Evaluation of US oncology electronic health record real-world data to reduce uncertainty in health technology appraisals: a retrospective cohort study.

OBJECTIVES: Examine whether data from early access to medicines in the USA can be used to inform National Institute for Health and Care Excellence (NICE) health technology assessments (HTA) in oncology. DESIGN: Retrospective cohort study. SETTING: Oncology-based community and academic treatment centres in the USA. PARTICIPANTS: Patients present in a nationwide electronic health record (EHR)-derived deidentified database. INTERVENTIONS: Cancer drugs that underwent NICE technology appraisal (TA) between 2014 and 2019. PRIMARY AND SECONDARY OUTCOME MEASURES: The count and follow-up time of US patients, available in the EHR, who were exposed to cancer drugs of interest in the period between Food and Drug Administration (FDA) approval and dates relevant to the NICE appraisal process. RESULTS: In 59 of 60 TAs analysed, the cancer therapy was approved in the USA before the final appraisal by NICE. The median time from FDA approval to the publication of NICE recommendations was 18.5 months, at which time the US EHR-derived database had, on average, 269 patients (SD=356) exposed to the new therapy, with a median of 75.3 person-years (IQR: 13.1-173) in time-at-risk. A case study generated evidence on real-world overall survival and treatment duration. CONCLUSIONS: Across different cancer therapies, there was substantial variability in US real-world data accumulated between FDA approval and NICE decision milestones. The applicability of these data to generate evidence for HTA decision-making should be assessed on a case-by-case basis depending on the intended HTA use case.

Perspectives on Patient-Reported Outcome Data After Treatment Discontinuation in Cancer Clinical Trials.

OBJECTIVES: Patient-reported outcome (PRO) data are critical in understanding treatments from the patient perspective in cancer clinical trials. The potential benefits and methodological approaches to the collection of PRO data after treatment discontinuation (eg, because of progressive disease or unacceptable drug toxicity) are less clear. The purpose of this article is to describe the Food and Drug Administration's Oncology Center of Excellence and the Critical Path Institute cosponsored 2-hour virtual roundtable, held in 2020, to discuss this specific issue. METHODS: We summarize key points from this discussion with 16 stakeholders representing academia, clinical practice, patients, international regulatory agencies, health technology assessment bodies/payers, industry, and PRO instrument development. RESULTS: Stakeholders recognized that any PRO data collection after treatment discontinuation should have clearly defined objectives to ensure that data can be analyzed and reported. CONCLUSIONS: Data collection after discontinuation without a justification for its use wastes patients' time and effort and is unethical.

Improving Transparency to Build Trust in Real-World Secondary Data Studies for Hypothesis Testing-Why, What, and How: Recommendations and a Road Map from the Real-World Evidence Transparency Initiative.

Real-world data (RWD) and the derivations of these data into real-world evidence (RWE) are rapidly expanding from informing healthcare decisions at the patient and health system level to influencing major health policy decisions, including regulatory approvals and coverage. Recent examples include the approval of palbociclib in combination with endocrine therapy for male breast cancer and the inclusion of RWE in the label of paliperidone palmitate for schizophrenia. This interest has created an urgency to develop processes that promote trust in the evidence-generation process. Key stakeholders and decision-makers include patients and their healthcare providers; learning health systems; health technology assessment bodies and payers; pharmacoepidemiologists and other clinical reseachers, and policy makers interested in bioethical and regulatory issues. A key to optimal uptake of RWE is transparency of the research process to enable decision-makers to evaluate the quality of the methods used and the applicability of the evidence that results from the RWE studies. Registration of RWE studies-particularly for hypothesis evaluating treatment effectiveness (HETE) studies-has been proposed to improve transparency, trust, and research replicability. Although registration would not guarantee better RWE studies would be conducted, it would encourage the prospective disclosure of study plans, timing, and rationale for modifications. A joint task force of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) recommended that investigators preregister their RWE studies and post their study protocols in a publicly available forum before starting studies to reduce publication bias and improve the transparency of research methods. Recognizing that published recommendations alone are insufficient, especially without accessible registration options and with no incentives, a group of experts gathered on February 25 and 26, 2019, in National Harbor, Maryland, to explore the structural and practical challenges to the successful implementation of the recommendations of the ISPOR/ISPE task force for preregistration. This positioning article describes a plan for making registration of HETE RWE studies routine. The plan includes specifying the rationale for registering HETE RWE studies, the studies that should be registered, where and when these studies should be registered, how and when analytic deviations from protocols should be reported, how and when to publish results, and incentives to encourage registration. Table 1 summarizes the rationale, goals, and potential solutions that increase transparency, in addition to unique concerns about secondary data studies. Definitions of terms used throughout this report are provided in Table 2.

Improving transparency to build trust in real-world secondary data studies for hypothesis testing-Why, what, and how: recommendations and a road map from the real-world evidence transparency initiative.

Real-world data (RWD) and the derivations of these data into real-world evidence (RWE) are rapidly expanding from informing healthcare decisions at the patient and health system level to influencing major health policy decisions, including regulatory approvals and coverage. Recent examples include the approval of palbociclib in combination with endocrine therapy for male breast cancer and the inclusion of RWE in the label of paliperidone palmitate for schizophrenia. This interest has created an urgency to develop processes that promote trust in the evidence-generation process. Key stakeholders and decision-makers include patients and their healthcare providers; learning health systems; health technology assessment bodies and payers; pharmacoepidemiologists and other clinical reseachers, and policy makers interested in bioethical and regulatory issues. A key to optimal uptake of RWE is transparency of the research process to enable decision-makers to evaluate the quality of the methods used and the applicability of the evidence that results from the RWE studies. Registration of RWE studies-particularly for hypothesis evaluating treatment effectiveness (HETE) studies-has been proposed to improve transparency, trust, and research replicability. Although registration would not guarantee better RWE studies would be conducted, it would encourage the prospective disclosure of study plans, timing, and rationale for modifications. A joint task force of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) recommended that investigators preregister their RWE studies and post their study protocols in a publicly available forum before starting studies to reduce publication bias and improve the transparency of research methods. Recognizing that published recommendations alone are insufficient, especially without accessible registration options and with no incentives, a group of experts gathered on February 25 and 26, 2019, in National Harbor, Maryland, to explore the structural and practical challenges to the successful implementation of the recommendations of the ISPOR/ISPE task force for preregistration. This positioning article describes a plan for making registration of HETE RWE studies routine. The plan includes specifying the rationale for registering HETE RWE studies, the studies that should be registered, where and when these studies should be registered, how and when analytic deviations from protocols should be reported, how and when to publish results, and incentives to encourage registration. Table 1 summarizes the rationale, goals, and potential solutions that increase transparency, in addition to unique concerns about secondary data studies. Definitions of terms used throughout this report are provided in Table 2.

Using Real-World Data in Health Technology Assessment (HTA) Practice: A Comparative Study of Five HTA Agencies.

BACKGROUND: Reimbursement decisions are conventionally based on evidence from randomised controlled trials (RCTs), which often have high internal validity but low external validity. Real-world data (RWD) may provide complimentary evidence for relative effectiveness assessments (REAs) and cost-effectiveness assessments (CEAs). This study examines whether RWD is incorporated in health technology assessment (HTA) of melanoma drugs by European HTA agencies, as well as differences in RWD use between agencies and across time. METHODS: HTA reports published between 1 January 2011 and 31 December 2016 were retrieved from websites of agencies representing five jurisdictions: England [National Institute for Health and Care Excellence (NICE)], Scotland [Scottish Medicines Consortium (SMC)], France [Haute Autorité de santé (HAS)], Germany [Institute for Quality and Efficacy in Healthcare (IQWiG)] and The Netherlands [Zorginstituut Nederland (ZIN)]. A standardized data extraction form was used to extract information on RWD inclusion for both REAs and CEAs. RESULTS: Overall, 52 reports were retrieved, all of which contained REAs; CEAs were present in 25 of the reports. RWD was included in 28 of the 52 REAs (54%), mainly to estimate melanoma prevalence, and in 22 of the 25 (88%) CEAs, mainly to extrapolate long-term effectiveness and/or identify drug-related costs. Differences emerged between agencies regarding RWD use in REAs; the ZIN and IQWiG cited RWD for evidence on prevalence, whereas the NICE, SMC and HAS additionally cited RWD use for drug effectiveness. No visible trend for RWD use in REAs and CEAs over time was observed. CONCLUSION: In general, RWD inclusion was higher in CEAs than REAs, and was mostly used to estimate melanoma prevalence in REAs or to predict long-term effectiveness in CEAs. Differences emerged between agencies' use of RWD; however, no visible trends for RWD use over time were observed.

Global topological features of cancer proteins in the human interactome.

MOTIVATION: The study of interactomes, or networks of protein-protein interactions, is increasingly providing valuable information on biological systems. Here we report a study of cancer proteins in an extensive human protein-protein interaction network constructed by computational methods. RESULTS: We show that human proteins translated from known cancer genes exhibit a network topology that is different from that of proteins not documented as being mutated in cancer. In particular, cancer proteins show an increase in the number of proteins they interact with. They also appear to participate in central hubs rather than peripheral ones, mirroring their greater centrality and participation in networks that form the backbone of the proteome. Moreover, we show that cancer proteins contain a high ratio of highly promiscuous structural domains, i.e., domains with a high propensity for mediating protein interactions. These observations indicate an underlying evolutionary distinction between the two groups of proteins, reflecting the central roles of proteins, whose mutations lead to cancer. CONTACT: paul.bates@cancer.org.uk SUPPLEMENTARY INFORMATION: The interactome data are available though the PIP (Potential Interactions of Proteins) web server at http://bmm.cancerresearchuk.org/servers/pip. Further additional material is available at http://bmm.cancerresearchuk.org/servers/pip/bioinformatics/

Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis.

BACKGROUND: Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable. RESULTS: We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome--the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis. CONCLUSION: The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest.