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Background: Individual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent population and patients who received neoadjuvant systemic therapy (NST)., Methods: Women, surgically treated for nonmetastatic breast cancer, diagnosed between 2012 and 2016, were selected from the Netherlands Cancer Registry. Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. Separate models were developed for NST patients. Discrimination and calibration were assessed by 100x bootstrap resampling., Results: In the non-NST and NST group, 49,631 and 10,154 patients were included, respectively. Age, mode of detection, histology, sublocalisation, grade, pT, pN, hormonal receptor status ± endocrine treatment, HER2 status ± targeted treatment, surgery ± immediate reconstruction ± radiation therapy, and chemotherapy were significant predictors for LRR and/or CBC in non-NST patients. For NST patients this was similar, but excluding (y)pT and (y)pN status, and including presence of ductal carcinoma in situ, axillary lymph node dissection and pathologic complete response. For non-NST patients, the Cox and RSF models were integrated in the online tool with 5-year AUCs of 0.77 (95%CI:0.77-0.77) and 0.68 (95%CI:0.67-0.68)] for LRR and CBC prediction, respectively. For NST patients, the RSF model performed best (AUCs 0.77 (95%CI:0.76-0.78) and 0.73 (95%CI:0.69-0.76) for LRR and CBC, respectively). Regarding calibration, observed-predicted differences were all <1 %., Conclusion: This INFLUENCE 3.0 models showed moderate performance in LRR and CBC prediction. The models have been made available as online tool to enable clinical decision support regarding personalised follow-up., Competing Interests: Declaration of coompeting interest Tom A. Hueting declares employment at Evidencio. The other authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: This study aimed to understand the importance of criteria describing methods (eg, duration, costs, validity, and outcomes) according to decision makers for each decision point in the medical product lifecycle (MPLC) and to determine the suitability of a discrete choice experiment, swing weighting, probabilistic threshold technique, and best-worst scale cases 1 and 2 at each decision point in the MPLC., Methods: Applying multicriteria decision analysis, an online survey was sent to MPLC decision makers (ie, industry, regulatory, and health technology assessment representatives). They ranked and weighted 19 methods criteria from an existing performance matrix about their respective decisions across the MPLC. All criteria were given a relative weight based on the ranking and rating in the survey after which an overall suitability score was calculated for each preference elicitation method per decision point. Sensitivity analyses were conducted to reflect uncertainty in the performance matrix., Results: Fifty-nine industry, 29 regulatory, and 5 health technology assessment representatives completed the surveys. Overall, "estimating trade-offs between treatment characteristics" and "estimating weights for treatment characteristics" were highly important criteria throughout all MPLC decision points, whereas other criteria were most important only for specific MPLC stages. Swing weighting and probabilistic threshold technique received significantly higher suitability scores across decision points than other methods. Sensitivity analyses showed substantial impact of uncertainty in the performance matrix., Conclusion: Although discrete choice experiment is the most applied preference elicitation method, other methods should also be considered to address the needs of decision makers. Development of evidence-based guidance documents for designing, conducting, and analyzing such methods could enhance their use., (Copyright © 2022. Published by Elsevier Inc.)

Objectives: The ICEpop Capability Measure for Adults (ICECAP-A) assesses 5 capabilities (stability, attachment, autonomy, achievement, and enjoyment) that are important to one's quality of life and might be an important addition to generic health questionnaires currently used in economic evaluations. This study aimed to develop a Dutch tariff of the Dutch translation of the ICECAP-A., Methods: The methods used are similar to those used in the development of the UK tariff. A profile case best-worst scaling task was presented to 1002 participants from the general Dutch population. A scale-adjusted latent class analysis was performed to test for preferences of ICECAP-A capabilities and scale heterogeneity., Results: A 3-preference class 2-scale class model with worst choice as scale predictor was considered optimal and was used to calculate the resulting tariff. Results indicated that the capabilities stability, attachment, and enjoyment were considered more important aspects of quality of life than autonomy and achievement. Additionally, improving capabilities from low to moderate levels had a larger effect on quality of life than improving capabilities that were already at a higher level., Conclusions: The ICECAP-A tariffs found in this study could be used in economic evaluations of healthcare interventions in The Netherlands., (Copyright © 2021 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc. Published by Elsevier Inc. All rights reserved.)

Objectives: This study aimed to provide detailed guidance on modeling approaches for implementing competing events in discrete event simulations based on censored individual patient data (IPD)., Methods: The event-specific distributions (ESDs) approach sampled times from event-specific time-to-event distributions and simulated the first event to occur. The unimodal distribution and regression approach sampled a time from a combined unimodal time-to-event distribution, representing all events, and used a (multinomial) logistic regression model to select the event to be simulated. A simulation study assessed performance in terms of relative absolute event incidence difference and relative entropy of time-to-event distributions for different types and levels of right censoring, numbers of events, distribution overlap, and sample sizes. Differences in cost-effectiveness estimates were illustrated in a colorectal cancer case study., Results: Increased levels of censoring negatively affected the modeling approaches' performance. A lower number of competing events and higher overlap of distributions improved performance. When IPD were censored at random times, ESD performed best. When censoring occurred owing to a maximum follow-up time for 2 events, ESD performed better for a low level of censoring (ie, 10%). For 3 or 4 competing events, ESD better represented the probabilities of events, whereas unimodal distribution and regression better represented the time to events. Differences in cost-effectiveness estimates, both compared with no censoring and between approaches, increased with increasing censoring levels., Conclusions: Modelers should be aware of the different modeling approaches available and that selection between approaches may be informed by data characteristics. Performing and reporting extensive validation efforts remains essential to ensure IPD are appropriately represented., (Copyright © 2021 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc. Published by Elsevier Inc. All rights reserved.)

Objectives: An important aim of follow-up after primary breast cancer treatment is early detection of locoregional recurrences (LRR). This study compares 2 personalized follow-up scheme simulations based on LRR risk predictions provided by a time-dependent prognostic model for breast cancer LRR and quantifies their possible follow-up efficiency., Methods: Surgically treated early patients with breast cancer between 2003 and 2008 were selected from the Netherlands Cancer Registry. The INFLUENCE nomogram was used to estimate the 5-year annual LRR. Applying 2 thresholds, they were defined according to Youden's J-statistic and a predefined follow-up sensitivity of 95%, respectively. These patient's risk estimations served as the basis for scheduling follow-up visits; 2 personalized follow-up schemes were simulated. The number of potentially saved follow-up visits and corresponding cost savings for each follow-up scheme were compared with the current Dutch breast cancer guideline recommendation and the observed utilization of follow-up on a training and testing cohort., Results: Using LRR risk-predictions for 30 379 Dutch patients with breast cancer from 2003 to 2006 (training cohort), 2 thresholds were calculated. The threshold according to Youden's approach yielded a follow-up sensitivity of 62.5% and a potential saving of 62.1% of follow-up visits and €24.8 million in 5 years. When the threshold corresponding to 95% follow-up sensitivity was used, 17% of follow-up visits and €7 million were saved compared with the guidelines. Similar results were obtained by applying these thresholds to the testing cohort of 11 462 patients from 2007 to 2008. Compared with the observed utilization of follow-up, the potential cost-savings decline moderately., Conclusions: Personalized follow-up schemes based on the INFLUENCE nomogram's individual risk estimations for breast cancer LRR could decrease the number of follow-up visits if one accepts a limited risk of delayed LRR detection., (Copyright © 2020 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background: Most preference-based instruments producing overall values for health states are devised on the simplifying assumption that the overall effect of distinct health-related quality of life domains (attributes) of the instrument equals the sum of the attributes. Nevertheless, health attributes are often inter-related and depend on each other., Objectives: To investigate whether inclusion of second-order interactions in the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L) value function would result in better fit and lead to different health state values than a model with main effects only., Methods: Using an efficient design, 400 pairs of EQ-5D-3L health states were generated in a pairwise choice format. We analyzed responses of 4000 people from the general population using a conditional logit model, and we tested goodness of fit using pseudo R 2 , Akaike information criterion, differences in log-likelihood, and likelihood ratio. We compared accuracies of models' predictions based on root mean square error and mean absolute error., Results: The interaction-effects model showed systematically lower values than the main-effects model. Inclusion of interactions resulted only in a slightly better model fit. Interactions comprising mobility and self-care were the most salient., Conclusions: For the EQ-5D-3L, a value function based on interactions produces systematically lower values than a main-effects model, meaning that the effect of two or more health problems combined is stronger than the sum of the individual main effects., (Copyright © 2018 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background: Lung cancer screening can reduce cancer mortality. Most implementation studies focus only on low-dose computed tomography (LDCT) and clinical attributes of screening and do not include preferences of potential participants. In this study we evaluated the perceived value of screening programs based on LDCT, breath analysis (BA), or blood biomarkers (BB) according to the perspective of the target population., Methods: A multi-criteria decision analysis framework was adopted. The weights of seven attributes of screening (sensitivity, specificity, radiation burden, duration of screening process, waiting time until results are communicated, location of screening, and mode of screening) were obtained from an earlier study that included a broad sample from the Netherlands. Performance data for the screening modalities was obtained from clinical trials and expert opinion. Parameter uncertainty about clinical performances was incorporated probabilistically, while heterogeneity in preferences was analyzed through subgroup analyses., Results: The mean overall values were 0.58 (CI: 0.57 to 0.59), 0.57 (CI: 0.56 to 0.59), and 0.44 (CI: 0.43 to 0.45) for BB, BA, and LDCT, respectively. Seventy-seven per cent of respondents preferred BB or BA. For most subgroups, the overall values were similar to those of the entire sample. BA had the highest value for respondents who would have been eligible for earlier screening trials., Discussion: BB and BA seem valuable to participants because they can be applied in a primary care setting. Although LDCT still seems preferable given its strong and positive evidence base, it is important to take non-clinical attributes into account to maximize attendance., (Copyright © 2018 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Objective: The objective of this study was to explore the perceived advantages and disadvantages of oral anticoagulant therapies (OAT), and the trade-offs patients make in choosing therapy and adhering to their drug regimen., Methods: Five focus group sessions were conducted across Europe among patients with atrial fibrillation to identify the most important factors impacting OAT's value and adherence., Results: The most frequently identified barriers to OAT were lack of knowledge; poor patient-physician relationships; distraction due to employment or social environment; prior bleeding event(s) or the fear of bleeding; and changes in routine. Factors identified as promoting adherence included patients' personality, motivation, attitudes, and medication-taking habits and routines, as well as good quality health services. Inconvenient aspects of vitamin-K antagonists, such as regular blood monitoring and diet restrictions, were not reported to influence adherence, but may trigger patients to switch to direct oral anticoagulants., Conclusion: Most patients reported that a mixture of modifiable and non-modifiable factors helps them to take their drugs as prescribed. Individual patients' particular needs and preferences regarding OAT vary., Practice Implications: OAT adherence can be promoted if therapies are tailored to patients' needs and preferences. Patients should be supported to share their preferences with their clinician., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To identify which specifications and approaches to model selection better predict health preferences, the International Academy of Health Preference Research (IAHPR) hosted a predictive modeling competition including 18 teams from around the world., Methods: In April 2016, an exploratory survey was fielded: 4074 US respondents completed 20 out of 1560 paired comparisons by choosing between two health descriptions (e.g., longer life span vs. better health). The exploratory data were distributed to all teams. By July, eight teams had submitted their predictions for 1600 additional pairs and described their analytical approach. After these predictions had been posted online, a confirmatory survey was fielded (4148 additional respondents)., Results: The victorious team, "Discreetly Charming Econometricians," led by Michał Jakubczyk, achieved the smallest χ 2 , 4391.54 (a predefined criterion). Its primary scientific findings were that different models performed better with different pairs, that the value of life span is not constant proportional, and that logit models have poor predictive validity in health valuation., Conclusions: The results demonstrated the diversity and potential of new analytical approaches in health preference research and highlighted the importance of predictive validity in health valuation., (Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

Background: Because early detection of lung cancer can substantially improve survival, there is increasing attention for lung cancer screening., Objectives: To estimate public preferences for lung cancer screening and to identify subgroups in preferences., Methods: Seven important attributes were selected using the literature, interviews, and a panel session. Preferences were elicited using a swing weighting questionnaire. The resulting attribute weights indicate the relative importance of swings from the worst to the best level between attributes. Hierarchical clustering was used to identify subgroups with different attribute weights., Results: One thousand thirty-four respondents from a representative Dutch panel aged between 40 and 80 years were included. The identified attributes were location of screening (weight = 0.18 ± 0.16), mode of screening (weight = 0.17 ± 0.14), sensitivity (weight = 0.16 ± 0.13) and specificity (weight = 0.13 ± 0.12) of the screening modality, waiting time until results (weight = 0.13 ± 0.12), radiation burden (weight = 0.13 ± 0.12), and duration of screening procedure (weight = 0.10 ± 0.09). Most respondents preferred breath analysis (45%) to giving blood samples (31%) or going through a scanner (24%) as screening modality; 59% preferred screening at the general practitioner's office instead of at the hospital. There was a significant difference in education between the five identified preference subgroups (P < 0.01)., Conclusions: There is considerable variation in how people value attributes of lung cancer screening. Different screening policies and implementation strategies may be appropriate for particular preference subgroups. Our results indicate that people prefer breath analysis and that they are more likely to attend screening modalities that can be used at a primary care facility., (Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)