Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs; it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases.<\/p>\n","protected":false},"excerpt":{"rendered":"
Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193724],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-941538","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-5-11","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"arXiv","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2305.06850","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Lauren E Dang","user_id":0,"rest_url":false},{"type":"text","value":"Susan Gruber","user_id":0,"rest_url":false},{"type":"text","value":"Hana Lee","user_id":0,"rest_url":false},{"type":"text","value":"Issa Dahabreh","user_id":0,"rest_url":false},{"type":"text","value":"Elizabeth A Stuart","user_id":0,"rest_url":false},{"type":"text","value":"Brian D Williamson","user_id":0,"rest_url":false},{"type":"text","value":"Richard Wyss","user_id":0,"rest_url":false},{"type":"text","value":"Iv\u00e1n D\u00edaz","user_id":0,"rest_url":false},{"type":"text","value":"Debashis Ghosh","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Emre Kiciman","user_id":31739,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Emre Kiciman"},{"type":"text","value":"Demissie Alemayehu","user_id":0,"rest_url":false},{"type":"text","value":"Katherine L Hoffman","user_id":0,"rest_url":false},{"type":"text","value":"Carla Y Vossen","user_id":0,"rest_url":false},{"type":"text","value":"Raymond A Huml","user_id":0,"rest_url":false},{"type":"text","value":"Henrik Ravn","user_id":0,"rest_url":false},{"type":"text","value":"Kajsa Kvist","user_id":0,"rest_url":false},{"type":"text","value":"Richard Pratley","user_id":0,"rest_url":false},{"type":"text","value":"Mei-Chiung Shih","user_id":0,"rest_url":false},{"type":"text","value":"Gene Pennello","user_id":0,"rest_url":false},{"type":"text","value":"David Martin","user_id":0,"rest_url":false},{"type":"text","value":"Salina P Waddy","user_id":0,"rest_url":false},{"type":"text","value":"Charles E Barr","user_id":0,"rest_url":false},{"type":"text","value":"Mouna Akacha","user_id":0,"rest_url":false},{"type":"text","value":"John B Buse","user_id":0,"rest_url":false},{"type":"text","value":"Mark van der Laan","user_id":0,"rest_url":false},{"type":"text","value":"Maya Petersen","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[],"msr_project":[788789],"publication":[],"video":[],"download":[],"msr_publication_type":"miscellaneous","related_content":{"projects":[{"ID":788789,"post_title":"Causal Learning-Inference","post_name":"causal-learning-inference","post_type":"msr-project","post_date":"2021-10-26 21:59:44","post_modified":"2021-11-25 18:59:46","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/causal-learning-inference\/","post_excerpt":"Current AI models can learn spurious correlation during data-fitting process, which can incur the failure of generalizing to new unseen domains. 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