Evidence map›Paper›PMID 25647419›Full record

ArticleJournal of pain and symptom management2015

Latent Class Analysis Reveals Distinct Subgroups of Patients Based on Symptom Occurrence and Demographic and Clinical Characteristics.

Christine Miaskowski, Laura Dunn, Christine Ritchie, Steven M Paul, Bruce Cooper, Bradley E Aouizerat, Kimberly Alexander, Helen Skerman, Patsy Yates

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of pain and symptom management, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 2 pooled it
4.5field-weighted citation impact, top 4% of its field
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

72 citing papers in PubMed, 2 syntheses or guidelines pooled it, 119 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Characterization of multidimensional dyspnea in advanced lung cancer with moderate to severe dyspnea-related functional impairment: a latent profile analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
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  4. Patient Engagement With an Automated Telephone Symptom Management Intervention: Predictors and Outcomes.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2020
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  11. A machine learning approach to profiling anxiety risk among cancer patients on treatment.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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12 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

9 authors at 2 institutions in 2 countries.

Christine MiaskowskiSchool of Nursing, University of California, San Francisco, California, USA. Electronic address: chris.miaskowski@nursing.ucsf.edu.
Laura DunnSchool of Medicine, University of California, San Francisco, California, USA.
Christine RitchieSchool of Medicine, University of California, San Francisco, California, USA.
Steven M PaulSchool of Nursing, University of California, San Francisco, California, USA.
Bruce CooperSchool of Nursing, University of California, San Francisco, California, USA.
Bradley E AouizeratSchool of Nursing, University of California, San Francisco, California, USA; Institute for Human Genetics, University of California, San Francisco, California, USA.
Kimberly AlexanderSchool of Nursing, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
Helen SkermanSchool of Nursing, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
Patsy YatesSchool of Nursing, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
University of California, San Francisco · USQueensland University of Technology · AU

Funding

FATIGUE, PAIN, AND SLEEP PROBLEMS DURING RADIATIONR01NR004835 · NINR · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI MIASKOWSKI, CHRISTINE A. · 1999 to 2002
$2.2M
Advancing the Science of Symptom Management in Oncology PatientsK05CA168960 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MIASKOWSKI, CHRISTINE A. · 2013 to 2017
$453k
NCI NIH HHS K05 CA168960NINR NIH HHS NR04853NINR NIH HHS R01 NR004835
6 · The paper itself

Abstract

contextCancer patients experience a broad range of physical and psychological symptoms as a result of their disease and its treatment. On average, these patients report 10 unrelieved and co-occurring symptoms.

objectivesThe aims were to determine if subgroups of oncology outpatients receiving active treatment (n = 582) could be identified based on their distinct experience with 13 commonly occurring symptoms; to determine whether these subgroups differed on select demographic and clinical characteristics; and to determine if these subgroups differed on quality of life (QOL) outcomes.

methodsDemographic, clinical, and symptom data from one Australian and two U.S. studies were combined. Latent class analysis was used to identify patient subgroups with distinct symptom experiences based on self-report data on symptom occurrence using the Memorial Symptom Assessment Scale.

resultsFour distinct latent classes were identified (i.e., all low [28.0%], moderate physical and lower psych [26.3%], moderate physical and higher psych [25.4%], and all high [20.3%]). Age, gender, education, cancer diagnosis, and presence of metastatic disease differentiated among the latent classes. Patients in the all high class had the worst QOL scores.

conclusionFindings from this study confirm the large amount of interindividual variability in the symptom experience of oncology patients. The identification of demographic and clinical characteristics that place patients at risk for a higher symptom burden can be used to guide more aggressive and individualized symptom management interventions.

Indexed as

AgedAgingAustraliaData Interpretation, StatisticalFemaleHumansMaleMiddle AgedNeoplasmsSelf ReportSex CharacteristicsSymptom AssessmentUnited Statesage differencesgender differenceslatent class analysisSymptom clusterssymptom profiles

Identifiers

PMID25647419
PMCPMC4492860
OpenAlexW2036236735

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.