IRUG 2013 – Beyond Data Review Raj Indupuri Chandi Kodthiwada 

Why JReview Graphs? •Supports self-service analysis •Get data insights in near real-time for ongoing trials •Leverage drill-down capabilities to: • Visually make it easy to identify Outliers • Safety and Efficacy analysis •Reduce dependence on SAS Programmers •Overall increases speed and efficiencies for data review and analysis 2 

Typical Analyses • Trends of Change from Baseline across visits • PK – PD Analysis • Shift Analysis • Abnormal changes from previous visit • Compare trends of multiple Efficacy and/or Safety parameters • Visit level or Treatment Group level review 3 

Trends of Change from Baseline across visits 4 • Numeric Result – Post Baseline Visit – Y-axis • Baseline Result – X-axis • Visit Number (By variable) 

PK – PD Analysis 5 • PK vs PD Parameters • PD Numeric result (Y-axis as Parameter) • PK Parameter Numeric Result (X- axis Parameter) 

Shift Analysis 6 • Scatter Plot (w Numeric Reference Ranges) • Numeric result – Bicarbonate (Y-axis) • Numeric result – Amylase (X- axis) 

Abnormal changes from previous visit 7 • Spaghetti Plots (Multiline ChartItem Value by Case) • Numeric result (Y-axis) • Visit Numbers (X-axis) 

Compare trends of multiple Efficacy and/or Safety parameters 8 • Multi Item Trend Plots (Line Chart ItemSummary vs Category) • Numeric result (Y-axis) • Visit Numbers (X-axis) • Lab Test Name (By variable) 

Visit level or Treatment Group level review 9 • Change of Mean of Test Result Plots (Mean+sStdDev vs Category) • Numeric result (Y-axis) • Visit Numbers (X-axis) 

Thank you