Introduction
Multiple myeloma (MM) is a cancer of plasma cells, with 3,900 cases and 1,700 deaths estimated in Canada in 2023. New therapies have improved survival, but MM remains incurable, leading to relapsed/refractory multiple myeloma (RRMM) [ 1 ]. RRMM significantly affects patients’ quality of life due to the disease and ongoing treatment’s physical, emotional, and financial burdens [ 2 ].
Chimeric antigen receptor (CAR) T-cell therapy offers a novel approach to RRMM by modifying T-cells to target cancer cells. This requires a complex administration process involving T-cell collection, modification and treatment administration. Compared to current alternative treatment options that require repeated treatment administration over multiple cycles and monitoring (hereafter referred to as “conventional therapies”), CAR-T therapy offers a potential one-time treatment (over a period of 3–4 months) and a longer treatment-free interval that may span several months or years before disease progression, potentially reducing the long-term treatment burden for heavily pre-treated patients. However, CAR-T therapy is also associated with unique side effects including cytokine release syndrome (CRS), neurotoxicity and B-cell aplasia, ranging from mild to life-threatening, and requiring close monitoring [ 3 – 5 ]. Furthermore, availability of CAR-T therapy is limited to selected specialized centres in Canada, presenting potential access barriers for rural patients and those otherwise distant from these centers.
While several studies examining treatment preferences of RRMM patients have been conducted in countries outside Canada – including the United States, United Kingdom, Germany, Italy, France, and Spain – there remains a pressing need for Canadian-specific research to understand the importance of these distinct treatment attributes to RRMM patients managed in the Canadian health care context [ 6 – 16 ]. Studies to date have generally found that efficacy attributes like overall survival and response rates are most important to patients, though treatment administration factors and side effects are also considered. However, these studies indicate that patients are willing to accept increased side effects and a more complex treatment administration if efficacy is strong [ 17 , 18 ].
Patient preference information plays a key role in patient-centered clinical decision-making, which is often a collaborative effort between patients, caregivers and healthcare professionals (HCPs) [ 19 ]. Patient input including treatment preference information can also provide an important contribution to Canadian health technology assessments (HTAs), that in turn, inform public drug reimbursement, particularly in the context of conditions for which the treatment decision is preference sensitive [ 20 ]. For example, Canada’s Drug Agency (CDA) explicitly identifies patient values as a key consideration for drug reimbursement recommendations in Canada [ 21 , 22 ]. When well-designed, quantitative patient preference studies have the potential to provide valuable, scientifically rigorous real-world evidence that can supplement or provide an alternative to qualitative evidence to inform these decisions.
A discrete choice experiment (DCE) is a quantitative research method used to elicit preferences by presenting participants with hypothetical scenarios or choices. In a DCE, respondents choose between alternatives with varying attributes and levels, allowing researchers to determine the importance of different factors in decision-making. This approach is valuable in healthcare for understanding patient preferences, revealing the trade-offs individuals are willing to make between aspects of care, such as efficacy, side effects, and administration [ 23 ].
As such, this study aimed to quantify the preferences of patients towards attributes of third-line or higher RRMM treatments using an online DCE survey. The attributes of the hypothetical treatment options shown to participants were designed to capture key characteristics of current RRMM treatments in the era of CAR-T therapy. In addition, the study quantified HCPs’ and caregivers’ perceptions of patients’ preferences towards RRMM treatment characteristics and compared these with the self-reported preferences of RRMM patients. The aims of this study were to inform clinical decision-making and health technology assessment in Canada.
Methods
Study design
An online survey study with a DCE component was designed to explore treatment preferences. The DCE survey was developed following best-practice guidelines, [ 23 , 24 ] and extensive involvement of a patient advocacy group, Myeloma Canada. Myeloma Canada was an active research partner throughout the study and contributed to all key stages of the study including attribute development, design of patient-facing materials, participant recruitment, interpretation and dissemination of results. Involvement of a physician partner with expertise in RRMM also ensured the relevance and comprehensibility of the survey across all participant groups. The survey was professionally translated into Canadian French and validated by Myeloma Canada. A full copy of the English-language survey is provided in Additional File 1.
This study was reviewed and approved by Pearl IRB (IRB ID: 22-ISPO-171).
Participants and recruitment
A target sample size of 100 patients, 50 caregivers, and 50 HCPs was determined based on recruitment feasibility. All participants were required to live in Canada and be able to read English or Canadian French. Patients and caregivers were recruited via Myeloma Canada. HCPs were recruited through a specialist recruitment vendor.
Additional eligibility criteria for each sample included:
Patients: (i) 18 years or older, (ii) self-reported diagnosis of MM and received at least 2 therapy regimens for MM including at least two of the following treatment types: proteasome inhibitor, immunomodulatory drug (IMiD), anti-CD38 antibody. Participants who received stem cell transplant must have relapsed. Caregivers: (i) 18 years or older, (ii) self-reported unpaid caregiver of an RRMM patient who has received at least two therapy regimens including current treatment. HCPs: (i) board-certified or board-eligible medical oncologist, hematologist or hematologist/oncologist, (ii), 3 or more years of experience in medical practice, (iii) at least 20% of professional time in previous 3 years spent on direct patient care, and (iv) managed treatment of at least five MM patients requiring therapy in past 3 months, including at least two RRMM patients.
Survey development
The complete survey consisted of five sections: (i) introduction to the survey, (ii) DCE component, (iii) task feedback, (iv) background questions, (v) sociodemographic questions.
Attribute and level selection
Attributes were identified using a multi-method approach and aimed to characterize key patient-relevant differences between CAR-T therapy and other RRMM treatments. Methods included: (i) targeted literature reviews of qualitative/mixed method studies examining treatment perceptions and preferences towards RRMM treatments and stated preference studies relating to RRMM treatments and CAR-T therapy used to treat any condition; (ii) interviews and a rating exercise with patients ( n = 5), caregivers ( n = 5) and HCPs ( n = 5) (see Macfarlane et al. for a full summary of findings) [ 25 ].
Attribute levels were determined based on a review of clinical trial and observational study data relating to the performance of CAR-T therapy and other RRMM treatments. The range of levels was carefully considered to reflect realistic scenarios while also allowing for the exploration of trade-offs.
Shortlisted attributes and levels were refined based on iterative feedback from Myeloma Canada, and cognitive debriefing interviews conducted with: (i) two patients, (ii) two caregivers of RRMM patients, and (iii) two HCPs experienced in RRMM management (see Additional File 2 for further details on attribute selection and refinement).
Five attributes were included in the final DCE (see Table 1 ). Attribute wording and descriptions were tailored for each sample group to reflect difference in choice context and to suit the target audience. To ensure accurate depiction of CAR-T therapy and other therapy treatment administration, interactive descriptions for both options were developed (see Additional File 1, pg. 25–26).
Table 1 Finalized attributes and their levels Attributes Levels Length of progression-free survival (HCP)/How long the treatment will keep the cancer from getting worse (Patients and caregivers) 6 months 12 months 24 months 48 months Chance of 10-year survival (HCP)/Chance of surviving 10 years (Patients and caregivers) 5% (5 in 100) will live for 10 years 15% (15 in 100) will live for 10 years 25% (25 in 100) will live for 10 years Chance of serious side effects occurring within 6 months of treatment 1% (1 in 100) will experience serious side effects within 6 months of treatment 5% (5 in 100) will experience serious side effects within 6 months of treatment 20% (20 in 100) will experience serious side effects within 6 months of treatment 35% (35 in 100) will experience serious side effects within 6 months of treatment Chance of long-term moderate side effects 25% (25 in 100) will experience long-term moderate side effects 50% (50 in 100) will experience long-term moderate side effects 75% (75 in 100) will experience long-term moderate side effects 95% (95 in 100) will experience long-term moderate side effects Treatment administration (HCP)/How the treatment is given (Patients and caregivers) a Conventional drug therapy Chimeric antigen receptor-modified T-cell therapy or CAR T-cell therapy a See Fig. 1 for an example of the short description displayed as part of the DCE questions. Additional File 1 shows the full description shown during the introduction to the DCE task
Table 1
Finalized attributes and their levels
| Attributes | Levels |
| Length of progression-free survival (HCP)/How long the treatment will keep the cancer from getting worse (Patients and caregivers) | 6 months 12 months 24 months 48 months |
| Chance of 10-year survival (HCP)/Chance of surviving 10 years (Patients and caregivers) | 5% (5 in 100) will live for 10 years 15% (15 in 100) will live for 10 years 25% (25 in 100) will live for 10 years |
| Chance of serious side effects occurring within 6 months of treatment | 1% (1 in 100) will experience serious side effects within 6 months of treatment 5% (5 in 100) will experience serious side effects within 6 months of treatment 20% (20 in 100) will experience serious side effects within 6 months of treatment 35% (35 in 100) will experience serious side effects within 6 months of treatment |
| Chance of long-term moderate side effects | 25% (25 in 100) will experience long-term moderate side effects 50% (50 in 100) will experience long-term moderate side effects 75% (75 in 100) will experience long-term moderate side effects 95% (95 in 100) will experience long-term moderate side effects |
| Treatment administration (HCP)/How the treatment is given (Patients and caregivers) a | Conventional drug therapy Chimeric antigen receptor-modified T-cell therapy or CAR T-cell therapy |
| a See Fig. 1 for an example of the short description displayed as part of the DCE questions. Additional File 1 shows the full description shown during the introduction to the DCE task |
DCE design
For each DCE choice task, participants were asked to choose between two hypothetical treatments (Treatment A or Treatment B) with different attribute levels (Table 2 ). Participants were shown a fixed scenario asking them to consider their own condition (patients), the condition of the person they care for (caregivers) or a hypothetical patient (HCPs). Feedback from the cognitive debriefing interviews indicated treatment preferences may vary based on the age and number of prior lines of therapy a person has received. These characteristics were expected to naturally vary across the final caregiver samples as participants based their responses on the experiences of the person they care for. For the HCP sample, the age and number of prior lines of therapy of the hypothetical patient in the HCPs’ choice scenario was varied randomly between HCPs (age of the hypothetical RRMM patient was varied between 40 and 90 years old and treatment lines was varied between 2 and 5). Table 2 shows example DCE choice task scenarios for each sample.
Table 2 Example choice task scenarios Study Subgroup Example Discrete Choice Task Patients Imagine your hematologist tells you your current treatment has begun to stop working and you need to switch to a new treatment to manage your multiple myeloma. After discussion with your hematologist, you are given two treatment options to choose from: treatment A and treatment B. If your hematologist presented you with the two treatment options below, would you prefer treatment A or treatment B? Please assume that these treatments are the same in any other way not mentioned below. Caregivers Imagine the hematologist of the person you care for tells them their current treatment has begun to stop working and they need to switch to a new treatment to manage their multiple myeloma. The hematologist suggests two treatment options to choose from: treatment A and treatment B. If the person you care for was presented with the two treatment options below, by their hematologist, would they prefer treatment A or treatment B? Please assume that these treatments are the same in any other way not mentioned below. Healthcare Practitioners Imagine you are consulting with a [randomized patient age between 40–90]-year old patient who was diagnosed with multiple myeloma about 4 years ago. The patient has received [random number between 1–5] prior lines of therapies which included anti-CD38 antibody, an immunomodulatory agent and a proteasome inhibitor. The patient recent clinical work-up shows that the myeloma has relapsed, and you must now recommend a new treatment line to the patient. If you presented this patient with the two treatment options below, would they prefer treatment A or treatment B? Please assume that these treatments are the same in any other way not mentioned below.
Table 2
Example choice task scenarios
| Study Subgroup | Example Discrete Choice Task |
| Patients | Imagine your hematologist tells you your current treatment has begun to stop working and you need to switch to a new treatment to manage your multiple myeloma. After discussion with your hematologist, you are given two treatment options to choose from: treatment A and treatment B. If your hematologist presented you with the two treatment options below, would you prefer treatment A or treatment B? Please assume that these treatments are the same in any other way not mentioned below. |
| Caregivers | Imagine the hematologist of the person you care for tells them their current treatment has begun to stop working and they need to switch to a new treatment to manage their multiple myeloma. The hematologist suggests two treatment options to choose from: treatment A and treatment B. If the person you care for was presented with the two treatment options below, by their hematologist, would they prefer treatment A or treatment B? Please assume that these treatments are the same in any other way not mentioned below. |
| Healthcare Practitioners | Imagine you are consulting with a [randomized patient age between 40–90]-year old patient who was diagnosed with multiple myeloma about 4 years ago. The patient has received [random number between 1–5] prior lines of therapies which included anti-CD38 antibody, an immunomodulatory agent and a proteasome inhibitor. The patient recent clinical work-up shows that the myeloma has relapsed, and you must now recommend a new treatment line to the patient. If you presented this patient with the two treatment options below, would they prefer treatment A or treatment B? Please assume that these treatments are the same in any other way not mentioned below. |
A D-efficient experimental design was used to generate 24 choice tasks with different combinations of attribute levels for each hypothetical treatment option using Ngene. To manage participant burden, choice tasks were split into two blocks of 12 tasks and each participant was randomly assigned to one block. The order of choice tasks was randomized between participants. Two non-experimental choice tasks were included: (i) a dominance test, to assess task understanding and rational decision-making, and (ii) a stability test, to check consistency of preferences. An example of a patient version of a choice task is shown in Fig. 1 . Fig. 1 Example choice task– patient version
Data analysis
Sociodemographic and clinical characteristics were analysed using standard descriptive statistics. DCE data were analyzed based on the random utility theory framework using multinomial logit (MNL) models. An MNL model with dummy-coded specification defined as a linear and additive function of the treatment’s attribute levels served as the reference model:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \:V\:=&\:{\alpha}_{1}\left[{Treatment}_{B}\right]+{\beta}_{1}\left[{PFS}_{12\:months}\right]\\&+{\beta}_{2}\left[{PFS}_{24\:months}\right]+{\beta}_{4}\left[{PFS}_{48\:months}\right]\\&+{\beta}_{5}\left[{Survival}_{15\%}\right]+{\beta}_{6}\left[{Survival}_{25\%}\right]\\&+{\beta}_{7}\left[{Serious\_SE}_{20\%}\right]+{\beta}_{8}\left[{Serious\_SE}_{5\%}\right]\:\\&+{\beta}_{9}\left[{Serious\_SE}_{1\%}\right]+\:{\beta}_{10}\left[{Longterm\_SE}_{75\%}\right]\\&+\:{\beta}_{11}\left[{Longterm\_SE}_{50\%}\right]+\:{\beta}_{12}\left[{Longterm\_SE}_{25\%}\right]\\&+\:{\beta}_{13}\left[{Admin}_{CAR\:T}\right]\end{aligned}$$\end{document}
Where α 1 is the alternative specific constant which controls for left-right bias and β 1–13 are the estimated marginal utilities that capture the effect of changes in each attribute on preferences and provide information on the extent to which participants prefer one level of an attribute over another. Model estimation employed z-tests to assess parameter significance, while model selection utilized goodness-of-fit statistics including Akaike information criterion (AIC), Bayesian information criterion (BIC), McFadden adjusted R2, and likelihood ratio tests. All statistical tests were two-sided with a significance level of 0.05, unless otherwise specified. All analysis was conducted in Stata 17 BE and/or R v 4.1.2.
MNL results were used to calculate behavioural measures to aid interpretation of the results including:
Relative attribute importance (RAI) expressed as percentages ranging from 0 to 100%, where a higher value indicates greater importance (i.e. a change in the attribute level has a larger impact on preference). The sum of RAI scores for all attributes is 100%. Benefit-risk trade-offs which indicate the minimum increase in progression-free survival (PFS) required to offset a 1% rise in the chance of serious side effects or a 1% risk in the chance of long-term moderate side effects, reflecting patients’ willingness to balance benefits and risks. Predicted preference shares, or treatment selection probability of different hypothetical treatment profiles described in terms of attributes and levels across the study population.
For the patient sample, exploratory subgroup analyses were also performed to explore how participant characteristics influence treatment preferences.
See Additional File 3 for a full analysis description.
Results
Sample characteristics
Data collection took place between September and November 2023. The final sample consisted of 101 patients, 46 unpaid caregivers, and 49 HCPs experienced in managing RRMM.
Demographics for patients and caregivers are shown in Table 3 . Approximately half were female (47%, 47/101) and the majority were white (92%, 90/101). The mean age was 68 years old (SD = 8.8), the average time since diagnosis was 7.8 years (SD = 7.3), and the average number of therapy lines received was 3 (SD = 1.3). No participants had received CAR-T therapy.
Most caregivers were female (72%, 33/46) and white (91%, 41/46) with a mean age of 64 years (SD = 12.1). The disease history of the patients they cared for was similar to that of the patients included within this study.
The final sample included participants from nine (patients) or eight (caregivers) of the 10 Canadian provinces (Table 3 ). Responses to additional descriptive survey questions are found in Additional File 4.
Table 3 Socio-demographic and clinical features of patients and caregivers Patients ( N = 101) Caregivers ( N = 46) Age (in years) Mean (SD) 67.9 (8.8) 63.7 (12.1) Range 45.0–89.0 23.0–80.0 Median [Q1; Q3] 69.0 [62.0; 74.0] 64.0 [58.2; 74.0] Sex Male 54 (53%) 12 (26%) Female 47 (47%) 33 (72%) Prefer not to answer 0 (0%) 1 (2%) Ethnicity a First Nations, Métis or Inuk (Inuit) b 3 (3%) 1 (2%) White 90 (92%) 41 (91%) South Asian 1 (1%) 2 (4%) Chinese 2 (2%) 0 (0%) Black 1 (1%) 0 (0%) Arab 1 (1%) 0 (0%) Japanese 1 (1%) 0 (0%) Prefer not to answer 2 (2%) 2 (4%) Province/territory living in British Columbia 18 (18%) 12 (26%) Alberta 17 (17%) 7 (15%) Saskatchewan 1 (1%) 0 (0%) Manitoba 4 (4%) 1 (2%) Ontario 42 (42%) 18 (39%) Quebec 14 (14%) 5 (11%) New Brunswick 3 (3%) 1 (2%) Nova Scotia 1 (1%) 1 (2%) Newfoundland and Labrador 1 (1%) 1 (2%) Years since MM diagnosis (caregiver for person they care for) Mean (SD) 7.8 (7.3) 7.3 (4.6) Range 0.4–51.9 0.5–18.4 Don’t know or prefer not to answer 1 (1%) 2 (4%) Line of therapy number currently on (caregiver for person they care for) Mean (SD) 3.1 (1.3) 3.5 (1.6) Range 2.0–8.0 2.0–8.0 Types of treatment taken to manage RRMM CAR T therapy 0 (0%) N/A Proteasome inhibitor 83 (82%) N/A Immunomodulatory drug (IMiD) 93 (92%) N/A Anti CD38 antibody 89 (88%) N/A Stem Cell Transplant 71 (70%) N/A Other 23 (23%) N/A Preferred role in treatment decision making Make the final treatment decision themselves 2 (2%) N/A Make the final treatment decision themselves after serious consideration of the doctor’s opinion 32 (32%) N/A Share responsibility for treatment decision with the doctor 56 (55%) N/A The doctor makes the final treatment decision, but seriously considers the patient’s opinion 9 (9%) N/A The doctor makes all treatment decisions 2 (2%) N/A Abbreviations : SD S tandard deviation, Q1 Quartile 1, Q3 Quartile 3, N/A Not applicable a Survey included full ethnicity responses as listed in the 2021 Canadian census. Tabulated results include only categories where the option was selected by at least one participant b Asked separately, hence ethnicity options sum to > 100%,
Table 3
Socio-demographic and clinical features of patients and caregivers
Abbreviations : SD S tandard deviation, Q1 Quartile 1, Q3 Quartile 3, N/A Not applicable a Survey included full ethnicity responses as listed in the 2021 Canadian census. Tabulated results include only categories where the option was selected by at least one participant b Asked separately, hence ethnicity options sum to > 100%,
| Patients ( N = 101) | Caregivers ( N = 46) | |
| Age (in years) | ||
| Mean (SD) | 67.9 (8.8) | 63.7 (12.1) |
| Range | 45.0–89.0 | 23.0–80.0 |
| Median [Q1; Q3] | 69.0 [62.0; 74.0] | 64.0 [58.2; 74.0] |
| Sex | ||
| Male | 54 (53%) | 12 (26%) |
| Female | 47 (47%) | 33 (72%) |
| Prefer not to answer | 0 (0%) | 1 (2%) |
| Ethnicity a | ||
| First Nations, Métis or Inuk (Inuit) b | 3 (3%) | 1 (2%) |
| White | 90 (92%) | 41 (91%) |
| South Asian | 1 (1%) | 2 (4%) |
| Chinese | 2 (2%) | 0 (0%) |
| Black | 1 (1%) | 0 (0%) |
| Arab | 1 (1%) | 0 (0%) |
| Japanese | 1 (1%) | 0 (0%) |
| Prefer not to answer | 2 (2%) | 2 (4%) |
| Province/territory living in | ||
| British Columbia | 18 (18%) | 12 (26%) |
| Alberta | 17 (17%) | 7 (15%) |
| Saskatchewan | 1 (1%) | 0 (0%) |
| Manitoba | 4 (4%) | 1 (2%) |
| Ontario | 42 (42%) | 18 (39%) |
| Quebec | 14 (14%) | 5 (11%) |
| New Brunswick | 3 (3%) | 1 (2%) |
| Nova Scotia | 1 (1%) | 1 (2%) |
| Newfoundland and Labrador | 1 (1%) | 1 (2%) |
| Years since MM diagnosis (caregiver for person they care for) | ||
| Mean (SD) | 7.8 (7.3) | 7.3 (4.6) |
| Range | 0.4–51.9 | 0.5–18.4 |
| Don’t know or prefer not to answer | 1 (1%) | 2 (4%) |
| Line of therapy number currently on (caregiver for person they care for) | ||
| Mean (SD) | 3.1 (1.3) | 3.5 (1.6) |
| Range | 2.0–8.0 | 2.0–8.0 |
| Types of treatment taken to manage RRMM | ||
| CAR T therapy | 0 (0%) | N/A |
| Proteasome inhibitor | 83 (82%) | N/A |
| Immunomodulatory drug (IMiD) | 93 (92%) | N/A |
| Anti CD38 antibody | 89 (88%) | N/A |
| Stem Cell Transplant | 71 (70%) | N/A |
| Other | 23 (23%) | N/A |
| Preferred role in treatment decision making | ||
| Make the final treatment decision themselves | 2 (2%) | N/A |
| Make the final treatment decision themselves after serious consideration of the doctor’s opinion | 32 (32%) | N/A |
| Share responsibility for treatment decision with the doctor | 56 (55%) | N/A |
| The doctor makes the final treatment decision, but seriously considers the patient’s opinion | 9 (9%) | N/A |
| The doctor makes all treatment decisions | 2 (2%) | N/A |
HCPs were mostly male (65%, 33/49) and half were white (46%, 23/49). They had a mean age of 49 years (SD = 7.9). HCPs reported treating or managing an average of 28 RRMM patients (SD = 25.3) in the previous three months. All HCPs had referred one or more individuals with a haematological malignancy to CAR-T therapy, however, no HCPS had referred an RRMM patient specifically. The final sample included participants from eight out of 10 Canadian provinces (Table 4 ). HCPs were also asked directly about their perceptions of CAR-T therapy. Their responses are found in Additional File 5.
Table 4 Socio-demographic characteristics and clinical experience of HCPs Characteristic N = 49 Age (in years) Mean (SD) 48.9 (7.9) Range 36.0–64.0 Median [Q1; Q3] 48.0 [43.0; 54.0] Sex Male 32 (65%) Female 16 (33%) Prefer not to answer 1 (2%) Ethnicity a First Nations, Métis or Inuk (Inuit) 0 (0%) White 23 (47%) Chinese 8 (16%) Arab 5 (10%) Southeast Asian 2 (4%) Japanese 1 (2%) Prefer not to answer 10 (20%) Years in current practice/profession Mean (SD) 16.0 (7.3) Percent of professional time spent on direct patient care in the previous 3 years Mean (SD) 86.7 (8.0) Number of patients with RRMM treated or managed in the previous 3 months Mean (SD) 27.6 (25.3) Range 2.0–115.0 Number of patients with hematologic malignancies referred for CAR T therapy 0 0 (0%) 1–3 17 (35%) 4–10 21 (43%) 11–20 9 (18%) More than 20 2 (4%) Abbreviations : SD S tandard deviation, Q1 Quartile 1, Q3 Quartile 3 a Survey included full ethnicity responses as listed in the 2021 Canadian census. Tabulated results include only categories where the option was selected by at least one participant
Table 4
Socio-demographic characteristics and clinical experience of HCPs
Abbreviations : SD S tandard deviation, Q1 Quartile 1, Q3 Quartile 3 a Survey included full ethnicity responses as listed in the 2021 Canadian census. Tabulated results include only categories where the option was selected by at least one participant
| Characteristic | N = 49 |
| Age (in years) | |
| Mean (SD) | 48.9 (7.9) |
| Range | 36.0–64.0 |
| Median [Q1; Q3] | 48.0 [43.0; 54.0] |
| Sex | |
| Male | 32 (65%) |
| Female | 16 (33%) |
| Prefer not to answer | 1 (2%) |
| Ethnicity a | |
| First Nations, Métis or Inuk (Inuit) | 0 (0%) |
| White | 23 (47%) |
| Chinese | 8 (16%) |
| Arab | 5 (10%) |
| Southeast Asian | 2 (4%) |
| Japanese | 1 (2%) |
| Prefer not to answer | 10 (20%) |
| Years in current practice/profession | |
| Mean (SD) | 16.0 (7.3) |
| Percent of professional time spent on direct patient care in the previous 3 years | |
| Mean (SD) | 86.7 (8.0) |
| Number of patients with RRMM treated or managed in the previous 3 months | |
| Mean (SD) | 27.6 (25.3) |
| Range | 2.0–115.0 |
| Number of patients with hematologic malignancies referred for CAR T therapy | |
| 0 | 0 (0%) |
| 1–3 | 17 (35%) |
| 4–10 | 21 (43%) |
| 11–20 | 9 (18%) |
| More than 20 | 2 (4%) |
DCE internal validity
Most participants across all samples passed the dominance test (to assess task understanding and rational decision-making) by selecting the objectively more desirable alternative (98%, 99/101 of patients; 98%, 45/46 caregivers; 94%, 46/49 HCPs). Likewise, the majority also passed the stability test (consistency in the repeated choice task), specifically, 84% (85/101) of patients, 85% (39/46) of caregivers, and 94% (46/49) of HCPs. These are within the typical range for a DCE study. In addition, all participants demonstrated trading behaviour between alternatives i.e., not selecting the same alternative throughout the choice tasks, indicating they were able to understand the DCE. See Additional File 6 for internal validity data.
DCE results
Preference for treatment characteristics
Preference weights for all samples are show in Fig. 2 . All attributes except treatment administration appeared to significantly impact the treatment decisions of participants and the direction of preference weights were as expected, implying, that all else being constant, patients preferred a treatment with a: (i) longer duration of PFS, (ii) higher chance of 10-year survival, (iii) lower chance of serious side effects, and (iv) lower chance of long-term of moderate side effects. Preference weights for other treatment and CAR-T therapy treatment administration profiles were not significantly different for any sample. Fig. 2 MNL preference weights
Relative Attribute Importance (RAI)
Relative attribute importance scores are shown in Fig. 3 . Overall, length of PFS was considered most important (35.4% of total preference share of patients) and chance of 10-year survival was the second most important attribute (26.0% of the total preference share of patients). Treatment administration was least important (1.2% of the total preference share of patients). Fig. 3 Relative attribute importance
The order and magnitude of attribute importance were similar to what caregivers and HCPs had predicted for patients. Comparison of sample groups using one-way ANOVA (analysis of variance) testing, indicated no significant differences in RAI scores estimated based on the self-reported preferences of patients and those estimated based on patient preferences predicted by the caregiver and HCP samples.
Benefit-risk trade-offs
Table 5 shows the trade-offs that participants were willing to make between the benefits and risks of treatment (see Additional File 7 for model results used for estimation). This is expressed as the minimum increase in length of PFS that participants would be willing to accept for a 1% increase in the risk of side effects associated with a treatment.
The estimated trade-offs demonstrate that, for every 1% increase in risk of serious side effects: (i) patients would require a minimum of 0.42 (95% CI: 0.24–0.60) month increase in the duration of PFS to offset the additional risk, and (ii) from the perspectives of caregivers and HCPs, patients would require a minimum of 0.53 (95% CI: 0.19–0.87) and 0.56 (95% CI: 0.31–0.80) months increase in PFS, respectively to offset the additional risk.
Likewise, patients considered an increase of 0.40 (95% CI: 0.29–0.52) months of PFS to be equivalent to each additional 1% increase in long-term moderate side effects associated with a treatment. Estimates from the caregiver and HCP samples were similar in magnitude.
Table 5 Minimum acceptable increase in PFS (months) Patients ( N = 101) Caregivers ( N = 46) HCPs ( N = 49) Increase in PFS (SE) 95% CI Increase in PFS (SE) 95% CI Increase in PFS (SE) 95% CI 1% increase in chance of serious side effects within 6 months of treatment 0.424*** (0.092) 0.244–0.604 0.526** (0.174) 0.185 − 0.867 0.555*** (0.124) 0.313–0.798 1% increase in chance of long-term moderate side effects 0.401*** (0.058) 0.287 − 0.515 0.402*** (0.109) 0.188–0.615 0.360*** (0.074) 0.216–0.505 Abbreviations : CI Confidence interval, HCP Health care professional, PFS Progression-free survival, SE Standard error ** p -value< 0.05, *** p -value <0.01
Table 5
Minimum acceptable increase in PFS (months)
Abbreviations : CI Confidence interval, HCP Health care professional, PFS Progression-free survival, SE Standard error ** p -value< 0.05, *** p -value <0.01
| Patients ( N = 101) | Caregivers ( N = 46) | HCPs ( N = 49) | ||||
| Increase in PFS (SE) | 95% CI | Increase in PFS (SE) | 95% CI | Increase in PFS (SE) | 95% CI | |
| 1% increase in chance of serious side effects within 6 months of treatment | 0.424*** (0.092) | 0.244–0.604 | 0.526** (0.174) | 0.185 − 0.867 | 0.555*** (0.124) | 0.313–0.798 |
| 1% increase in chance of long-term moderate side effects | 0.401*** (0.058) | 0.287 − 0.515 | 0.402*** (0.109) | 0.188–0.615 | 0.360*** (0.074) | 0.216–0.505 |
Predicted preference shares
Three scenarios were explored to show changes in efficacy and side effects for different hypothetical treatments impacts overall acceptability (Fig. 4 ). Fig. 4 Predicted uptake probability
In scenario 1, treatment A was a CAR-T therapy with a longer PFS (12 month vs. 6 months) and lower risk of long-term moderate side effects (50% vs. 75%) compared to a conventional treatment administration option with a lower risk of serious side effects within 6 months of treatment (5% vs. 35%) and an equal chance of 10-year survival (15%). In this scenario, all samples did not have a significant preference towards either treatment.
In scenario 2, treatment B remained unchanged while for treatment A, the length of PFS increased from 12 months to 48 months and the chance of 10-year survival was improved from 15 to 25%. In this scenario, treatment A became the preferred option for all three samples (86.9% patient-reported, 77.1% caregiver-reported, 89.8% HCP-reported).
In scenario 3, treatment B remained unchanged while for treatment A, the improvements in chance of 10-year survival and length of PFS in treatment A were maintained as per in Scenario 2, but the risk of long-term moderate side effects increased from 50 to 75%. In this scenario, treatment A remained the preferred option across all samples.
Patient subgroup analyses
Exploratory subgroup analysis indicated patient characteristics may influence their treatment preferences. Specifically, participants ≤ 69 years and those who had undergone fewer lines of treatment (3 or less) placed more importance on length of PFS than older or more heavily pretreated patients (see Additional File 8 for full results).
Discussion
To our knowledge, this is the first study to quantify the treatment preferences of Canadian RRMM patients and the first to explore HCP and caregiver perceptions of the preferences of these patients, building on recent qualitative findings highlighting the experiences of Canadian MM patients and their caregivers [ 26 ].
Across all samples, treatment decisions were primarily driven by efficacy measures, specifically PFS length and the chance of 10-year survival. Long-term moderate side effects were more concerning than serious short-term side effects, whilst treatment administration profile (CAR-T therapy versus conventional treatment) was the least important attribute.
These findings have several implications. High agreement between self-reported preferences of patients, and caregiver and HCP perceptions indicate a good understanding of patient priorities, supporting shared decision-making and patient-centred care for this complex condition. This study complements and extends prior findings, which similarly demonstrated that caregivers and HCPs align with patient priorities, particularly on PFS [ 17 , 18 , 26 – 28 ].
The prioritization of PFS by patients may have important implications for assessing treatment efficacy in clinical trials. While PFS is primarily used as a surrogate endpoint when overall survival (OS) is impractical due to lengthy follow-up, this study found that PFS was as crucial to decision-making as OS, suggesting that treatments extending PFS - even without OS impact– may be valued by RRMM patients. Previous studies on PFS valuation by cancer patients have shown varied results, mainly due to difference in PFS descriptions and whether OS was considered in trade-offs [ 29 ]. In this study, PFS was described as a deterministic outcome while long-term OS was expressed as a probability. Considering this, participants’ preference for PFS over OS may reflect their preference for an expected gain over a probabilistic one. A survey-based study of ovarian cancer trials similarly found ovarian cancer patients prioritized PFS over OS, especially if lower toxicity was involved, aligning with our findings [ 30 ].
Exploratory subgroup analysis suggests that some subgroups of RRMM patients, including younger individuals or those with less advanced disease may be open to more aggressive, higher risk treatments if they offer greater survival. However, it is important to note that subgroup analyses in this study are largely exploratory and limited by the small sample size. Nevertheless, these differences highlight the importance of individualized care and patient input in treatment decisions.
DCE findings suggest that patients prioritize treatments with better efficacy and safety profiles, regardless of the administration method. In our study, ‘CAR-T therapy’ was defined as a one-time, multi-step treatment over three-to-four months at a specialized hospital, often requiring a week-long stay, with one-to-three months of close post-infusion monitoring and possible years of treatment-free intervals. ‘Conventional treatment’ was described as involving daily, weekly, or monthly medication over multiple cycles, administered at various locations without overnight stays, with regular follow-up monitoring and potential breaks between cycles. The observed indifference between these two administration methods suggests a willingness to endure aspects of CAR-T therapy that may be viewed as less favourable in return for its perceived benefits. However, the need to travel or relocate for treatment was identified as a significant potential barrier during descriptive survey questions (See Additional File 4), suggesting that improving geographic accessibility could greatly increase CAR-T therapy’s acceptability for patients and caregivers.
The findings of this study demonstrate a favourable view of CAR-T therapy by RRMM patients, caregivers and HCPs compared to conventional treatments when considering the benefit-risk profile of both. 29 Differences in the described safety profiles of the two treatments support CAR-T therapy’s acceptability; as a one-time treatment, CAR-T therapy map help avoid cumulative or cyclical side effects seen with continuous conventional care. Clinical evidence suggests that although CAR-T therapy is associated with long-term toxicities, occurrence appears to be lower than with conventional treatments [ 31 ]. While the risk of serious adverse events arising from CAR-T therapy, specifically CRS, is widely considered an uptake barrier [ 25 ], our study indicated patients were more concerned with longer-term moderate side effects than short-term serious ones at rates observed in clinical trials [ 31 ]. Based on the benefit risk trade-offs results, patients considered a minimum gain of 4 months in PFS as sufficient to make CAR-T therapy worthwhile if the chance of short-term serious side effects increased by 10% compared to conventional treatments.
The findings herein can be compared to other stated preference studies on RRMM treatments as well as CAR-T therapy in different populations. In a similar study using DCE methodology in RRMM patients in several different countries, Thomas et al. similarly noted the importance of treatment efficacy, with overall response rate and survival being prioritized, aligning with results from studies in other cancers like relapsed/refractory diffuse large B-cell lymphoma [ 18 , 32 ]. However, in contrast to the current study, they found a preference for other RRMM treatments over CAR-T therapy. This discrepancy could be due to differences in how treatment options were described; in the present study we included detailed descriptions of each treatment option whereas Thomas et al. did not. Our study found that the risk of long-term moderate side effects was less preferable to short-term serious side effects, which aligns with previous work by Janssens et al. in European RRMM patients, where individuals prioritized avoiding issues like mobility and cognitive problems over more immediate life-threatening side effects [ 11 ]. In contrast, Postmus et al. found UK RRMM patients prioritized severe side effects over moderate ones, potentially due to the higher risk levels they used (20–80%) compared to this study’s maximum of 35% [ 12 ]. The discrepancy in risk levels may explain the varying importance attributed to side effects between studies.
This study has several limitations that should be considered. The small sample size across all three participant groups limited possibility of exploring preference heterogeneity using more advanced analysis or conducting more subgroup analyses. MNL models used to analyse data in this study assume preferences are homogenous across the population. Generalizability of the study findings may be affected by the convenience sampling; study sample consist of primarily white participants who were also active members of the Myeloma Canada network. Additionally, there is potential for hypothetical bias as participants were asked to consider hypothetical treatments that may not align with real-life decisions. To enhance internal and external validity, the study employed a comprehensive development approach including ongoing collaboration with Myeloma Canada and cognitive debriefing interviews with the target population to validate the survey instrument. Overall, data quality assessments of DCE results demonstrated high levels of understanding and engagement from patient and caregiver respondents. This shows that, apart from improving the quality of the research, collaboration with Myeloma Canada also ensured that the study produced meaningful results which highlights the relevance of the research finding to the myeloma community. Future research with larger representative samples and real-world decision scenarios could further validate and extend these findings.
In conclusion, this study examines treatment preferences of Canadian RRMM patients, comparing CAR-T therapy and other therapies using DCE methodology. Findings reveal that patients prioritize efficacy, particularly PFS, and are willing to accept higher side effect risks for improved survival, with preferences varying by age and treatment history. While patients were indifferent between CAR-T therapy and other treatment options based on the method of treatment administration alone, CAR-T therapy emerged as a favourable option when considering its clinical benefits of potential increased survival and lower long-term side effect risks. As novel therapies continue to emerge, understanding patient preferences will be crucial in guiding treatment decisions and optimizing patient outcomes in RRMM.
Supplementary Information
Additional file 1.
Additional file 2.
Additional file 3.
Additional file 4.
Additional file 5.
Additional file 6.
Additional file 7.
Additional file 8.
Abbreviations
Chimeric antigen receptor (CAR) T-cell
cytokine release syndrome
Discrete Choice Experiment
healthcare professionals
Multiple Myeloma
Multinomial Logit Model
Progression-free survival
Relative attribute importance
Relapsed/refractory multiple myeloma
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Acknowledgements
This study was made possible with the support of Myeloma Canada and voluntary participation of patients with RRMM and their caregivers and HCPs. Medical writing assistance was provided by Sebastian Snow of Acaster Lloyd Consulting. The study was also supported by Ipsos. Additional acknowledgement to Ursula Mann, Ewa Wywial, Jennifer Obijaju, Omarwalid Noorzada, Zoe Bider, Michelle Robichaud and Paula Porumbaceanu.
Authors’ contributions
All authors contributed to the design of the study. JR contributed to the acquisition of the data. RH and GNC conducted the data analysis. All authors contributed to the interpretation of the data. All authors wrote and revised the manuscript critically for intellectual content and have read and approved the final manuscript.
Funding
The study was sponsored by Johnson & Johnson.
Data availability
The datasets generated and/or analysed during the current study are not publicly available as they are owned by the pharmaceutical company who funded the study.
Declarations
Ethics approval and consent to participate
This study was conducted in line with the principles of the Declaration of Helsinki. This study was reviewed and approved by Pearl IRB (IRB ID: 22-ISPO-171), an AAHRPP-accredited independent institutional review board. All participants provided informed consent prior to participating in the study.
Consent for publication
Not applicable.
Competing interests
RH and GNC are employees of Acaster Lloyd Consulting, Ltd., which was paid by Janssen Inc. for work related to this study. BM, KS, SS are employees of Janssen Inc., a Johnson & Johnson company and own Johnson & Johnson stock and stock options.
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