, classification, clustering, population models, forecasting and prediction, spatio-temporal models, and causal inference. This paper describes three illustrative applications of BNP methods, including a randomized clinical trial to compare treatments for intraoperative air leaks after pulmonary resection, estimating survival time with different multi-stage chemotherapy regimes for acute leukemia, and evaluating joint effects of targeted treatment and an intermediate biological outcome on progression-free survival time in prostate cancer.

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Section: Estimating Targeted Agent Effects On Survivalmentioning

confidence: 99%

“…Motivated by this problem, Graziani et al reported a BNP analysis of the trial data. Figure gives BNP density estimates, plotted as dashed lines for C post and solid lines for C pre , corresponding to each histogram.…”

Section: Estimating Targeted Agent Effects On Survivalmentioning

confidence: 99%

Bayesian nonparametric statistics: A new toolkit for discovery in cancer research

Thall

Müeller

et al. 2017

Pharmaceutical Statistics

Self Cite

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“…We define a distance measure between two distributions based on the vertical quantile comparison function as used in Graziani et al. () to define a measure of distributional change and identify groups of individuals having similar biological responses. Specifically, we use

D false(F_{X}, F_{Y} false) = false| P false(X < Y false) - 0.5 false|

as a distance between two distributions

F_{X}

and

F_{Y}

.…”

Section: Simulationmentioning

confidence: 99%

Clustering Distributions with the Marginalized Nested Dirichlet Process

et al. 2017

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We introduce a marginal version of the nested Dirichlet process to cluster distributions or histograms. We apply the model to cluster genes by patterns of gene-gene interaction. The proposed approach is based on the nested partition that is implied in the original construction of the nested Dirichlet process. It allows simulation exact inference, as opposed to a truncated Dirichlet process approximation. More importantly, the construction highlights the nature of the nested Dirichlet process as a nested partition of experimental units. We apply the proposed model to inference on clustering genes related to DNA mismatch repair (DMR) by the distribution of gene-gene interactions with other genes. Gene-gene interactions are recorded as coefficients in an auto-logistic model for the co-expression of two genes, adjusting for copy number variation, methylation and protein activation. These coefficients are extracted from an online database, called Zodiac, computed based on The Cancer Genome Atlas (TCGA) data. We compare results with a variation of k-means clustering that is set up to cluster distributions, truncated NDP and a hierarchical clustering method. The proposed inference shows favorable performance, under simulated conditions and also in the real data sets.

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“…Many authors have applied the Bayesian non-parametric procedures to study various categories of biomarkers ranging from prognostic, predictive, phamacodynamic, and surrogate endpoints. For example, [4] studied the prognostic biomarkers and showed how they related to the clinical outcome using the Bayesian non-parametric procedures. Additionally, [3] studied the prognostic biomarkers using Bayesian parametric procedures, and finally [5] studied the surrogate endpoints using the Bayesian methods.…”

Section: Introductionmentioning

confidence: 99%

“…The study of [4] applied the Bayesian non-parametrics in modeling biological markers. In the study the model assumed measurements of the biomarkers were taken continuously before the subjects under study are introduced to treatment and after the patient has been given some treatment.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers

Peter¹,

Mbugua²,

Wanjoya³

2019

JDAIP

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The effect of treatment on patient's outcome can easily be determined through the impact of the treatment on biological events. Observing the treatment for patients for a certain period of time can help in determining whether there is any change in the biomarker of the patient. It is important to study how the biomarker changes due to treatment and whether for different individuals located in separate centers can be clustered together since they might have different distributions. The study is motivated by a Bayesian non-parametric mixture model, which is more flexible when compared to the Bayesian Parametric models and is capable of borrowing information across different centers allowing them to be grouped together. To this end, this research modeled Biological markers taking into consideration the Surrogate markers. The study employed the nested Dirichlet process prior, which is easily peaceable on different distributions for several centers, with centers from the same Dirichlet process component clustered automatically together. The study sampled from the posterior by use of Markov chain Monte carol algorithm. The model is illustrated using a simulation study to see how it performs on simulated data. Clearly, from the simulation study it was clear that, the model was capable of clustering data into different clusters.

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Bayesian Nonparametric Estimation of Targeted Agent Effects on Biomarker Change to Predict Clinical Outcome

Cited by 10 publications

References 43 publications

Bayesian nonparametric statistics: A new toolkit for discovery in cancer research

Bayesian nonparametric statistics: A new toolkit for discovery in cancer research

Clustering Distributions with the Marginalized Nested Dirichlet Process

Bayesian Non-Parametric Mixture Model with Application to Modeling Biological Markers

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