Evidence map›Paper›PMID 28872693›Full record

ArticleStatistics in medicine2017

Modeling continuous response variables using ordinal regression.

Qi Liu, Bryan E Shepherd, Chun Li, Frank E Harrell

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.

0numbers the graph read from it
0cells of the map it votes in
123citing 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.

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

123 citing papers in PubMed.

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63 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

4 authors.

Qi LiuDepartment of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.ORCID http://orcid.org/0000-0001-5326-3419
Bryan E ShepherdDepartment of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.
Chun LiDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.
Frank E HarrellDepartment of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.ORCID http://orcid.org/0000-0002-8271-5493

Funding

CCASAnet: Caribbean, Central and South America NetworkU01AI069923 · NIAID · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Pedro Enrique Cahn, Jessica L Castilho · 2006 to 2026
$41.6M
Statistical Methods for Ordinal Variables in HIV/AIDS StudiesR01AI093234 · NIAID · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Chun Li, Bryan Earl Shepherd · 2011 to 2026
$4.9M
NIAID NIH HHS R01 AI093234NIAID NIH HHS U01 AI069923
6 · The paper itself

Abstract

We study the application of a widely used ordinal regression model, the cumulative probability model (CPM), for continuous outcomes. Such models are attractive for the analysis of continuous response variables because they are invariant to any monotonic transformation of the outcome and because they directly model the cumulative distribution function from which summaries such as expectations and quantiles can easily be derived. Such models can also readily handle mixed type distributions. We describe the motivation, estimation, inference, model assumptions, and diagnostics. We demonstrate that CPMs applied to continuous outcomes are semiparametric transformation models. Extensive simulations are performed to investigate the finite sample performance of these models. We find that properly specified CPMs generally have good finite sample performance with moderate sample sizes, but that bias may occur when the sample size is small. Cumulative probability models are fairly robust to minor or moderate link function misspecification in our simulations. For certain purposes, the CPMs are more efficient than other models. We illustrate their application, with model diagnostics, in a study of the treatment of HIV. CD4 cell count and viral load 6 months after the initiation of antiretroviral therapy are modeled using CPMs; both variables typically require transformations, and viral load has a large proportion of measurements below a detection limit.

Indexed as

Models, StatisticalRegression AnalysisAnti-HIV AgentsCD4 Lymphocyte CountData Interpretation, StatisticalHIV InfectionsHumansLikelihood FunctionsProbabilityStatistics, NonparametricTreatment OutcomeViral LoadAnti-HIV Agentsnonparametric maximum likelihood estimationordinal regression modelrank-based statisticssemiparametric transformation model

Identifiers

PMID28872693
PMCPMC5675816

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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.