Exposure adjustment in Risk-based monitoring in clinical trials with software implementation in jmp Anastasia Dmitrienko (Blue Valley North High School, Overland Park, KS) Richard C. Zink, Ph.D. (JMP Life Sciences, SAS Institute, Cary, NC) Risk-Based Monitoring Exposure-Adjusted Incidence Rates Windowing by Time Interval Risk-based monitoring is used in clinical trials to identify sites with unusual characteristics such as excessive adverse event risks (TransCelerate Biopharma Inc., 2013; Zink, 2014) Primary goal is to detect risks to patient safety or anomalies indicating problems in data quality Monitoring rules can be defined using control limits based on expected variability of the outcome of interest (e.g., 95% confidence intervals for means or proportions); sites outside the control limits are flagged Control limits are commonly visualized using funnel plots (Spiegelhalter, 2005) Our objective is to develop monitoring rules for trial sites with an adjustment for the time on study (exposure) Exposure-adjusted incidence rates (EAIRs) are introduced to account for exposure variability across sites. Control limits for EAIRs can be computed using asymptotic confidence intervals for the corresponding parameters and presented using funnel plots or heat maps   Exposure-adjusted monitoring can be further enhanced by breaking time into time intervals to account for changing risk over time Outcomes of interest can be “binned” using pre-defined time intervals (e.g., 30-days) Absolute: Intervals defined according to calendar time, starting with the first patient in the trial Relative: Intervals defined according to study day within site, starting with the first patient within site Heat maps (Figure 3) help visualize risks across trial sites and time periods Each cell represents a specific time interval at each site Control limits are developed for each row and classified as low, normal or high Several sets of control limits can be applied, e.g., 80% and 95% limits, and multiple categories can be created Figure 3: Exposure-Adjusted Heat Map (Click to enlarge) Figure 2: Exposure-Adjusted Funnel Plot (Click to enlarge) Case Study & Standard Rates Phase III trial in patients with schizophrenia conducted at 98 sites around the world Outcome of interest: Patient’s early discontinuation from the trial (binary outcome) Blinded summary of 546 patients (range of 1 to 14 patients per site) Trial participation ranged from 1 to 11 months Figure 1 shows monitoring rules to identify trial sites with unusually low or unusually high early discontinuation rates based on a the binary outcome without accounting for differences in exposure Summary Other Outcomes Standard rules used in RBM do not account for exposure which may vary considerably across sites in a clinical trial The resulting monitoring rules are likely to be unreliable Funnel plots and heat maps based on EAIRs were developed These monitoring rules support stratification by time interval and enable clinical trial sponsors to better detect risk changes over time These monitoring tools were implemented using JMP software to analyze early discontinuation of patients in a Phase III clinical trial Constructing a funnel plot based on counts is possible with Poisson control limits. The difference is that a patient may theoretically have an unlimited number of events, with increasingly smaller probabilities. For example, Poisson methods may be used for the number of adverse events experienced by a patients. If counts are used, the standard error must be adjusted accordingly, and the control limits will be constructed using the approach described earlier. Figure 1: Standard Funnel Plot (Click to enlarge) Click Here for References

Figure 1: Standard Funnel Plot Figure 1: Control limits are based on asymptotic 95% and 99.7% confidence intervals for proportions Click to Return

Figure 2: Exposure-Adjusted Funnel Plot Figure 2: Control limits are based on asymptotic 95% and 99.7% confidence intervals for EAIR Click to Return

Figure 3: Exposure-Adjusted Heat Map Dark blue: Very low Light blue: Low Green: Normal Orange: High Red: Very high Figure 3: Control limits are based on asymptotic 80% and 95% confidence intervals for EAIR Click to Return

References Chan, I.S.F, Wang, W.W.B. (2009). On analysis of the difference of two exposure-adjusted Poisson rates with stratification: from asymptotic to exact approaches. Statistics in Biosciences. 1, 65-79. Liu, G.F., Wang, J., Liu, K., Snavely, D.B. (2006). Confidence intervals for an exposure adjusted incidence rate difference with applications to clinical trials. Statistics in Medicine. 25, 1275-1286. Spiegelhalter, D.J. (2005). Funnel plots for comparing institutional performance. Statistics in Medicine. 24, 1185-1202. TransCelerate Biopharma Inc. (2013). Position paper: Risk-based monitoring methodology. Available at http://www.transceleratebiopharmainc.com/assets/risk-based-monitoring/. Zink, R.C. (2014). Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS. SAS Press. Click to Return