Patient preferences in the treatment of hemophilia A: A latent class analysis

PLoS One. 2021 Aug 23;16(8):e0256521. doi: 10.1371/journal.pone.0256521. eCollection 2021.

Abstract

Objective: To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A.

Methods: Best-Worst Scaling (BWS) Case 3 (four attributes: application type; bleeding frequencies/year; inhibitor development risk; thromboembolic events of hemophilia A treatment risk) conducted via online survey. Respondents chose the best and the worst option of three treatment alternatives. Data were analyzed via latent class model (LCM), allowing capture of heterogeneity in the sample. Respondents were grouped into a predefined number of classes with distinct preferences.

Results: The final dataset contained 57 respondents. LCM analysis segmented the sample into two classes with heterogeneous preferences. Preferences within each were homogeneous. For class 1, the most decisive factor was bleeding frequency/year. Respondents seemed to focus mainly on this in their choice decisions. With some distance, inhibitor development was the second most important. The remaining attributes were of far less importance for respondents in this class. Respondents in class 2 based their choice decisions primarily on inhibitor development, also followed, by some distance, the second most important attribute bleeding frequency/year. There was statistical significance (P < 0.05) between the number of annual bleedings and the probability of class membership.

Conclusions: The LCM analysis addresses heterogeneity in respondents' choice decisions, which helps to tailor treatment alternatives to individual needs. Study results support clinical and allocative decision-making and improve the quality of interpretation of clinical data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Choice Behavior
  • Hemophilia A
  • Humans
  • Latent Class Analysis
  • Patient Preference*

Grants and funding

This study was financed by Roche Pharma AG (https://www.roche.de). Employees of the sponsor are listed as authors and were involved in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors received support for third-party editing assistance, provided by Roche Pharma AG, Grenzach-Wyhlen, Germany or GEB mbH, Germany. Prof. Dr. Mühlbacher is employed at Hochschule Neubrandenburg and GEB mbH and reports grants for studies, presentations and advisory boards (national and international) from Abbott, AbbVie, Actelion, Amgen, AstraZeneca, Baxter, Bayer, Boehringer Ingelheim, BPI, Bristol Myers Squibb, Credopard GmbH, Daiichi Sankyo, Eli Lilly, Genentech, Gilead Sciences, GlaxoSmithKline, Grünenthal, Insight Health, IQWiG, Ipsen Pharma, Janssen-Cilag, Johnson & Johnson, Merck, MSD, Merck KGaA, NICE, Novartis, Novo Nordisk, Pfizer, Roche, Sanofi-Aventis, Stallergenes, Shire, Steigerwald Arzneimittelwerk, VFA, ViiV Healthcare GmbH, Wyeth. Drs. Juhnke and Sadler are employed at Hochschule Neubrandenburg and report grants from GEB mbH during the conduct of the study. Dr. Lamprecht is an employee of Roche Pharma AG, Grenzach-Wyhlen, Germany. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The funder provided support in the form of a salary for BL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of BL is articulated in the ‘Author contributions’ section.