Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer.

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs. OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs. METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate. RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%. CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.

The effect of time before diagnosis and treatment on colorectal cancer outcomes: systematic review and dose-response meta-analysis.

BACKGROUND: This systematic review and meta-analysis aimed to evaluate existing evidence on the relationship between diagnostic and treatment intervals and outcomes for colorectal cancer. METHODS: Four databases were searched for English language articles assessing the role of time before initial treatment in colorectal cancer on any outcome, including stage and survival. Two reviewers independently screened articles for inclusion and data were synthesised narratively. A dose-response meta-analysis was performed to examine the association between treatment interval and survival. RESULTS: One hundred and thirty papers were included in the systematic review, eight were included in the meta-analysis. Forty-five different intervals were considered in the time from first symptom to treatment. The most common finding was of no association between the length of intervals on any outcome. The dose-response meta-analysis showed a U-shaped association between the treatment interval and overall survival with the nadir at 45 days. CONCLUSION: The review found inconsistent, but mostly a lack of, association between interval length and colorectal cancer outcomes, but study design and quality were heterogeneous. Meta-analysis suggests survival becomes increasingly poorer for those commencing treatment more than 45 days after diagnosis. REGISTRATION: This review was registered, and the protocol is available, in PROSPERO, the international database of systematic reviews, with the registration ID CRD42021255864.

Pharmacological treatment patterns in patients with juvenile idiopathic arthritis in the Netherlands: a real-world data analysis.

OBJECTIVE: To investigate medication prescription patterns among children with JIA, including duration, sequence and reasons for medication discontinuation. METHODS: This study is a single-centre, retrospective analysis of prospective data from the electronic medical records of JIA patients receiving systemic therapy aged 0-18 years between 1 April 2011 and 31 March 2019. Patient characteristics (age, gender, JIA subtype) and medication prescriptions were extracted and analysed using descriptive statistics, Sankey diagrams and Kaplan-Meier survival methods. RESULTS: Over a median of 4.2 years follow-up, the 20 different medicines analysed were prescribed as monotherapy (n = 15) or combination therapy (n = 48 unique combinations) among 236 patients. In non-systemic JIA, synthetic DMARDs were prescribed to almost all patients (99.5%), and always included MTX. In contrast, 43.9% of non-systemic JIA patients received a biologic DMARD (mostly adalimumab or etanercept), ranging from 30.9% for oligoarticular persistent ANA-positive JIA, to 90.9% for polyarticular RF-positive JIA. Among systemic JIA, 91.7% received a biologic DMARD (always including anakinra). When analysing medication prescriptions according to their class, 32.6% involved combination therapy. In 56.8% of patients, subsequent treatment lines were initiated after unsuccessful first-line treatment, resulting in 68 unique sequences. Remission was the most common reason for DMARD discontinuation (44.7%), followed by adverse events (28.9%) and ineffectiveness (22.1%). CONCLUSION: This paper reveals the complexity of pharmacological treatment in JIA, as indicated by: the variety of mono- and combination therapies prescribed, substantial variation in medication prescriptions between subtypes, most patients receiving two or more treatment lines, and the large number of unique treatment sequences.

Quantifying hospital-associated costs, and accompanying travel costs and productivity losses, before and after withdrawing tumour necrosis factor-alfa inhibitors in juvenile idiopathic arthritis.

OBJECTIVE: To quantify differences in hospital-associated costs, and accompanying travel costs and productivity losses, before and after withdrawing TNFi in JIA patients. METHODS: Retrospective analysis of prospectively collected data from electronic medical records of paediatric JIA patients treated with TNFi, which were either immediately discontinued, spaced (increased treatment interval) or tapered (reduced subsequent doses). Costs of hospital-associated resource use (consultations, medication, radiology procedures, laboratory testing, procedures under general anaesthesia, hospitalisation) and associated travel costs and productivity losses were quantified during clinically inactive disease until TNFi withdrawal (pre-withdrawal period) and compared with costs during the first and second year after withdrawal initiation (first and second year post-withdrawal). RESULTS: Fifty-six patients were included of whom 26 immediately discontinued TNFi, 30 spaced and zero tapered. Mean annual costs were €9,165/patient on active treatment (pre-withdrawal) and decreased significantly to €5,063/patient (-44.8%) and €6,569/patient (-28.3%) in the first and second year post-withdrawal, respectively (p< 0.05). Of these total annual costs, travel costs plus productivity losses were €834/patient, €1,180/patient, and €1,320/patient, in the three periods respectively. Medication comprised 80.7%, 61.5% and 72.4% of total annual costs in the pre-withdrawal, first, and second year post-withdrawal period, respectively. CONCLUSION: In the first two years after initiating withdrawal, the total annual costs are decreased compared with the pre-withdrawal period. However, cost reductions were lower in the second year compared with the first year post-withdrawal, primarily due to restarting or intensifying biologics. To support biologic withdraw decisions, future research should assess the full long-term societal cost impacts, and include all biologics.

Heterogeneity in how women value risk-stratified breast screening.

PURPOSE: Risk-stratified screening has potential to improve the cost effectiveness of national breast cancer screening programs. This study aimed to inform a socially acceptable and equitable implementation framework by determining what influences a woman's decision to accept a personalized breast cancer risk assessment and what the relative impact of these key determinants is. METHODS: Multicriteria decision analysis was used to elicit the relative weights for 8 criteria that women reported influenced their decision. Preference heterogeneity was explored through cluster analysis. RESULTS: The 2 criteria valued most by the 347 participants related to program access, "Mode of invitation" and "Testing process". Both criteria significantly influenced participation (P < .001). A total of 73% preferred communication by letter/online. Almost all women preferred a multidisease risk assessment with potential for a familial high-risk result. Four preference-based subgroups were identified. Membership to the largest subgroup was predicted by lower educational attainment, and women in this subgroup were concerned with program access. Higher relative perceived breast cancer risk predicted membership to the smallest subgroup that was focused on test parameters, namely "Scope of test" and "Test specificity". CONCLUSION: Overall, Australian women would accept a personalized multidisease risk assessment, but when aligning with their preferences, it will necessitate a focus on program access and the development of online communication frameworks.

Health economic evidence for the use of molecular biomarker tests in hematological malignancies: A systematic review.

OBJECTIVES: Molecular biomarker tests can inform the clinical management of genomic heterogeneous hematological malignancies, yet their availability in routine care largely depends on the supporting health economic evidence. This study aims to systematically review the economic evidence for recent molecular biomarker tests in hematological malignancies. METHODS: We conducted a systematic search in five electronic databases for studies published between January 2010 and October 2020. Publications were independently screened by two reviewers. Clinical study characteristics, economic methodology, and results were extracted, and reporting quality was assessed. RESULTS: Fourteen studies were identified, of which half (n = 7; 50%) were full economic evaluations examining both health and economic outcomes. Studies were predominantly conducted in a first-line treatment setting (n = 7; 50%) and adopted a non-lifetime time horizon to measure health outcomes and costs (n = 7; 50%). Five studies reported that companion diagnostics for associated therapies were likely cost-effective for acute myeloid leukemia, chronic myeloid leukemia, diffuse large B-cell lymphoma, and multiple myeloma. Four studies suggested molecular biomarker tests for treatment monitoring in chronic myeloid leukemia were likely cost-saving. CONCLUSIONS: Although there is initial confirmation of the promising health economic results, the present research for molecular biomarker tests in hematological malignancies is sparse with many applications of technological advances yet to be evaluated.

Can we increase efficiency of CT lung cancer screening by combining with CVD and COPD screening? Results of an early economic evaluation.

OBJECTIVES: Estimating the maximum acceptable cost (MAC) per screened individual for low-dose computed tomography (LDCT) lung cancer (LC) screening, and determining the effect of additionally screening for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), or both on the MAC. METHODS: A model-based early health technology assessment (HTA) was conducted to estimate whether a new intervention could be cost-effective by calculating the MAC at a willingness-to-pay (WTP) of €20k/quality-adjusted life-year (QALY) and €80k/QALY, for a population of current and former smokers, aged 50-75 years in The Netherlands. The MAC was estimated based on incremental QALYs gained from a stage shift assuming screened individuals are detected in earlier disease stages. Data were obtained from literature and publicly available statistics and validated with experts. RESULTS: The MAC per individual for implementing LC screening at a WTP of €20k/QALY was €113. If COPD, CVD, or both were included in screening, the MAC increased to €230, €895, or €971 respectively. Scenario analyses assessed whether screening-specific disease high-risk populations would improve cost-effectiveness, showing that high-risk CVD populations were more likely to improve economic viability compared to COPD. CONCLUSIONS: The economic viability of combined screening is substantially larger than for LC screening alone, primarily due to benefits from CVD screening, and is dependent on the target screening population, which is key to optimise the screening program. The total cost of breast and cervical cancer screening is lower (€420) than the MAC of Big-3, indicating that Big-3 screening may be acceptable from a health economic perspective. KEY POINTS: • Once-off combined low-dose CT screening for lung cancer, COPD, and CVD in individuals aged 50-75 years is potentially cost-effective if screening would cost less than €971 per screened individual. • Multi-disease screening requires detailed insight into the co-occurrence of these diseases to identify the optimal target screening population. • With the same target screening population and WTP, lung cancer-only screening should cost less than €113 per screened individual to be cost-effective.

Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer.

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.

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Do Health Technology Assessment organisations consider manufacturers' costs in relation to drug price? A study of reimbursement reports.

INTRODUCTION: Drug reimbursement decisions are often made based on a price set by the manufacturer. In some cases, this price leads to public and scientific debates about whether its level can be justified in relation to its costs, including those related to research and development (R&D) and manufacturing. Such considerations could enter the decision process in collectively financed health care systems. This paper investigates whether manufacturers' costs in relation to drug prices, or profit margins, are explicitly mentioned and considered by health technology assessment (HTA) organisations. METHOD: An analysis of reimbursement reports for cancer drugs was performed. All relevant Dutch HTA-reports, published between 2017 and 2019, were selected and matched with HTA-reports from three other jurisdictions (England, Canada, Australia). Information was extracted. Additionally, reimbursement reports for three cases of expensive non-oncolytic orphan drugs prominent in pricing debates in the Netherlands were investigated in depth to examine consideration of profit margins. RESULTS: A total of 66 HTA-reports concerning 15 cancer drugs were included. None of these reports contained information on manufacturer's costs or profit margins. Some reports contained general considerations of the HTA organisation which related prices to manufacturers' costs: six contained a statement on the lack of price setting transparency, one mentioned recouping R&D costs as a potential argument to justify a high price. For the case studies, 21 HTA-reports were selected. One contained a cost-based price justification provided by the manufacturer. None of the other reports contained information on manufacturer's costs or profit margins. Six reports contained a discussion about lack of transparency. Reports from two jurisdictions contained invitations to justify high prices by demonstrating high costs. CONCLUSION: Despite the attention given to manufacturers' costs in relation to price in public debates and in the literature, this issue does not seem to get explicit systematic consideration in the reimbursement reports of expensive drugs.

Disparities in quality of life, social distress and employment outcomes in Australian cancer survivors.

PURPOSE: To examine how socio-demographic, comorbidities and information needs influence quality of life (QoL) outcomes of survivors of breast, colorectal, or prostate cancer, non-Hodgkin lymphoma or melanoma. METHODS: Cross-sectional postal survey with eligible participants identified through a population-based cancer registry. QoL outcomes were assessed by EQ-5D-5L, social difficulties index (SDI) and, for those employed at diagnosis, current employment. Regression analyses explored associations between outcome variables and cancer type, age, time since diagnosis, residential location, socio-economic disadvantage, comorbidities and unmet information needs. Mediation analyses examined whether comorbidities and information needs explained relationships between outcome variables and socio-economic disadvantage. RESULTS: 2115 survivors participated. Mean EQ-5D-5L scores (mean = 0.84) were similar to population averages and SDI scores were low for the entire sample (mean = 3.80). In multivariate analyses, being aged over 80, greater socio-economic disadvantage, comorbidities and unmet information needs decreased EQ-5D-5L scores. Higher SDI scores were associated with socio-economic disadvantage, comorbidities and unmet information needs. Not being employed was associated with being aged over 50, more comorbidities and socio-economic disadvantage. Comorbidities but not information needs partially mediated the impact of socio-economic disadvantage on EQ-5D-5L and SDI accounting for 17% and 14% of the total effect of socio-economic disadvantage respectively. Neither comorbidities nor information needs mediated the association between socio-economic disadvantage and employment outcomes. CONCLUSIONS: To improve quality of life, survivorship care should be better tailored to address the needs of individuals given their overall health and impact of comorbidities, their age and type of cancer and not simply time since diagnosis.

The societal impact of implementing an at-home blood sampling device for chronic care patients: patient preferences and cost impact.

BACKGROUND: Diabetes mellitus, cardiovascular diseases, chronic kidney disease, and thyroid diseases are chronic diseases that require regular monitoring through blood tests. This paper first investigates the experiences of chronic care patients with venipuncture and their expectations of an at-home blood-sampling device, and then assesses the impact on societal costs of implementing such a device in current practice. METHODS: An online survey was distributed among chronic care patients to gain insight into their experience of blood sampling in current practice, and their expectations of an at-home blood-sampling device. The survey results were used as input parameters in a patient-level monte carlo analysis developed to represent a hypothetical cohort of Dutch chronically ill patients to investigate the impact on societal costs compared to usual care. RESULTS: In total, 1311 patients participated in the survey, of which 31% experience the time spent on the phlebotomy appointment as a burden. Of all respondents, 71% prefer to use an at-home blood-sampling device to monitor their chronic disease. The cost analysis indicated that implementing an at-home blood-sampling device increases the cost of phlebotomy itself by €27.25 per patient per year, but it reduces the overall societal costs by €24.86 per patient per year, mainly due to limiting productivity loss. CONCLUSIONS: Patients consider an at-home blood-sampling device to be more user-friendly than venous phlebotomy on location. Long waiting times and crowded locations can be avoided by using an at-home blood-sampling device. Implementing such a device is likely cost-saving as it is expected to reduce societal costs.

Evaluating the resource implications of different service delivery models for offering additional genomic findings.

PURPOSE: To evaluate the resource implications of different delivery models for the provision of additional findings (AF) in genomics from a health-care purchaser perspective. METHODS: Data from the Additional Findings study were used to develop and validate a discrete event simulation model that represented the pathway of delivering AF. Resource implications were estimated by microcosting the consultations, sample verifications, bioinformatics, curation, and multidisciplinary case review meetings. A proof-of-concept model was used to generate costing, and then the simulation model was varied to assess the impact of an automated analysis pipeline, use of telehealth consultation, full automation with electronic decision support, and prioritizing case review for cases with pathogenic variants. RESULTS: For the proof-of-concept delivery model, the average total cost to report AF was US$430 per patient irrespective of result pathogenicity (95% confidence interval [CI] US$375-US$489). However, the cost of per AF diagnosis was US$4349 (95% CI US$3794-US$4953). Alternative approaches to genetic counseling (telehealth, decision support materials) and to multidisciplinary case review (pathogenic AF cases only) lowered the total per patient cost of AF analysis and reporting by 41-51%. CONCLUSION: Resources required to provide AF can be reduced substantially by implementing alternative approaches to counseling and multidisciplinary case review.

Whole genome sequencing in oncology: using scenario drafting to explore future developments.

BACKGROUND: In oncology, Whole Genome Sequencing (WGS) is not yet widely implemented due to uncertainties such as the required infrastructure and expertise, costs and reimbursements, and unknown pan-cancer clinical utility. Therefore, this study aimed to investigate possible future developments facilitating or impeding the use of WGS as a molecular diagnostic in oncology through scenario drafting. METHODS: A four-step process was adopted for scenario drafting. First, the literature was searched for barriers and facilitators related to the implementation of WGS. Second, they were prioritized by international experts, and third, combined into coherent scenarios. Fourth, the scenarios were implemented in an online survey and their likelihood of taking place within 5 years was elicited from another group of experts. Based on the minimum, maximum, and most likely (mode) parameters, individual Program Evaluation and Review Technique (PERT) probability density functions were determined. Subsequently, individual opinions were aggregated by performing unweighted linear pooling, from which summary statistics were extracted and reported. RESULTS: Sixty-two unique barriers and facilitators were extracted from 70 articles. Price, clinical utility, and turnaround time of WGS were ranked as the most important aspects. Nine scenarios were developed and scored on likelihood by 18 experts. The scenario about introducing WGS as a clinical diagnostic with a lower price, shorter turnaround time, and improved degree of actionability, scored the highest likelihood (median: 68.3%). Scenarios with low likelihoods and strong consensus were about better treatment responses to more actionable targets (26.1%), and the effect of centralizing WGS (24.1%). CONCLUSIONS: Based on current expert opinions, the implementation of WGS as a clinical diagnostic in oncology is heavily dependent on the price, clinical utility (both in terms of identifying actionable targets as in adding sufficient value in subsequent treatment), and turnaround time. These aspects and the optimal way of service provision are the main drivers for the implementation of WGS and should be focused on in further research. More knowledge regarding these factors is needed to inform strategic decision making regarding the implementation of WGS, which warrants support from all relevant stakeholders.

Lifetime Health and Economic Outcomes of Active Surveillance, Radical Prostatectomy, and Radiotherapy for Favorable-Risk Localized Prostate Cancer.

OBJECTIVES: To estimate the lifetime health and economic outcomes of selecting active surveillance (AS), radical prostatectomy (RP), or radiation therapy (RT) as initial management for low- or favorable-risk localized prostate cancer. METHODS: A discrete-event simulation model was developed using evidence from published randomized trials. Health outcomes were measured in life-years and quality-adjusted life-years (QALYs). Costs were included from a public payer perspective in Australian dollars. Outcomes were discounted at 5% over a lifetime horizon. Probabilistic and scenario analyses quantified parameter and structural uncertainty. RESULTS: A total of 60% of patients in the AS arm eventually received radical treatment (surgery or radiotherapy) compared with 90% for RP and 91% for RT. Although AS resulted in fewer treatment-related complications, it led to increased clinical progression (AS 40.7%, RP 17.6%, RT 19.9%) and metastatic disease (AS 13.4%, RP 6.1%, RT 7.0%). QALYs were 10.88 for AS, 11.10 for RP, and 11.13 for RT. Total costs were A$17 912 for AS, A$15 609 for RP, and A$15 118 for RT. At a willingness to pay of A$20 000/QALY, RT had a 61.4% chance of being cost-effective compared to 38.5% for RP and 0.1% for AS. CONCLUSIONS: Although AS resulted in fewer and delayed treatment-related complications, it was not found to be a cost-effective strategy for favorable-risk localized prostate cancer over a lifetime horizon because of an increase in the number of patients developing metastatic disease. RT was the dominant strategy yielding higher QALYs at lower cost although differences compared with RP were small.

Using Metamodeling to Identify the Optimal Strategy for Colorectal Cancer Screening.

OBJECTIVES: Metamodeling can address computational challenges within decision-analytic modeling studies evaluating many strategies. This article illustrates the value of metamodeling for evaluating colorectal cancer screening strategies while accounting for colonoscopy capacity constraints. METHODS: In a traditional approach, the best screening strategy was identified from a limited subset of strategies evaluated with the validated Adenoma and Serrated pathway to Colorectal CAncer model. In a metamodeling approach, metamodels were fitted to this limited subset to evaluate all potentially plausible strategies and determine the best overall screening strategy. Approaches were compared based on the best screening strategy in life-years gained compared with no screening. Metamodel runtime and accuracy was assessed. RESULTS: The metamodeling approach evaluated >40 000 strategies in <1 minute with high accuracy after 1 adaptive sampling step (mean absolute error: 0.0002 life-years) using 300 samples in total (generation time: 8 days). Findings indicated that health outcomes could be improved without requiring additional colonoscopy capacity. Obtaining similar insights using the traditional approach could require at least 1000 samples (generation time: 28 days). Suggested benefits from screening at ages <40 years require adequate validation of the underlying Adenoma and Serrated pathway to Colorectal CAncer model before making policy recommendations. CONCLUSIONS: Metamodeling allows rapid assessment of a vast set of strategies, which may lead to identification of more favorable strategies compared to a traditional approach. Nevertheless, metamodel validation and identifying extrapolation beyond the support of the original decision-analytic model are critical to the interpretation of results. The screening strategies identified with metamodeling support ongoing discussions on decreasing the starting age of colorectal cancer screening.

A scoping review of studies using observational data to optimise dynamic treatment regimens.

BACKGROUND: Dynamic treatment regimens (DTRs) formalise the multi-stage and dynamic decision problems that clinicians often face when treating chronic or progressive medical conditions. Compared to randomised controlled trials, using observational data to optimise DTRs may allow a wider range of treatments to be evaluated at a lower cost. This review aimed to provide an overview of how DTRs are optimised with observational data in practice. METHODS: Using the PubMed database, a scoping review of studies in which DTRs were optimised using observational data was performed in October 2020. Data extracted from eligible articles included target medical condition, source and type of data, statistical methods, and translational relevance of the included studies. RESULTS: From 209 PubMed abstracts, 37 full-text articles were identified, and a further 26 were screened from the reference lists, totalling 63 articles for inclusion in a narrative data synthesis. Observational DTR models are a recent development and their application has been concentrated in a few medical areas, primarily HIV/AIDS (27, 43%), followed by cancer (8, 13%), and diabetes (6, 10%). There was substantial variation in the scope, intent, complexity, and quality between the included studies. Statistical methods that were used included inverse-probability weighting (26, 41%), the parametric G-formula (16, 25%), Q-learning (10, 16%), G-estimation (4, 6%), targeted maximum likelihood/minimum loss-based estimation (4, 6%), regret regression (3, 5%), and other less common approaches (10, 16%). Notably, studies that were primarily intended to address real-world clinical questions (18, 29%) tended to use inverse-probability weighting and the parametric G-formula, relatively well-established methods, along with a large amount of data. Studies focused on methodological developments (45, 71%) tended to be more complicated and included a demonstrative real-world application only. CONCLUSIONS: As chronic and progressive conditions become more common, the need will grow for personalised treatments and methods to estimate the effects of DTRs. Observational DTR studies will be necessary, but so far their use to inform clinical practice has been limited. Focusing on simple DTRs, collecting large and rich clinical datasets, and fostering tight partnerships between content experts and data analysts may result in more clinically relevant observational DTR studies.

Genomic Health Literacy Interventions in Pediatrics: Scoping Review.

BACKGROUND: The emergence of genetic and genomic sequencing approaches for pediatric patients has raised questions about the genomic health literacy levels, attitudes toward receiving genomic information, and use of this information to inform treatment decisions by pediatric patients and their parents. However, the methods to educate pediatric patients and their parents about genomic concepts through digital health interventions have not been well-established. OBJECTIVE: The primary objective of this scoping review is to investigate the current levels of genomic health literacy and the attitudes toward receiving genomic information among pediatric patients and their parents. The secondary aim is to investigate patient education interventions that aim to measure and increase genomic health literacy among pediatric patients and their parents. The findings from this review will be used to inform future digital health interventions for patient education. METHODS: A scoping review using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and protocols was completed using the following databases: MEDLINE, Embase, CINAHL, and Scopus. Our search strategy included genomic information inclusive of all genetic and genomic terms, pediatrics, and patient education. Inclusion criteria included the following: the study included genetic, genomic, or a combination of genetic and genomic information; the study population was pediatric (children and adolescents <18 years) and parents of patients with pediatric illnesses or only parents of patients with pediatric illnesses; the study included an assessment of the knowledge, attitudes, and intervention regarding genomic information; the study was conducted in the last 12 years between 2008 and 2020; and the study was in the English language. Descriptive data regarding study design, methodology, disease population, and key findings were extracted. All the findings were collated, categorized, and reported thematically. RESULTS: Of the 4618 studies, 14 studies (n=6, 43% qualitative, n=6, 43% mixed methods, and n=2, 14% quantitative) were included. Key findings were based on the following 6 themes: knowledge of genomic concepts, use of the internet and social media for genomic information, use of genomic information for decision-making, hopes and attitudes toward receiving genomic information, experiences with genetic counseling, and interventions to improve genomic knowledge. CONCLUSIONS: This review identified that older age is related to the capacity of understanding genomic concepts, increased genomic health literacy levels, and the perceived ability to participate in decision-making related to genomic information. In addition, internet-searching plays a major role in obtaining genomic information and filling gaps in communication with health care providers. However, little is known about the capacity of pediatric patients and their parents to understand genomic information and make informed decisions based on the genomic information obtained. More research is required to inform digital health interventions and to leverage the leading best practices to educate these genomic concepts.

Evaluating the Age-Based Recommendations for Long-Term Follow-Up in Breast Cancer.

BACKGROUND: After 5 years of annual follow-up following breast cancer, Dutch guidelines are age based: annual follow-up for women <60 years, 60-75 years biennial, and none for >75 years. We determined how the risk of recurrence corresponds to these consensus-based recommendations and to the risk of primary breast cancer in the general screening population. SUBJECTS, MATERIALS, AND METHODS: Women with early-stage breast cancer in 2003/2005 were selected from the Netherlands Cancer Registry (n = 18,568). Cumulative incidence functions were estimated for follow-up years 5-10 for locoregional recurrences (LRRs) and second primary tumors (SPs). Risks were compared with the screening population without history of breast cancer. Alternative cutoffs for age were determined by log-rank tests. RESULTS: The cumulative risk for LRR/SP was lower in women <60 years (5.9%, 95% confidence interval [CI] 5.3-6.6) who are under annual follow-up than for women 60-75 (6.3%, 95% CI 5.6-7.1) receiving biennial visits. All risks were higher than the 5-year risk of a primary tumor in the screening population (ranging from 1.4% to 1.9%). Age cutoffs <50, 50-69, and > 69 revealed better risk differentiation and would provide more risk-based schedules. Still, other factors, including systemic treatments, had an even greater impact on recurrence risks. CONCLUSION: The current consensus-based recommendations use suboptimal age cutoffs. The proposed alternative cutoffs will lead to a more balanced risk-based follow-up and thereby more efficient allocation of resources. However, more factors should be taken into account for truly individualizing follow-up based on risk for recurrence. IMPLICATIONS FOR PRACTICE: The current age-based recommendations for breast cancer follow-up after 5 years are suboptimal and do not reflect the actual risk of recurrent disease. This results in situations in which women with higher risks actually receive less follow-up than those with a lower risk of recurrence. Alternative cutoffs could be a start toward risk-based follow-up and thereby more efficient allocation of resources. However, age, or any single risk factor, is not able to capture the risk differences and therefore is not sufficient for determining follow-up. More risk factors should be taken into account for truly individualizing follow-up based on the risk for recurrence.

Being Precise About Precision Medicine: What Should Value Frameworks Incorporate to Address Precision Medicine? A Report of the Personalized Precision Medicine Special Interest Group.

Precision medicine is a dynamic area embracing a diverse and increasing type of approaches that allow the targeting of new medicines, screening programs or preventive healthcare strategies, which include the use of biologic markers or complex tests driven by algorithms also potentially taking account of patient preferences. The International Society for Pharmacoeconomics and Outcome Research expanded its current work around precision medicine to (1) describe the evolving paradigm of precision medicine with examples of current and evolving applications, (2) describe key stakeholders perspectives on the value of precision medicine in their respective domains, and (3) define the core factors that should be considered in a value assessment framework for precision medicine. With the ultimate goal of improving health of well-defined patient groups, precision medicine will affect all stakeholders in the healthcare system at multiple levels spanning the individual perspective to the societal perspective. For an efficient, timely and practical precision medicine value assessment framework, it will be important to address these multiple perspectives through building consensus among the stakeholders for robust procedures and measures of value aspects, including performance of precision mechanism; aligned reimbursement processes of precision mechanism and subsequent treatment; transparent expectations for evidence requirements and study designs adequately matched to the intended use of the precision mechanism and to the smaller target patient populations; recognizing the potential range of value-generation such as ruling-in and ruling-out decisions.