Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing.

As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.

Conceptualization of utility in translational clinical genomics research.

Prior to integration into clinical care, a novel medical innovation is typically assessed in terms of its balance of benefits and risks, often referred to as utility. Members of multidisciplinary research teams may conceptualize and assess utility in different ways, which has implications within the translational genomics community and for the evidence base upon which clinical guidelines groups and healthcare payers make decisions. Ambiguity in the conceptualization of utility in translational genomics research can lead to communication challenges within research teams and to study designs that do not meet stakeholder needs. We seek to address the ambiguity challenge by describing the conceptual understanding of utility and use of the term by scholars in the fields of philosophy, medicine, and the social sciences of decision psychology and health economics. We illustrate applications of each field's orientation to translational genomics research by using examples from the Clinical Sequencing Evidence-Generating Research (CSER) consortium, and we provide recommendations for increasing clarity and cohesion in future research. Given that different understandings of utility will align to a greater or lesser degree with important stakeholders' views, more precise use of the term can help researchers to better integrate multidisciplinary investigations and communicate with stakeholders.

Population Genomic Screening for Three Common Hereditary Conditions : A Cost-Effectiveness Analysis.

BACKGROUND: The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown. OBJECTIVE: To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH). DESIGN: Decision analytic Markov model. DATA SOURCES: Published literature. TARGET POPULATION: Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults. TIME HORIZON: Lifetime. PERSPECTIVE: U.S. health care payer. INTERVENTION: Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands. OUTCOME MEASURES: Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs. RESULTS OF BASE-CASE ANALYSIS: Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900). RESULTS OF SENSITIVITY ANALYSIS: Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters. LIMITATIONS: Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments. CONCLUSION: Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions. PRIMARY FUNDING SOURCE: National Human Genome Research Institute.

Longitudinal adherence to breast cancer surveillance following cancer genetic testing in an integrated health care system.

PURPOSE: Screening with mammography and breast magnetic resonance imaging (MRI) is an important risk management strategy for individuals with inherited pathogenic variants (PVs) in genes associated with increased breast cancer risk. We describe longitudinal screening adherence in individuals who underwent cancer genetic testing as part of usual care in a vertically integrated health system. METHODS: We determined the proportion time covered (PTC) by annual mammography and breast MRI for individuals with PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, and ATM. We determined time covered by biennial mammography beginning at age 50 years for individuals who received negative results, uncertain results, or with PVs in genes without specific breast cancer screening recommendations. RESULTS: One hundred and forty individuals had PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, or ATM. Among these individuals, average PTC was 48% (range 0-99%) for annual screening mammography and 34% (range 0-100%) for annual breast MRI. Average PTC was highest for individuals with PVs in CHEK2 (N = 14) and lowest for individuals with PVs in TP53 (N = 3). Average PTC for biennial mammography (N = 1,027) was 49% (0-100%). CONCLUSION: Longitudinal screening adherence in individuals with PVs in breast cancer associated genes, as measured by the proportion of time covered, is low; adherence to annual breast MRI falls below that of annual mammography. Additional research should examine screening behavior in individuals with PVs in breast cancer associated genes with a goal of developing interventions to improve adherence to recommended risk management.

Risk-reducing surgery in unaffected individuals receiving cancer genetic testing in an integrated health care system.

BACKGROUND: Germline genetic testing enables primary cancer prevention, including through prophylactic surgery. We examined risk-reducing surgeries in unaffected individuals tested for hereditary cancer susceptibly between 2010 and 2018 in the Kaiser Permanente Northwest health system. METHODS: We used an internal genetic testing database to create a cohort of individuals who received tests including one or more high-penetrance hereditary cancer susceptibility gene. We then identified, after testing, bilateral mastectomy, bilateral salpingo-oophorectomy (BSO), and total hysterectomy procedures in electronic health record and claims data through 2019. We describe surgery utilization by genetic test results and National Comprehensive Cancer Network (NCCN) guidelines. RESULTS: The cohort included 1020 individuals, 16% with pathogenic/likely pathogenic (P/LP) variants in one or more of the following genes: BRCA1, BRCA2, CHEK2, APC, MUTYH, ATM, MSH2, PALB2, BRIP1, MLH1, MSH6, EPCAM, FLCN, RAD51C, RAD51D, or TP53. Among individuals with P/LP variants making them candidates for mastectomy, BSO, or hysterectomy per NCCN guidelines, 34% (33/97), 24% (23/94), and 8% (1/12), respectively, underwent surgery during follow-up. Fifty-three percent (18/37) of hysterectomies were among APC, BRCA1, and BRCA2 P/LP variant heterozygotes, typically concurrent with BSO. Three individuals with variants of uncertain significance (only) and 22 with negative results had prophylactic surgery after genetic testing. CONCLUSIONS: Uptake of risk-reducing surgery following usual care genetic testing appears to be lower than in studies that actively recruit high-risk patients and provide testing and follow-up care in specialized settings. Factors in addition to genetic test results and NCCN guidelines motivate prophylactic surgery use and deserve further study.

Cost-effectiveness of population-wide genomic screening for Lynch syndrome in the United States.

PURPOSE: Genomic screening for Lynch syndrome (LS) could prevent colorectal cancer (CRC) by identifying high-risk patients and instituting intensive CRC screening. We estimated the cost-effectiveness of a population-wide LS genomic screening vs family history-based screening alone in an unselected US population. METHODS: We developed a decision-analytic Markov model including health states for precancer, stage-specific CRC, and death and assumed an inexpensive test cost of $200. We conducted sensitivity and threshold analyses to evaluate model uncertainty. RESULTS: Screening unselected 30-year-olds for LS variants resulted in 48 (95% credible range [CR] = 35-63) fewer overall CRC cases per 100,000 screened individuals, leading to 187 quality-adjusted life-years (QALYs; 95% CR = 123-260) gained at an incremental cost of $24.6 million (95% CR = $20.3 million-$29.1 million). The incremental cost-effectiveness ratio was $132,200, with an 8% and 71% probability of being cost-effective at $100,000 and $150,000 per QALY willingness-to-pay thresholds, respectively. CONCLUSION: Population LS screening may be cost-effective in younger patient populations under a $150,000 willingness-to-pay per QALY threshold and with a relatively inexpensive test cost. Further reductions in testing costs and/or the inclusion of LS testing within a broader multiplex screening panel are needed for screening to become highly cost-effective.

Cost-effectiveness frameworks for comparing genome and exome sequencing versus conventional diagnostic pathways: A scoping review and recommended methods.

PURPOSE: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. METHODS: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. RESULTS: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. CONCLUSION: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities.

OBJECTIVES: Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods. METHODS: We used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use. RESULTS: We found that the measurement of core elements of value-health outcomes and cost-are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians' productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians' workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians' work can improve productivity but may reduce skills. CONCLUSIONS: The potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.

A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.

OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study. METHODS: We used longitudinal RWD for a cohort of adults (n = 4247) from the Cystic Fibrosis Foundation Patient Registry to compare outcomes of an LTx referral policy based on machine learning (ML) mortality risk predictions to referral based on (1) forced expiratory volume in 1 second (FEV1) alone and (2) heterogenous usual care (UC). We then developed a patient-level simulation model to project number of patients referred for LTx and 5-year survival, accounting for transplant availability, organ allocation policy, and heterogenous treatment effects. RESULTS: Only 12% of patients (95% confidence interval 11%-13%) were referred for LTx over 5 years under UC, compared with 19% (18%-20%) under FEV1 and 20% (19%-22%) under ML. Of 309 patients who died before LTx referral under UC, 31% (27%-36%) would have been referred under FEV1 and 40% (35%-45%) would have been referred under ML. Given a fixed supply of organs, differences in referral time did not lead to significant differences in transplants, pretransplant or post-transplant deaths, or overall survival in 5 years. CONCLUSIONS: Health outcomes modeling with RWD may help to identify novel ML risk prediction models with high potential real-world clinical utility and rule out further investment in models that are unlikely to offer meaningful real-world benefits.

Assessing Payers' Preferences for Real-World Evidence in the United States: A Discrete Choice Experiment.

OBJECTIVES: To rank the US payers' preferences for attributes of real-world evidence (RWE) studies in the context of chronic disease and to quantify trade-offs among them. METHODS: We conducted a discrete choice experiment in which 180 employees from payer organizations were tasked to choose between 2 RWE studies assuming they were assessing evidence to inform formulary decisions for chronic disease treatment. Each RWE study was characterized by 7 attributes with 3 levels each: very informative, moderately informative, and not measured. We used a D-optimal main-effects design. Survey data were fitted to a conditional logit model to obtain a relative measure of the ranking of importance for each attribute. RESULTS: Clinical outcomes were the most preferred attribute. It was 4.68 times as important as productivity outcomes-the least preferred attribute. It was followed by health-related quality of life (2.78), methodologic rigor (2.09), resource utilization (1.71), and external validity (1.56). CONCLUSIONS: This study provides a quantification of the value payers place on key RWE attributes. Across attributes, payers have higher preferences for clinical and health-related quality of life outcomes than the other attributes. Between attributes' levels, payers prefer high levels of information in clinical outcomes and methodologic rigor but are indifferent in other attributes. Our results bridge the gap between the information that payers seek and the attributes that RWE studies prioritize and effectively guide future research design.

Prescribing Alzheimer's Disease treatments by provider type and geographic region: a comparison among physicians, nurse practitioners, and physician assistants.

BACKGROUND: The estimated increase in Alzheimer's Disease (AD) caseload may present a logistical challenge to the US healthcare system. While nurse practitioners (NPs) and physician assistants (PAs) are increasingly delivering primary care to patients with chronic diseases, the nature of their prescribing of AD medications is largely unknown. The primary objective of this study was to compare the prescribing of AD medications across provider types (physician, NP, and PA) and geographic regions. METHODS: We conducted a retrospective cohort study using IBM MarketScan® commercial and Medicare supplemental claims to examine unique AD prescriptions prescribed between January 1, 2016, and December 31, 2019. Parallel analysis of prescriptions for another geriatric condition, osteoporosis (OP), was also conducted for comparison. RESULTS: A total of 103,067 AD prescriptions and 131,773 OP prescriptions were included in analyses. Physicians prescribed most AD prescriptions (95.65%), followed by NPs (3.37%) and PAs (0.98%). Small differences were identified among individual AD medications prescribed by physicians compared to NP/PAs. NPs/PAs prescribed a significantly higher proportion of AD prescriptions in rural as compared to urban areas (z = 0.023, 95%CI [0.018, 0.028]). CONCLUSION: Minimal variation exists in AD prescribing among physicians, NPs, and PAs, but NPs/PAs prescribe more AD prescriptions in rural areas. NPs/PAs, especially in rural areas, may play critical roles in alleviating projected workforce constraints. Further research assessing AD care, health outcomes, and costs by provider type and region is necessary to better guide healthcare workforce planning for AD care.

The Clinical Sequencing Evidence-Generating Research Consortium: Integrating Genomic Sequencing in Diverse and Medically Underserved Populations.

The Clinical Sequencing Evidence-Generating Research (CSER) consortium, now in its second funding cycle, is investigating the effectiveness of integrating genomic (exome or genome) sequencing into the clinical care of diverse and medically underserved individuals in a variety of healthcare settings and disease states. The consortium comprises a coordinating center, six funded extramural clinical projects, and an ongoing National Human Genome Research Institute (NHGRI) intramural project. Collectively, these projects aim to enroll and sequence over 6,100 participants in four years. At least 60% of participants will be of non-European ancestry or from underserved settings, with the goal of diversifying the populations that are providing an evidence base for genomic medicine. Five of the six clinical projects are enrolling pediatric patients with various phenotypes. One of these five projects is also enrolling couples whose fetus has a structural anomaly, and the sixth project is enrolling adults at risk for hereditary cancer. The ongoing NHGRI intramural project has enrolled primarily healthy adults. Goals of the consortium include assessing the clinical utility of genomic sequencing, exploring medical follow up and cascade testing of relatives, and evaluating patient-provider-laboratory level interactions that influence the use of this technology. The findings from the CSER consortium will offer patients, healthcare systems, and policymakers a clearer understanding of the opportunities and challenges of providing genomic medicine in diverse populations and settings, and contribute evidence toward developing best practices for the delivery of clinically useful and cost-effective genomic sequencing in diverse healthcare settings.

Modeling the Ex Post Real Option Value in Metastatic Melanoma Using Real-World Data.

OBJECTIVES: Real option value (ROV) is created when a drug enables a patient to live long enough to benefit from a future innovation. Few studies have quantified ROV in the real world. We aimed to estimate the ex post ROV for ipilimumab in metastatic melanoma using real-world data (RWD). METHODS: We developed a framework for calculating ROV using RWD, accounting for the health gain in the standard therapy arm and the uptake of future innovations. A Markov model was developed to estimate the quality-adjusted life-years (QALYs) gained with ipilimumab compared with chemotherapy for patients with or without subsequent cancer immunotherapy (CIT). A nationwide electronic health record-derived, deidentified database was used to estimate survival and uptake of CIT. RESULTS: The incremental QALYs gained for ipilimumab compared with chemotherapy without subsequent CIT were 1.74. With subsequent CIT, the incremental QALYs compared with chemotherapy increased by 0.92, 0.60, 0.33, 0.18, 0.10, and 0.02 when CIT became available 0, 3, 6, 9, 12, and 24 months after the initiation of first-line treatment, respectively. The results were most sensitive to the survival benefit of ipilimumab, the survival benefit of subsequent CIT, and the uptake of CIT. CONCLUSIONS: This is the first study to estimate ex post ROV using RWD. The ex post ROV was between 1% and 54% of conventional value for patients who received a diagnosis within 2 years before CIT availability. Further studies are needed to understand ROV in other disease areas, particularly those with longer survival times.

Medication persistence of targeted immunomodulators for plaque psoriasis: A retrospective analysis using a U.S. claims database.

PURPOSE: Studies of medication persistence in plaque psoriasis have shown inconsistent results, likely due to differing definitions of nonpersistence and of the permissible gap between refills. Also, medication persistence information for two recently approved drugs, apremilast and ixekizumab, is limited. METHODS: We use the Truven Health MarketScan claims database to assess persistence for six drugs: adalimumab, apremilast, etanercept, ixekizumab, secukinumab, and ustekinumab. We define the permissible gap in three ways: 150 days for ustekinumab and 90 days for all other drugs (150/90 model); 120 days for all drugs (120 model); and twice the days' supply for all drugs (days' supply model). To estimate unadjusted persistence, we use Kaplan-Meier curves, and a proportional hazards model to estimate the adjusted risk of non-persistence. RESULTS: Ustekinumab is most sensitive to changes in the definition of permissible gap, likely because of its longer maintenance dosing interval. Among targeted drug-experienced patients using ustekinumab, median persistence is 358 days (95% confidence interval: 343-371) in the 150/90 model and 189 days (179-199) in the days' supply model. Among targeted drug-experienced patients, median persistence in the days' supply model is longest for ixekizumab and secukinumab at 252 (217-301) and 222 (210-244) days, respectively. We also find that adjusted risk of nonpersistence increases by approximately 1% per year at treatment start. CONCLUSION: The definition of permissible gap meaningfully changes both absolute and ordinal estimates of medication persistence. Each definition has unique limitations, which should be considered when interpreting persistence data.

Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study.

PURPOSE: Clinical sequencing emerging in health care may result in secondary findings (SFs). METHODS: Seventy-four of 6240 (1.2%) participants who underwent genome or exome sequencing through the Clinical Sequencing Exploratory Research (CSER) Consortium received one or more SFs from the original American College of Medical Genetics and Genomics (ACMG) recommended 56 gene-condition pair list; we assessed clinical and psychosocial actions. RESULTS: The overall adjusted prevalence of SFs in the ACMG 56 genes across the CSER consortium was 1.7%. Initially 32% of the family histories were positive, and post disclosure, this increased to 48%. The average cost of follow-up medical actions per finding up to a 1-year period was $128 (observed, range: $0-$678) and $421 (recommended, range: $141-$1114). Case reports revealed variability in the frequency of and follow-up on medical recommendations patients received associated with each SF gene-condition pair. Participants did not report adverse psychosocial impact associated with receiving SFs; this was corroborated by 18 participant (or parent) interviews. All interviewed participants shared findings with relatives and reported that relatives did not pursue additional testing or care. CONCLUSION: Our results suggest that disclosure of SFs shows little to no adverse impact on participants and adds only modestly to near-term health-care costs; additional studies are needed to confirm these findings.

Correction: Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study.

The originally published version of this Article contained errors in Fig. 2. The numbers below the black arrowheads were incorrect; please see incorrect Figure in associated Correction. These errors have now been corrected in the PDF and HTML versions of the Article.

Are There Different Evidence Thresholds for Genomic Versus Clinical Precision Medicine? A Value of Information-Based Framework Applied to Antiplatelet Drug Therapy.

BACKGROUND: The threshold of sufficient evidence for adoption of clinically- and genomically-guided precision medicine (PM) has been unclear. OBJECTIVE: To evaluate evidence thresholds for clinically guided PM versus genomically guided PM. METHODS: We develop an "evidence threshold criterion" (ETC), which is the time-weighted difference between expected value of perfect information and incremental net health benefit minus the cost of research, and use it as a measure of evidence threshold that is proportional to the upper bound of disutility to a risk-averse decision maker for adopting a new intervention under decision uncertainty. A larger (more negative) ETC value indicates that only decision makers with low risk aversion would adopt new intervention. We evaluated the ETC plus cost of research (ETCc), assuming the same cost of research for both interventions, over time for a pharmacogenomic (PGx) testing intervention and avoidance of a drug-drug interaction (aDDI) intervention for acute coronary syndrome patients indicated for antiplatelet therapy. We then examined how the ETC may explain incongruous decision making across different national decision-making bodies. RESULTS: The ETCc for PGx increased over time, whereas the ETCc for aDDI decreased to a negative value over time, indicating that decision makers with even low risk aversion will have doubts in adopting PGx, whereas decision makers who are highly risk-averse will continue to have doubts about adopting aDDI. National recommendation bodies appear to be consistent over time within their own decision making, but had different levels of risk aversion. CONCLUSION: The ETC may be a useful metric for assessing policy makers' risk preferences and, in particular, understanding differences in policy recommendations for genomic versus clinical PM.

Do cancer treatments have option value? Real-world evidence from metastatic melanoma.

A change in the expectations about future treatments may change the option value of a current treatment, thereby affecting its utilization. We conducted an interrupted time series analysis using a large administrative claims database to test whether the utilization of existing cancer treatments changed after the disclosures of the then-investigational drug ipilimumab's Phase II and Phase III results among metastatic melanoma patients from 2008 to 2011. We used a multinomial logistic regression to analyze the temporal probability of receiving antineoplastic systemic therapy, surgical resection of metastasis, or both, relative to no treatment, in the first 3 months following the first metastasis diagnosis. One thousand eight hundred forty-six metastatic melanoma patients were included. After adjusting for clinical and sociodemographic variables and the underlying time trend, the disclosure of ipilimumab's Phase II result was associated with a nearly twofold immediate increase in the probability of receiving surgical resection of metastasis relative to no treatment, which was significant at 5% level. No significant effect was observed for the time trend. No significant effects were found for the announcement of the Phase III result. Our findings in metastatic melanoma provide the first empirical evidence of the impact of option value in cancer treatment decision making.

The Feelings About genomiC Testing Results (FACToR) Questionnaire: Development and Preliminary Validation.

The purpose of this study was to develop a brief instrument, the Feelings About genomiC Testing Results (FACToR), to measure the psychosocial impact of returning genomic findings to patients in research and clinical practice. To create the FACToR, we modified and augmented the Multidimensional Impact of Cancer Risk Assessment (MICRA) questionnaire based on findings from a literature review, two focus groups (N = 12), and cognitive interviews (N = 6). We evaluated data from 122 participants referred for evaluation for inherited colorectal cancer or polyposis from the New EXome Technology in (NEXT) Medicine Study, an RCT of exome sequencing versus usual care. We assessed floor and ceiling effects of each item, conducted principal component analysis to identify subscales, and evaluated each subscale's internal consistency, test-retest reliability, and construct validity. After excluding items that were ambiguous or demonstrated floor or ceiling effects, 12 items forming four distinct subscales were retained for further analysis: negative emotions, positive feelings, uncertainty, and privacy concerns. All four showed good internal consistency (0.66-0.78) and test-retest reliability (0.65-0.91). The positive feelings and the uncertainty subscales demonstrated known-group validity. The 12-item FACToR with four subscales shows promising psychometric properties on preliminary evaluation in a limited sample and needs to be evaluated in other populations.