Evidence map›Paper›PMID 36964606›Full record

ArticleAlzheimer's research & therapy2023

Therapeutic preference for Alzheimer's disease treatments: a discrete choice experiment with caregivers and neurologists.

George Dranitsaris, Quanwu Zhang, Lin Mu, Christopher Weyrer, Erik Drysdale, Peter Neumann, Alireza Atri, Amir Abbas Tahami Monfared

Open access · goldFull text read
In one paragraph

Article in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
5.7field-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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
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

8 authors at 6 institutions in 2 countries.

George DranitsarisDepartment of Public Health, Falk College, Syracuse University, 150 Crouse Dr., Syracuse, NY, 13244, USA. George.dranitsaris@gmail.com.
Quanwu ZhangEisai, Inc., Nutley, NJ, USA.
Lin MuBoston Consulting Group, Boston, MA, USA.
Christopher WeyrerBoston Consulting Group, Boston, MA, USA.
Erik DrysdaleBoston Consulting Group, Boston, MA, USA.
Peter NeumannTufts Medical Center, Boston, MA, USA.
Alireza AtriBanner Sun Health Research Institute, Sun City, AZ, USA.
Amir Abbas Tahami MonfaredEisai, Inc., Nutley, NJ, USA.
Boston Consulting Group (United States)Banner Sun Health Research Institute · USEisai (United States) · USMcGill University · CASyracuse University · USTufts Medical Center · US

Funding

Research Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI GONZALEZ HERNANDEZ, GRACIELA · 2001 to 2020
$32.5M
Research Education ComponentP30AG072980 · NIA · BANNER HEALTH · PI HEATHER Allyson BIMONTE-NELSON · 2021 to 2026
$24.9M
NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG072980
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is a major global health crisis in need of more effective therapies. However, difficult choices to optimize value-based care will need to be made. While identifying preferred therapeutic attributes of new AD therapies is necessary, few studies have explored how preferences may vary between the stakeholders. In this study, the trade-offs among key attributes of amyloid plaque-lowering therapies for AD were assessed using a discrete choice experiment (DCE) and compared between caregivers and neurologists.

methodsAn initial pilot study was conducted to identify the potentially relevant features of a new therapy. The DCE evaluated seven drug attributes: clinical effects in terms of delay in AD progression over the standard of care (SOC), variation in clinical effects, biomarker response (achieving amyloid plaque clearance on PET scan), amyloid-related imaging abnormalities-edema (ARIA-E), duration of therapy, need for treatment titration as well as route, and frequency of drug administration. Respondents were then randomly presented with 12 choice sets of treatment options and asked to select their preferred option in each choice set. Hierarchical Bayesian regression modeling was used to estimate weighted preference attributes, which were presented as mean partial utility scores (pUS), with higher scores suggesting an increased preference.

resultsBoth caregivers (n = 137) and neurologists (n = 161) considered clinical effects (mean pUS = 0.47 and 0.82) and a 5% incremental in ARIA-E (mean pUS =  - 0.26 and - 0.52) to be highly impactful determinants of therapeutic choice. In contrast, variation in clinical effects (mean pUS = 0.12 and 0.14) and treatment duration (mean pUS =  - 0.02 and - 0.13) were the least important characteristics of any new treatment. Neurologists' also indicated that subcutaneous drug delivery (mean pUS = 0.42 vs. 0.07) and administration every 4 weeks (mean pUS = 1.0 vs. 0.20) are highly desirable therapeutic features. Respondents were willing to accept up to a 9% increment in ARIA-E for one additional year of delayed progression.

conclusionsCaregivers and neurologists considered incremental clinical benefit over SOC and safety to be highly desirable qualities for a new drug that could clear amyloid plaques and delay clinical progression and indicated a willingness to accept incremental ARIA-E to achieve additional clinical benefits.

Indexed as

Alzheimer DiseaseBayes TheoremCaregiversChoice BehaviorHumansNeurologistsPilot ProjectsPlaque, AmyloidSuppurationAlzheimer’s diseaseCaregiver preferenceDiscrete choice experimentPhysician preference

Identifiers

PMID36964606
PMCPMC10037811
OpenAlexW4360866215

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read113
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.