1. White Papers to Fill the Gaps of Standardization of Tables, Figures, and Listings (Creating Standard Targets) Analyses and Displays for Vital Signs, ECG, and Laboratory Analyte Measurements Mary Nilsson, Eli Lilly and Company; Wei Wang, Eli Lilly and Company; Qi Jiang, Amgen White Paper 2 – Under 1 st Round Review! White Paper 1 – Finalized October 2013 Conclusion: Industry standards have evolved over time for data collection (CDASH), observed data (SDTM), and analysis datasets (ADaM). Development of standard tables and figures with associated analyses is the next step! Having an organized process for shared learning of improved methodologies can lead to earlier safety signal detection and better characterization of the safety profile of our products. We welcome new members! Contact information on phusewiki.org. OBJECTIVE The objective of this poster is to receive feedback on recommended displays for vital signs, electrocardiogram, and laboratory analyte measurements as outlined in PhUSE Computational Science Symposium (CSS) white papers as part of a standardization and code-sharing effort. The Vision – Development of Standard Scripts for Analysis and Reporting Working Group Standard Targets and Validated Scripts IndustryFDAAcademic Data Collection Systems Observed Datasets Analysis Datasets Tables, Figures and Listings Clinical Data Flow Trial Design PRM SDTMADaM No TFL Stds Exist Industry Standards Alignment CDASH Examples of Discussed Topics Whether to report p-values and confidence intervals Handling of measures collected in reflex manner, repeated measures, unplanned measures, measurements post drug exposure Defining baseline Central versus local laboratories Choices of lab reference limits Units and transformations ECG correction factors Recommended Tables and Figures Allows for visual inspection of changes over time Can visually assess the potential impact of outliers on the central tendency summary statistic Out of range values in red Easy to see treatment differences Summary table compliments box plot so numbers are available for textual summaries Allows for visual inspection of changes over time Can visually assess the potential impact of outliers on the central tendency summary statistic Out of range values in red Easy to see treatment differences Summary table compliments box plot so numbers are available for textual summaries Two pages per measurement o One for Max. baseline vs. Max. post-baseline (shown on the left) o Another identical one for Min. baseline vs. Min. post-baseline Two pages per measurement o One for Max. baseline vs. Max. post-baseline (shown on the left) o Another identical one for Min. baseline vs. Min. post-baseline Scatter plot o Shows patient level information o Allows quick browsing through a large amount of information Shift table o Shows useful summary level statistics Treatment emergent high/low table o Shows concise summary level statistics o Allows for easy assessment of treatment difference Scatter plot o Shows patient level information o Allows quick browsing through a large amount of information Shift table o Shows useful summary level statistics Treatment emergent high/low table o Shows concise summary level statistics o Allows for easy assessment of treatment difference Acknowledgements: Special acknowledgement to members of the PhUSE Computational Science Symposium Standard Scripts for Analysis and Programming Working Group, White Papers Project Team who have contributed to the white papers, and to those who have provided review comments. Only has mean and SD - Lacks additional useful summary statistics Outliers not displayed Can be misleading when data are non-normal Only has mean and SD - Lacks additional useful summary statistics Outliers not displayed Can be misleading when data are non-normal Lack of information on patient level information o Lack of magnitude of shift Difficult to assess overall treatment difference o Very popular, however, users tend to count and create grouped percentages manually for those shifting to high from low/normal (or low from normal/high). Lack of information on patient level information o Lack of magnitude of shift Difficult to assess overall treatment difference o Very popular, however, users tend to count and create grouped percentages manually for those shifting to high from low/normal (or low from normal/high). Traditional Tables and Figures Other White Papers In Development: Adverse Events Demographics, Disposition, Medications Hepatotoxicity Non-compartmental PK Planned: QT Studies Where to Find Things PhUSE CSS Final Deliverables: www.phuse.eu, click Publications PhUSE CSS Work in Progress: www.phusewiki.org, click CSS Working Groups, then Development of Standard Scripts Script Repository (reusable code library): https://code.google.com/p/phuse-script