ON THE EXPRESSIVE POWER OF THE RELATIONAL MODEL: A DATABASE DESIGNER\u27S POINT OF VIEW

The purpose of this paper is to introduce a framework for assessing the expressive power of data models and to apply this framework to the relational model of data. From a designer\u27s point of view, a data model such as the relational model should not only be formally defined and easy to understand, but should also provide a powerful set of constructs to model real-world phenomena. The expressive power of a data model, defined as the degree to which its constructs match with constructs encountered in reality, can be judged by two complementary principles: the interpretation principle and the representation principle. It is asserted that database designers attempt to minimize the number of ad hoc database constraints, and that a data model faithful to the two principles supports this design strategy. Subsequently, this constraint minimization strategy is used to assess the expressive power of a particular data model, i.e., the relational data model. The authors take the position that the expressive power of the relational model is not optimal, due to a lack of adherence to both the interpretation principle and the representation principle. The paper amplifies this position by means of a number of examples, all based on publications by Codd and Date

Patient Preferences for Lung Cancer Treatment: A Qualitative Study Protocol Among Advanced Lung Cancer Patients

Introduction: Lung cancer is the deadliest and most prevalent cancer worldwide. Lung cancer treatments have different characteristics and are associated with a range of benefits and side effects for patients. Such differences may raise uncertainty among drug developers, regulators, payers, and clinicians regarding the value of these treatment effects to patients. The value of conducting patient preference studies (using qualitative and/or quantitative methods) for benefits and side effects of different treatment options has been recognized by healthcare stakeholders, such as drug developers, regulators, health technology assessment bodies, and clinicians. However, evidence-based guidelines on how and when to conduct and use these studies in drug decision-making are lacking. As part of the Innovative Medicines Initiative PREFER project, we developed a protocol for a qualitative study that aims to understand which treatment characteristics are most important to lung cancer patients and to develop attributes and levels for inclusion in a subsequent quantitative preference survey. Methods: The study protocol specifies a four-phased approach: (i) a scoping literature review of published literature, (ii) four focus group discussions with stage III and IV Non-Small Cell Lung Cancer patients, (iii) two nominal group discussions with stage III and IV Non-Small Cell Lung Cancer patients, and (iv) multi-stakeholder discussions involving clinicians and preference experts. Discussion: This protocol outlines methodological and practical steps as to how qualitative research can be applied to identify and develop attributes and levels for inclusion in patient preference studies aiming to inform decisions across the drug life cycle. The results of this study are intended to inform a subsequent quantitative preference survey that assesses patient trade-offs regarding lung cancer treatment options. This protocol may assist researchers, drug developers, and decision-makers in designing qualitative studies to understand which treatment aspects are most valued by patients in drug development, regulation, and reimbursement

Factors and Situations Affecting the Value of Patient Preference Studies: Semi-Structured Interviews in Europe and the US

Objectives: Patient preference information (PPI) is gaining recognition among the pharmaceutical industry, regulatory authorities, and health technology assessment (HTA) bodies/payers for use in assessments and decision-making along the medical product lifecycle (MPLC). This study aimed to identify factors and situations that influence the value of patient preference studies (PPS) in decision-making along the MPLC according to different stakeholders. Methods: Semi-structured interviews (n = 143) were conducted with six different stakeholder groups (physicians, academics, industry representa

Patient Preferences in the Medical Product Life Cycle: What do Stakeholders Think? Semi-Structured Qualitative Interviews in Europe and the USA.

Background Patient preferences (PP), which are investigated in PP studies using qualitative or quantitative methods, are a growing area of interest to the following stakeholders involved in the medical product lifecycle: academics, health technology assessment bodies,

Public preferences for digital health data sharing: Discrete choice experiment study in 12 european countries

Background: With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public s views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. Objective: This study investigated public preferences for digital health data sharing. Methods: A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class s pooled and regional data sets. Results: A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: A national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. Conclusions: This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe

Factors and situations influencing the value of patient preference studies along the medical product lifecycle: a literature review

Industry, regulators, health technology assessment (HTA) bodies, and payers are exploring the use of patient preferences in their decision-making processes. In general, experience in conducting and assessing patient preference studies is limited. Here, we performed a systematic literature search and review to identify factors and situations influencing the value of patient preference studies, as well as applications throughout the medical product lifecyle. Factors and situations identified in 113 publications related to the organization, design, and conduct of studies, and to communication and use of results. Although current use of patient preferences is limited, we identified possible applications in discovery, clinical development, marketing authorization, HTA, and postmarketing phases

Patient Preferences for Lung Cancer Treatments: A Study Protocol for a Preference Survey Using Discrete Choice Experiment and Swing Weighting

Background: Advanced treatment options for non-small cell lung cancer (NSCLC) consist of immunotherapy, chemotherapy, or a combination of both. Decisions surrounding NSCLC can be considered as preference-sensitive because multiple treatments exist that vary in terms of mode of administration, treatment schedules, and benefit–risk profiles. As part of the IMI PREFER project, we developed a protocol for an online preference survey for NSCLC patients exploring differences in preferences according to patient characteristics (preference heterogeneity). Moreover, this study will evaluate and compare the use of two different preference elicitation methods, the discrete choice experiment (DCE) and the swing weighting (SW) task. Finally, the study explores how demographic (i.e., age, gender, and educational level) and clinical (i.e., cancer stage and line of treatment) information, health literacy, health locus of control, and quality of life may influence or explain patient preferences and the usefulness of a digital interactive tool in providing information on preference elicitation tasks according to patients. Methods: An online survey will be implemented with the aim to recruit 510 NSCLC patients in Belgium and Italy. Participants will be randomized 50:50 to first receive either the DCE or the SW. The survey will also collect information on participants' disease-related status, health locus of control, health literacy, quality of life, and perception of the educational tool. Discussion: This protocol outlines methodological and practical steps to quantitatively elicit and study patient preferences for NSCLC treatment alternatives. Results from this study will increase the understanding of which treatment aspects are most valued by NSCLC patients to inform decision-making in drug development, regulatory approval, and reimbursement. Methodologically, the comparison between the DCE and the SW task will be valuable to gain information on how these preference methods perform against each other in eliciting patient preferences. Overall, this protocol may assist researchers, drug developers, and decision-makers in designing quantitative patient preferences into decision-making along the medical product life cycle

Factors and situations influencing the value of patient preference studies along the medical product lifecycle: a literature review

Industry, regulators, health technology assessment (HTA) bodies, and payers are exploring the use of patient preferences in their decision-making processes. In general, experience in conducting and assessing patient preference studies is limited. Here, we performed a systematic literature search and review to identify factors and situations influencing the value of patient preference studies, as well as applications throughout the medical product lifecyle. Factors and situations identified in 113 publications related to the organization, design, and conduct of studies, and to communication and use of results. Although current use of patient preferences is limited, we identified possible applications in discovery, clinical development, marketing authorization, HTA, and postmarketing phases. This study can inform different stakeholders on how to conduct, assess, and use patient preference studies and on when to include patient preference studies in development plans

Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review.

Background: The inclusion of patient preferences (PP) in the medical product life cycle is a topic of growing interest to stakeholders such as academics, Health Technology Assessment (HTA) bodies, reimbursement agencies, industry, patients, physicians and regulators. This review aimed to understand the potential roles, reasons for using PP and the expectations, concerns and requirements associated with PP in industry processes, regulatory benefitrisk assessment (BRA) and marketing authorization (MA), and HTA and reimbursement decision-making. Methods: A systematic review of peer-reviewed and grey literature published between January 2011 and March 2018 was performed. Consulted databases were EconLit, Embase, Guidelines International Network, PsycINFO and PubMed. A two-step strategy was used to select literature. Literature was analyzed using NVivo (QSR international). Results: From 1015 initially identified documents, 72 were included. Most were written from an academic perspective (61%) and focused on PP in BRA/MA and/or HTA/reimbursement (73%). Using PP to improve understanding of patients’ valuations of treatment outcomes, patients’ benefit-risk trade-offs and preference heterogeneity were roles identified in all three decision-making contexts. Reasons for using PP relate to the unique insights and position of patients and the positive effect of including PP on the quality of the decision-making process. Concerns shared across decision-making contexts included methodological questions concerning the validity, reliability and cognitive burden of preference methods. In order to use PP, general, operational and quality requirements were identified, including recognition of the importance of PP and ensuring patient understanding in PP studies. Conclusions: Despite the array of opportunities and added value of using PP throughout the different steps of the MPLC identified in this review, their inclusion in decision-making is hampered by methodological challenges and lack of specific guidance on how to tackle these challenges when undertaking PP studies. To support the development of such guidance, more best practice PP studies and PP studies investigating the methodological issues identified in this review are critically needed

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.