Evidence map›Paper›PMID 25328363›Full record

ArticleACM transactions on modeling and computer simulation : a publication of the Association for Computing Machinery2013

Massive parallelization of serial inference algorithms for a complex generalized linear model.

Marc A Suchard, Shawn E Simpson, Ivan Zorych, Patrick Ryan, David Madigan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACM transactions on modeling and computer simulation : a publication of the Association for Computing Machinery, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 6 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

90 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  17. Assessing Covariate Balance with Small Sample Sizes.medRxiv : the preprint server for health sciences · 2025
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30 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Marc A SuchardDepartment of Biomathematics University of California, Los Angeles, CA, USA ; Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA ; Department of Biostatistics, UCLA School of Public Health, University of California, Los Angeles, CA, USA.
Shawn E SimpsonDepartment of Statistics, Columbia University, New York, NY, USA.
Ivan ZorychDepartment of Statistics, Columbia University, New York, NY, USA.
Patrick RyanJohnson & Johnson Pharmaceutical Research and Development Titusville, NJ, USA.
David MadiganDepartment of Statistics, Columbia University, New York, NY, USA.

Funding

Genomics, GPUs, and Next Generation Computational StatisticsR01HG006139 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SOBEL, ERIC · 2011 to 2023
$5.1M
Bayesian Joint Estimation of Alignment and PhylogenyR01GM086887 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUCHARD, MARC A. · 2008 to 2012
$1.5M
NHGRI NIH HHS R01 HG006139NIGMS NIH HHS R01 GM086887
6 · The paper itself

Abstract

Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this paper we show how high-performance statistical computation, including graphics processing units, relatively inexpensive highly parallel computing devices, can enable complex methods in large databases. We focus on optimization and massive parallelization of cyclic coordinate descent approaches to fit a conditioned generalized linear model involving tens of millions of observations and thousands of predictors in a Bayesian context. We find orders-of-magnitude improvement in overall run-time. Coordinate descent approaches are ubiquitous in high-dimensional statistics and the algorithms we propose open up exciting new methodological possibilities with the potential to significantly improve drug safety.

Identifiers

PMID25328363
PMCPMC4201181

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Registered trials

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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.