Patient Preferences for Multiple Myeloma (MM) Treatment: Interim Analysis of a Discrete Choice Experiment

Abstract Background: The growing importance of patient preferences in treatment decision-making in oncology is evidenced by the expanding role of patient-reported experience in both regulatory and reimbursement considerations of value. While recent introduction of new treatments for multiple myeloma (MM) have demonstrated longer time to progression and improved survival, specific regimen options still vary with respect to efficacy, safety, and dosing. Therefore, patients and providers must consider the trade-offs inherent in making treatment decisions. However, a lack of evidence exists describing patient reported preferences within the context of currently available regimens. To address this gap, this interim analysis of an ongoing study was conducted to examine patient preferences for MM treatments. Methods: A sequential mixed methods design, which incorporates both qualitative and quantitative phases, was utilized for this study. The qualitative phase identified content and language, via semi-structured interviews, to elucidate how patients understand and construct treatment-related factors, such as overall survival (OS), progression free survival (PFS), dosing, and tolerability. Results from the qualitative phase were subsequently used to inform the development of the quantitative survey. The online survey was sent to adults diagnosed with MM, who had received or were currently receiving first-line therapy (FL), second-line therapy (1PL), or third-line therapy (2PL) at the time of the survey (target N=200). Patients were recruited from targeted panels, advocacy partnerships, patient communities, and physician referrals from May to June 2018. The survey utilized a Discrete Choice Experiment (DCE) methodology to assess preferences and willingness to accept trade-offs among hypothetical treatments that varied on levels of specific attributes. Treatment attributes and levels were identified through literature review, current treatment guidelines, and clinical input. In the quantitative survey, patients were asked to rate the levels of each treatment attribute (based on a 5-point Likert scale, ranging from 1=very bad to 5=very good) and select which regimen they prefer when presented with two different hypothetical regimens (fixed choice exercise - Table 1) to identify trade-offs that patients were willing to make when selecting treatments. Descriptive statistics were used to characterize the interim survey data. Full DCE results, which will model additional treatment scenarios, will be based upon the complete sample. Results: In this interim analysis (n=74), the mean age was 63 years and 51% were male. The average time since diagnosis was 70 months. Twenty-five patients were on FL, 25 were on 1PL and 24 were on 2PL. Mean duration of most recent/current treatment at the time of survey completion was 16 months for FL, 15 months for 1PL, and 11 months for 2PL. On average, patients in earlier lines of therapy (FL and 1PL) indicated greater importance of OS than 2PL patients. The importance of tolerability was also greater for patients in earlier lines of therapy (FL and 1PL vs. 2PL). Results of the fixed choice exercise available at the time of this analysis are shown in Table 2. When efficacy (OS and PFS) were comparable, 92% of patients preferred the treatment profile with a lower dosing frequency over a 1 year period (21x) despite a longer infusion time (>5h) compared to a treatment profile with higher dosing frequency (78x) and shorter infusion time (<2h). Conclusion: Results from this interim analysis suggest that patient preferences for MM treatments may vary by treatment history. Additionally, when efficacy is similar, a significant number of patients place greater importance on dosing frequency than on the duration of treatment administration. Patients may consider treatment options holistically - e.g., convenience is not simply "chair-time," but rather also includes frequency of outpatient visits. This study provides insight into how patients with MM value and assess meaningful "benefit-risk" when making treatment decisions, which can be useful for facilitating physician-patient communications and shared decision making. Analysis of the complete study population and DCE will provide additional results on the relative importance of specific treatment attributes and preference for hypothetical treatment regimens that were not available for this analysis. Disclosures McKay: Janssen Scientific Affairs,LLC: Employment. Maiese:Janssen Scientific Affairs,LLC: Employment, Equity Ownership. Chiarappa:Janssen: Employment. Cambron-Mellott:Kantar Health: Employment. Maculaitis:Kantar Health: Employment. Alunni:Kantar Health: Employment. Raje:BMS: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Merck: Consultancy; Takeda: Consultancy; AstraZeneca: Research Funding; Research to Practice: Honoraria; Medscape: Honoraria; Amgen Inc.: Consultancy.

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Quantifying Patient Preferences for Treatment Outcomes in AML: A Discrete-Choice Experiment

Blood ◽

10.1182/blood-2018-99-113071 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 718-718

Author(s):

Daniel R. Richardson ◽

Jaein Seo ◽

B. Douglas Smith ◽

Elihu H. Estey ◽

Bernadette O'Donoghue ◽

...

Keyword(s):

Side Effects ◽

Drug Development ◽

Treatment Outcomes ◽

Patient Preferences ◽

Choice Experiment ◽

Event Free Survival ◽

Short Term ◽

Free Survival ◽

Trade Offs

Abstract Background: Treatment of acute myeloid leukemia (AML) remains a significant challenge: induction chemotherapy is associated with substantial toxicities, and most patients will eventually die related to their disease. While inducing complete remission (CR) has historically been the primary aim of most treatment regimens, it is unknown to what extent patients would be willing to trade-off the chance at remission for other outcomes such as a reduced toxicity profile. Clinicians routinely make such decisions for patients by selecting less intense regimens for elderly patients or those with significant comorbidities, or when remission may not be clinically relevant or likely achievable. Patient preferences for treatment outcomes often inform shared decision making at the bedside and are increasingly recognized by the FDA and industry as a critical aspect to incorporate into drug development. As part of a patient-focused drug development initiative led by The Leukemia & Lymphoma Society (LLS), we sought to quantify patient preferences for treatment outcomes in AML. Methods: In collaboration with diverse stakeholders (including patients, clinicians, industry representatives, and the LLS staff), a survey instrument was developed to quantify patient preferences for treatment outcomes in AML. A discrete-choice experiment (DCE), in which participants made choices between 9 pairs of hypothetical treatments, was utilized with five attributes: event-free survival, complete remission, time in hospital, short-term side effects, and long-term side effects. All attributes were tiered to three clinically relevant levels. A national survey was conducted and analyzed using a conditional logistic regression. Elicited preference weights, reflecting the desirability for individual attributes, were aggregated into preference estimates. Sub-group analysis compared outcomes based on remission status. Results: The survey was sent to 896 patients with AML who were identified through the LLS database. Three-hundred and twenty-two patients participated in the survey; 294 completed the DCE. Most patients were white (89.4%), married (74.7%), college-educated (74.4%), and privately-insured (76.9%). The majority of patients had received an allogeneic stem cell transplant (63.8%) and nearly all (95.0%) were in remission. The mean time since diagnosis was 8.0 years (range = 1 - 40). Mean age was 56.8 years (Standard Deviation = 12.4). Based on the DCE, CR was identified by patients to be the most important attribute (Preference estimates, 10% increase in CR = 1.05, Standard Error (SE) = 0.06), followed by severity of long-term side effects (1-step increase = -0.52, SE = 0.05), event-free survival (6 months increase = 0.29, SE = 0.02), severity of short-term side effects (1-step increase = -0.33, SE = 0.04), and time in hospital (1 month increase = -0.12, SE = 0.03). These preference estimates suggest that patients were willing to accept a 1-step increase in severity of short-term side effects for a 3% increase in chance at CR. Patients would be willing to accept a 5% decrease in the chance at CR for a 1-step decrease in long-term side effects. Patients equivalently valued an additional 3 more months in the hospital to a 3% increase in chance of CR. They valued a 10% increase in chance of CR as approximately the same as 22 months of event-free survival. No differences were seen between patients currently in remission and those not in remission. Patients found the questions easy to understand (79%), easy to answer (68%), and relevant to them (72%). Conclusions: In addition to demonstrating the feasibility of a DCE in eliciting patient preferences and creating the first large dataset of its kind, this study illustrates that AML patients are willing to make trade-offs regarding treatment outcomes. While patients most highly value the chance at CR, they were willing to forgo small increases in the probability of remission to improve other outcomes, especially long-term side effects. Demonstrating patients' willingness to make such trade-offs becomes critical as incremental gains in CR with novel treatments may come with significantly increased toxicities. These data will be important to inform shared decision making, drug development and approval. Prospective studies using DCE may be helpful to guide individual treatment recommendations. Figure. Figure. Disclosures Seo: Evidera/PPD: Employment. O'Donoghue:The Leukemia & Lymphoma Society: Employment. Bridges:The Leukemia & Lymphoma Society: Research Funding.

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Developing a Discrete Choice Experiment to Understand Patient Preferences in Resource-Limited Settings: a Six-Step Guide

10.1101/2020.10.05.20206557 ◽

2020 ◽

Author(s):

Sarah Moor ◽

Andrew K. Tusubira ◽

Ann R Akiteng ◽

Evelyn Hsieh ◽

Christine Ngaruiya ◽

...

Keyword(s):

Discrete Choice Experiment ◽

Discrete Choice ◽

Patient Preferences ◽

Choice Experiment ◽

Development Process ◽

Attribute Selection ◽

Attribute Level ◽

Resource Limited Settings ◽

Resource Limited ◽

Trade Offs

AbstractA discrete choice experiment (DCE) is a method to quantify preferences for goods and services in a population. Participants are asked to choose between sets of 2 hypothetical scenarios that differ in terms of particular characteristics. Their selections reveal the relative importance of each “attribute”, or characteristic, and the extent to which people consider trade-offs between characteristics. DCEs are increasingly used in healthcare and public health settings as they can inform the design of health-related interventions to achieve maximum impact. Specific efforts must be made in the development process to ensure relevance of DCEs to the communities in which they are administered. Herein, we build upon gaps in the prior literature by offering researchers a step-by-step process to guide DCE development for resource-limited settings, including detailed methodological considerations for each step and a specific actionable approach that we hope will simplify the process for other researchers. We present the 6 steps we followed to develop a DCE to evaluate patient preferences for management of hypertension and diabetes in rural Uganda. These steps are: 1) formative work; 2) attribute selection; 3) attribute level selection; 4) DCE design selection; 5) determination of attribute level combinations; and 6) assessment and enhancement of tool comprehensibility. We describe each of these steps in detail to ease the development process for researchers looking to develop locally contextualized, end-user-centric health interventions.

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A three month interim analysis of quality of life in patients with relapsing remitting multiple sclerosis in the UK: the Patient Reported Outcomes with Fingolimod in Local Experience (PROFILE) study

10.26226/morressier.59a3e8b7d462b8028d895a36 ◽

2017 ◽

Author(s):

Michel Kroes

Keyword(s):

Quality Of Life ◽

Interim Analysis ◽

Patient Reported Outcomes ◽

Local Experience ◽

Patient Reported ◽

Relapsing Remitting ◽

The Uk ◽

Relapsing Remitting Multiple Sclerosis ◽

Profile Study

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Patient-reported treatment satisfaction and choice of dosing frequency with biologic treatment for moderate to severe plaque psoriasis

Patient Preference and Adherence ◽

10.2147/ppa.s85773 ◽

2015 ◽

pp. 777 ◽

Cited By ~ 11

Author(s):

Mingliang Zhang ◽

Susan Brenneman ◽

Chureen Carter ◽

Breanna Essoi ◽

Kamyar Farahi ◽

...

Keyword(s):

Treatment Satisfaction ◽

Biologic Treatment ◽

Patient Reported ◽

Dosing Frequency

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Patient preferences for specialist outpatient video consultations: A discrete choice experiment

Journal of Telemedicine and Telecare ◽

10.1177/1357633x211022898 ◽

2021 ◽

pp. 1357633X2110228

Author(s):

Centaine L Snoswell ◽

Anthony C Smith ◽

Matthew Page ◽

Liam J Caffery

Keyword(s):

General Practitioner ◽

Discrete Choice Experiment ◽

Discrete Choice ◽

Patient Preferences ◽

Choice Experiment ◽

Healthcare Delivery ◽

General Practitioner Practice ◽

Specialist Care ◽

Time Away ◽

Out Of Pocket Costs

Introduction Telehealth has been shown to improve access to care, reduce personal expenses and reduce the need for travel. Despite these benefits, patients may be less inclined to seek a telehealth service, if they consider it inferior to an in-person encounter. The aims of this study were to identify patient preferences for attributes of a healthcare service and to quantify the value of these attributes. Methods We surveyed patients who had taken an outpatient telehealth consult in the previous year using a survey that included a discrete choice experiment. We investigated patient preferences for attributes of healthcare delivery and their willingness to pay for out-of-pocket costs. Results Patients ( n = 62) preferred to have a consultation, regardless of type, than no consultation at all. Patients preferred healthcare services with lower out-of-pocket costs, higher levels of perceived benefit and less time away from usual activities ( p < 0.008). Most patients preferred specialist care over in-person general practitioner care. Their order of preference to obtain specialist care was a videoconsultation into the patient’s local general practitioner practice or hospital ( p < 0.003), a videoconsultation into the home, and finally travelling for in-person appointment. Patients were willing to pay out-of-pocket costs for attributes they valued: to be seen by a specialist over videoconference ($129) and to reduce time away from usual activities ($160). Conclusion Patients value specialist care, lower out-of-pocket costs and less time away from usual activities. Telehealth is more likely than in-person care to cater to these preferences in many instances.

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Discrete choice experiment for eliciting preference for health services for patients with ALS and their informal caregivers

BMC Health Services Research ◽

10.1186/s12913-021-06191-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Katy Tobin ◽

Sinead Maguire ◽

Bernie Corr ◽

Charles Normand ◽

Orla Hardiman ◽

...

Keyword(s):

Health Services ◽

Discrete Choice Experiment ◽

Discrete Choice ◽

Choice Experiment ◽

Strong Preference ◽

Multidisciplinary Teams ◽

Relative Importance ◽

Trade Offs ◽

Als Patients ◽

The Impact

Abstract Background Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative condition with a mean life expectancy of 3 years from first symptom. Understanding the factors that are important to both patients and their caregivers has the potential to enhance service delivery and engagement, and improve efficiency. The Discrete Choice Experiment (DCE) is a stated preferences method which asks service users to make trade-offs for various attributes of health services. This method is used to quantify preferences and shows the relative importance of the attributes in the experiment, to the service user. Methods A DCE with nine choice sets was developed to measure the preferences for health services of ALS patients and their caregivers and the relative importance of various aspects of care, such as timing of care, availability of services, and decision making. The DCE was presented to patients with ALS, and their caregivers, recruited from a national multidisciplinary clinic. A random effects probit model was applied to estimate the impact of each attribute on a participant’s choice. Results Patients demonstrated the strongest preferences about timing of receiving information about ALS. A strong preference was also placed on seeing the hospice care team later rather than early on in the illness. Patients also indicated their willingness to consider the use of communication devices. Grouping by stage of disease, patients who were in earlier stages of disease showed a strong preference for receipt of extensive information about ALS at the time of diagnosis. Caregivers showed a strong preference for engagement with healthcare professionals, an attribute that was not prioritised by patients. Conclusions The DCE method can be useful in uncovering priorities of patients and caregivers with ALS. Patients and caregivers have different priorities relating to health services and the provision of care in ALS, and patient preferences differ based on the stage and duration of their illness. Multidisciplinary teams must calibrate the delivery of care in the context of the differing expectations, needs and priorities of the patient/caregiver dyad.

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Patient preferences for use of virtual consultations in an orthopaedic rehabilitation setting: Results from a discrete choice experiment

Journal of Health Services Research & Policy ◽

10.1177/13558196211035427 ◽

2021 ◽

pp. 135581962110354

Author(s):

Anthony W Gilbert ◽

Emmanouil Mentzakis ◽

Carl R May ◽

Maria Stokes ◽

Jeremy Jones

Keyword(s):

Conceptual Model ◽

Discrete Choice Experiment ◽

Discrete Choice ◽

Patient Preferences ◽

Choice Experiment ◽

Best Practice ◽

Time Of Day ◽

Problem Area ◽

Fractional Factorial ◽

The Impact

Objective Virtual Consultations may reduce the need for face-to-face outpatient appointments, thereby potentially reducing the cost and time involved in delivering health care. This study reports a discrete choice experiment (DCE) that identifies factors that influence patient preferences for virtual consultations in an orthopaedic rehabilitation setting. Methods Previous research from the CONNECT (Care in Orthopaedics, burdeN of treatmeNt and the Effect of Communication Technology) Project and best practice guidance informed the development of our DCE. An efficient fractional factorial design with 16 choice scenarios was created that identified all main effects and partial two-way interactions. The design was divided into two blocks of eight scenarios each, to reduce the impact of cognitive fatigue. Data analysis were conducted using binary logit regression models. Results Sixty-one paired response sets (122 subjects) were available for analysis. DCE factors (whether the therapist is known to the patient, duration of appointment, time of day) and demographic factors (patient qualifications, access to equipment, difficulty with activities, multiple health issues, travel costs) were significant predictors of preference. We estimate that a patient is less than 1% likely to prefer a virtual consultation if the patient has a degree, is without access to the equipment and software to undertake a virtual consultation, does not have difficulties with day-to-day activities, is undergoing rehabilitation for one problem area, has to pay less than £5 to travel, is having a consultation with a therapist not known to them, in 1 weeks’ time, lasting 60 minutes, at 2 pm. We have developed a simple conceptual model to explain how these factors interact to inform preference, including patients’ access to resources, context for the consultation and the requirements of the consultation. Conclusions This conceptual model provides the framework to focus attention towards factors that might influence patient preference for virtual consultations. Our model can inform the development of future technologies, trials, and qualitative work to further explore the mechanisms that influence preference.

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