Evidence map›Paper›PMID 37721558›Full record

ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2024

Determining patient preferences for the medical management of osteoporosis using conjoint analysis.

Xiaoyu Liu, Katherine E Makaroff, Christopher V Almario, Carine Khalil, So Yung Choi, Jeffrey R Curtis, Brennan M R Spiegel

Open access · hybridAbstract read
In one paragraph

Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Review
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

7 authors at 2 institutions in 1 country.

Xiaoyu Liu *Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA.
Katherine E Makaroff *Cedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA.
Christopher V AlmarioCedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA.
Carine KhalilCedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA.
So Yung ChoiBiostatistics and Bioinformatics Research Center, Cedars-Sinai Cancer, Los Angeles, CA, USA.
Jeffrey R CurtisDivision of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
Brennan M R SpiegelCedars-Sinai Center for Outcomes Research and Education (CS-CORE), Los Angeles, CA, USA. Brennan.Spiegel@cshs.org.ORCID http://orcid.org/0000-0002-4608-6896
Cedars-Sinai Medical Center · USUniversity of Alabama at Birmingham · US

Funding

Amgen CSR218205
6 · The paper itself

Abstract

We used conjoint analysis-a method that assesses complex decision making-to quantify patients' choices when selecting an osteoporosis therapy. While 60% of people prioritized medication efficacy when deciding among treatments, the remaining 40% highly valued factors other than efficacy, suggesting the need for personalized shared decision-making tools.

introductionIn this study, we aimed to examine patient decision-making surrounding osteoporosis medications using conjoint analysis.

methodsWe enrolled osteoporosis patients at an academic medical center to complete an online conjoint exercise which calculated each patient's relative importance score of 6 osteoporosis medication attributes (higher = greater relative importance in decision-making). We used latent class analysis to identify distinct segments of patients with similar choice patterns and then used logistic regression to determine if demographics and osteoporosis disease features were associated with latent class assignment.

resultsOverall, 304 participants completed the survey. The rank order of medication attributes by importance score was the following: efficacy at preventing hip fractures (accounted for 31.0% of decision making), mode of administration (17.5%); risk of serious side effects (16.6%); dose frequency (13.9%); efficacy at preventing spine fractures (12.5%); risk of non-serious side effects (8.4%). We found that 60.9% of the cohort prioritized medication efficacy as their top factor when selecting among the therapies. Being a college graduate, having stronger beliefs on the necessity of using medications for osteoporosis, and never having used osteoporosis medicines were the only factors associated with prioritizing medication efficacy for fracture prevention over the other factors in the decision-making process.

conclusionsWhile about 60% of patients prioritized efficacy when selecting an osteoporosis therapy, the remaining 40% valued other factors more highly. Furthermore, individual patient characteristics and clinical factors did not reliably predict patient decision making, suggesting that development and implementation of shared decision-making tools is warranted.

Indexed as

Fractures, BoneOsteoporosisHumansLogistic ModelsPatient PreferenceConjoint analysisOsteoporosisShared decision-makingTreatment decision making

Identifiers

PMID37721558
PMCPMC10787002
OpenAlexW4386817951

What OpenQuestion holds

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LicenceCC BY-NC
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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.