Analytics Approaches to Assessing Clinical Endpoint Data Consistency July 27, 2021 Alan Kott Posted in Data Analytics Share: LinkedIn0Tweet0 Clinical research sponsors collect more data on more endpoints than ever before, especially since the proliferation of remote/decentralized clinical trials. From electronic data capture systems to digital health technologies and biological data, data sets in clinical research continue to grow exponentially every year. Yet, with so many data points, quality issues and data inconsistencies that result from errors, non-adherence, or fraud often remain hidden until the end of a study. How can study teams leverage these data to monitor endpoint quality throughout the course of a study and program? An in-study data quality monitoring and analytics strategy, such as Signant’s Blinded Data Analytics, can help sponsors proactively monitor data as they flow in, providing opportunities to mitigate consistency, accuracy, and other quality issues before they can compromise a study. Study teams can leverage Signant’s analytics tools and expertise to compare data beyond the individual study; when these data are compared with site-, rater-, country-, and region-level data, analysts and biostatisticians can identify anomalies and outliers that may indicate areas of quality concern. For example, in CNS studies which rely on subjective clinician-reported outcome assessments, data analytics services can identify variations in inter- and intra-rater scoring that can compromise endpoint data reliability. This strategy, coupled with remediation approaches such as rater training programs or independent central reviews of assessments, detects and mitigates common challenges that endanger study success including: Participant eligibility issues Variability in raters’ scoring and administration of assessments Protocol non-adherence Errors Fraud Monitoring data quality in these ways improves endpoint reliability and prevents costly trial failures, but it is equally important to have experienced data scientists, analysts, and clinicians interpret the data to ensure conclusions are correct. Learn more about Signant’s analytics solutions and services, and subscribe to our blog to stay apprised of important trends and topics. Download Blog Alan Kott, MUDr. Clinical Vice President and Practice Lead, Data Analytics Recent Posts Scientific Advisory Get to Know the SME: Manuela Bossi May 19, 2022 Signant Health Learn more Scientific Advisory Signant Health’s Clinical Scientist Jennifer Olt Receives ISCTM New Investigator Award May 11, 2022 Signant Health Learn more eCOA Patient-Reported Outcomes in Medical Device Trials – What We Can Learn From the FDA Guidance May 10, 2022 Bill Byrom Learn more eCOA Measurement Comparability of Electronic and Paper Administration of Visual Analogue Scales: A Review of Published Studies Mar 21, 2022 Bill Byrom Learn more Events Leveraging the Power of Real-Time Data Aggregation: Virtual Event Recap Mar 02, 2022 Signant Health Learn more
Clinical research sponsors collect more data on more endpoints than ever before, especially since the proliferation of remote/decentralized clinical trials. From electronic data capture systems to digital health technologies and biological data, data sets in clinical research continue to grow exponentially every year. Yet, with so many data points, quality issues and data inconsistencies that result from errors, non-adherence, or fraud often remain hidden until the end of a study. How can study teams leverage these data to monitor endpoint quality throughout the course of a study and program? An in-study data quality monitoring and analytics strategy, such as Signant’s Blinded Data Analytics, can help sponsors proactively monitor data as they flow in, providing opportunities to mitigate consistency, accuracy, and other quality issues before they can compromise a study. Study teams can leverage Signant’s analytics tools and expertise to compare data beyond the individual study; when these data are compared with site-, rater-, country-, and region-level data, analysts and biostatisticians can identify anomalies and outliers that may indicate areas of quality concern. For example, in CNS studies which rely on subjective clinician-reported outcome assessments, data analytics services can identify variations in inter- and intra-rater scoring that can compromise endpoint data reliability. This strategy, coupled with remediation approaches such as rater training programs or independent central reviews of assessments, detects and mitigates common challenges that endanger study success including: Participant eligibility issues Variability in raters’ scoring and administration of assessments Protocol non-adherence Errors Fraud Monitoring data quality in these ways improves endpoint reliability and prevents costly trial failures, but it is equally important to have experienced data scientists, analysts, and clinicians interpret the data to ensure conclusions are correct. Learn more about Signant’s analytics solutions and services, and subscribe to our blog to stay apprised of important trends and topics. Download Blog Alan Kott, MUDr. Clinical Vice President and Practice Lead, Data Analytics