Individual Trade-Offs Between Possible Benefits and Risks of Cancer Treatments: Results from a Stated Preference Study with Patients with Multiple Myeloma

Postmus, Douwe; Richard, Sarah; Bere, Nathalie; Valkenhoef, Gert van; Galinsky, Jayne; Low, Eric; Moulon, Isabelle; Mavris, Maria; Salmonsson, Tomas; Flores, Beatriz; Hillege, Hans L.; Pignatti, Francesco

doi:10.1634/theoncologist.2017-0257

Cited by 52 publications

(54 citation statements)

References 8 publications

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“…Two other studies that used ASW to elicit patient preferences have been published 26 , 27 . Both studies also observed substantial preference heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Patient-centered benefit-risk analysis of transcatheter aortic valve replacement

Marsh

Hawken²,

Brookes

et al. 2020

F1000Res

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Background: Aortic stenosis (AS) treatments include surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). Choosing between SAVR and TAVR requires patients to trade-off benefits and risks. The objective of this research was to determine which TAVR and SAVR outcomes patients consider important, collect quantitative data about how patients weigh benefits and risks, and evaluate patients’ preferences for SAVR or TAVR. Methods: Patients were recruited from advocacy organization databases. Patients self-reported as being diagnosed with AS, and as either having received AS treatment or as experiencing AS-related physical activity limitations. An online adapted swing weighting (ASW) method – a pairwise comparison of attributes – was used to elicit attribute tradeoffs from 219 patients. Survey data were used to estimate patients’ weights for AS treatment attributes, which were incorporated into a quantitative benefit-risk analysis (BRA) to evaluate patients’ preferences for TAVR and SAVR. Results: On average, patients put greater value on attributes that favored TAVR than SAVR. Patients’ valuation of the lower mortality rate, reduced procedural invasiveness, and quicker time to return to normal quality of life associated with TAVR, offset their valuation of the time over which SAVR has been proven to work. There was substantial heterogeneity in patients’ preferences. This was partly explained by age, with differences in preference observed between patients <60 years to those ≥60 years. A Monte Carlo Simulation found that 79.5% of patients prefer TAVR. Conclusions: Most AS patients are willing to tolerate sizable increases in clinical risk in exchange for the benefits of TAVR, resulting in a large proportion of patients preferring TAVR to SAVR. Further work should be undertaken to characterize the heterogeneity in preferences for AS treatment attributes. Shared decision-making tools based on attributes important to patients can support patients’ selection of the procedure that best meets their needs.

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“…Two other studies that used ASW to elicit patient preferences have been published 26 , 27 . Both studies also observed substantial preference heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Patient-centered benefit-risk analysis of transcatheter aortic valve replacement

Marsh

Hawken²,

Brookes

et al. 2020

F1000Res

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“…Despite these limitations, Q-TWiST analyses help to fill a gap in our understanding of the tradeoffs in clinical risk and benefit of various oncology therapies from a patient perspective. These tradeoffs are receiving increasing attention from regulators and physicians, including the European Medicine Agency, [23][24][25] the US Food and Drug Administration, 26 and the American Society of Clinical Oncology, 27 which formally define a net health benefit score based on clinical benefits and toxicities. The Q-TWiST approach makes it possible for any physician or patient to tailor the results to his or her own personal value placed on TOX, TWiST, and REL and adapt the survival analysis to reflect his or her own personal situation and beliefs.…”

Section: Discussionmentioning

confidence: 99%

A quality‐adjusted survival time without symptoms or toxicities analysis of glasdegib plus low‐dose cytarabine versus low‐dose cytarabine as initial therapy for acute myeloid leukemia in patients who are not considered candidates for intensive chemotherapy

Solem

Bell

Kwon

et al. 2020

Cancer

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Background In a randomized study, glasdegib (a hedgehog inhibitor) plus low‐dose cytarabine (LDAC) significantly prolonged survival in comparison with LDAC in patients with acute myeloid leukemia (AML). A quality‐adjusted time without symptoms of disease progression or toxicity (Q‐TWiST) approach was used to evaluate comparative quality‐adjusted survival. Methods Overall survival was partitioned into the following: time with any treatment‐emergent grade 3 or higher adverse events (TOX); time without symptoms of disease progression or toxicity (TWiST); and time after treatment discontinuation due to insufficient clinical response, relapse, or death time after progression (REL). Q‐TWiST was calculated by multiplying the restricted mean time in each state by respective utilities and then summing up the utility‐adjusted time. Results At 20 months of follow‐up, the survival probabilities for the glasdegib‐LDAC arm and the LDAC arm were 28.2% and 7.9%, respectively. Glasdegib‐LDAC patients (n = 78), in comparison with LDAC patients (n = 38), had significantly longer mean TWiST (+3.4 months; 95% confidence interval [CI], 1.8‐5.2 months) and TOX (+0.8 months; 95% CI, 0.1‐1.6 months) and longer but nonsignificant REL (+0.3 months; 95% CI, −1.9 to 2.3 months). Q‐TWiST was 4.0 months (95% CI, 2.1‐5.8 months) longer with glasdegib plus LDAC, and this translated into a 75% relative improvement in quality‐adjusted survival with respect to LDAC. Results were robust to the length of follow‐up (6‐24 months) and remained significant when all adverse events, regardless of grade, were included. Conclusions These results suggest that most of the survival benefit from glasdegib plus LDAC versus LDAC alone is TWiST, and this represents added time in relatively “good” health. These results support the clinical value of glasdegib plus LDAC as initial therapy for AML in patients for whom intensive chemotherapy is not an option.

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“…Development of theoretically sound benefit‐risk models is not easy and elicitation of stakeholder preferences for such models needs to account for critical study success factors, which are dependent on the chosen elicitation methodology . Preferences can be elicited efficiently with methods such as DCE, choice‐based matching, threshold technique, or swing weighting, and the most appropriate method is dependent on characteristics of the benefit‐risk assessment . Most elicitation methods result in preferences being measured on utility scales that can be difficult to interpret.…”

Section: Discussionmentioning

confidence: 99%

Quantifying Preferences in Drug Benefit‐Risk Decisions

Tervonen

Angelis

Hockley

et al. 2019

Clin Pharma and Therapeutics

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Benefit‐risk assessment is used in various phases along the drug lifecycle, such as marketing authorization and surveillance, health technology assessment (HTA), and clinical decisions, to understand whether, and for which patients, a drug has a favorable or more valuable profile with reference to one or more comparators. Such assessments are inherently preference‐based as several clinical and nonclinical outcomes of varying importance might act as evaluation criteria, and decision makers must establish acceptable trade‐offs between these outcomes. Different healthcare stakeholder perspectives, such as those from patients and healthcare professionals, are key for informing benefit‐risk trade‐offs. However, the degree to which such preferences inform the decision is often unclear as formal preference‐based evaluation frameworks are generally not used for regulatory decisions, and, if used, rarely communicated in HTA decisions. We argue that for better decisions, as well as for reasons of transparency, preferences in benefit‐risk decisions should more often be quantified and communicated explicitly.

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Individual Trade-Offs Between Possible Benefits and Risks of Cancer Treatments: Results from a Stated Preference Study with Patients with Multiple Myeloma

Abstract: The objectives of this study were to ascertain the treatment preferences of patients with multiple myeloma, considering benefits and risks of particular cancer treatments, and to illustrate how such data may be used to estimate patients' acceptance of new treatments.

Cited by 52 publications

References 8 publications

Patient-centered benefit-risk analysis of transcatheter aortic valve replacement

Patient-centered benefit-risk analysis of transcatheter aortic valve replacement

A quality‐adjusted survival time without symptoms or toxicities analysis of glasdegib plus low‐dose cytarabine versus low‐dose cytarabine as initial therapy for acute myeloid leukemia in patients who are not considered candidates for intensive chemotherapy

Quantifying Preferences in Drug Benefit‐Risk Decisions

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