Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia.

William Barcella, Maria De Iorio, Stefano Favaro, Gary L Rosner

Abstract read

In one paragraph

Article in Biostatistics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

William BarcellaDepartment of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK.

Maria De IorioDepartment of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK.

Stefano FavaroDepartment of Economics and Statistics, University of Torino, Corso Unione Sovietica 218/bis, Torino 10134, Italy.

Gary L RosnerOncology Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, 550 N. Broadway, Suite 1103, Baltimore, MD 21205, USA.

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI Rosalie Wollett · 1985 to 2026

$208.6M

PAARK4Kids-Pharmacogenomics of Anticancer Agents Research in ChildrenU01GM092666 · NIGMS · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI RELLING, MARY V · 2010 to 2014

$8.9M

NCI NIH HHS P30 CA006973NIGMS NIH HHS U01 GM092666

6 · The paper itself

Abstract

We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete distributions. This process is defined by including a suitable Beta regression framework within a generalized Dirichlet process to induce dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation, and availability of efficient MCMC algorithms. The motivation for this work is the study of the impact of asparginage metabolism on lipid levels in a group of pediatric patients treated for acute lymphoblastic leukemia.

Indexed as

BiostatisticsData Interpretation, StatisticalModels, StatisticalAntineoplastic AgentsAsparaginaseBayes TheoremChildHumansOutcome Assessment, Health CarePrecursor Cell Lymphoblastic Leukemia-LymphomaStatistics, NonparametricAntineoplastic AgentsAsparaginase

Identifiers

PMID28968716

DOI10.1093/biostatistics/kxx042

PMCPMC6959469

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.