QbD and RBM strategies using Excel
An Wholistic View of Risk
The Dashboard worksheet displays all your site-specific KRI relative values and signals sites which have KRI metrics falling outside of their respective threshold values. The wholistic perspective of all your sites KRI distributions and histories allows to determine the overall risk of your study as it progresses. The Dashboard also has a function to highlight sites of interest for focused, in-depth analysis.
Key Risk Indicators Metrics and Statistics
The Metrics worksheet provides statistics computed from the data imported into the workbook. It namely displays current calculated Risk indicators metrics and their periodic variation along with manually entered limits to compare the risk metrics against and trigger risk signals. The statistic including the means and standard deviations are displayed next to the limits for a quick statistical evaluation of set limits. The cumulative probability P[X≤xi], that a randomly observed value will be less than or equal to a calculated risk metric, is also calculated for each individual site’s risk metrics to help evaluate outliers.
Optimizing Site Visits with RBM
The PI worksheet displays individual site status including estimated workload which is useful for scheduling on-site visits optimizing site coordinators and CRAs’ precious time. Workload is calculated as a function of the number of subjects enrolled since last monitoring visit, the number of AEs recorded since the last monitoring visit and the number of pages which currently require SDV. The PI sheet also displays site-specific Query rate and Error rates, as these metrics are especially important to determine the level of SDV required at individual sites for Risk-Based Monitoring.
KRI metrics histories displays how risk progresses during your study and allows to evaluate the impact of mitigation actions and determine if risks are properly managed. KRI metrics histories also allows for the assessment of the fitness of set threshold limits with respect to calculated metrics to effectively identify outliers. The information contained in the KRI metrics histories is instrumental to risk review and the traceability of your risk management process. It is displayed in a fashion that eases communication with clinical operations.
Terminal Digit Analysis
Deliberate data fraud is rare but can have significant impact on trial integrity. One straightforward way to fabricate data is to take existing data and copy them within or across study subjects. Such a data propagation method results in certain values occurring more often than others as humans tend to favor certain digits when fabricating numbers. A simple way to detect this type of data fabrication is to calculate the frequency of terminal digits from data collected. The Data Fraud Detector has been designed to do just that.
Real subject-specific data are expected to vary to a certain extent from one physician’s office visit to the next. An inlier analysis can be used to detect whether this is the case or not. Inlier analysis specifically evaluates how close to their respective means a set of multivariate observations lies and suggests fabrication if those observations, taken together, lie abnormally close to their respective mean. The Data Fraud Detector can be used to perform this type of analysis.
Documenting Risk Management Processes
Tools and processes for the implementation Central Monitoring require supporting documents. A Quality Risk Management Plan (IQRMP) presents the quality management strategy including the activities of risk-assessment, KRI calculations and central monitoring which are to be implemented in the course of study. Central Monitoring Reports contains periodic analysis of risk signal that are communicated to stakeholders and ensures the traceability of the central monitoring process. Validation Plans and associated validation scripts ensure the proper working of risk calculation tools. User manuals supports the transfer of knowledge between staff assigned to the operation of risk calculation tools. Templates offered with the RI Calculator greatly reduces the work required to produce these documents.
The Risk Index Calculator
The Risk Index Model
The RI model has been developed to assist central monitors in the calculation of site-specific KRI metrics and the generation of associated risk signals using the RI calculator. The model aims to use periodic datasets exported from EDC or CTMS systems to perform risk analyses and produce reports that keep trial management teams informed about sites' status and provide them with information concerning sites’ needs. The RI model represents a method to orient QbD and Risk-Based monitoring approaches using simple statistics to supports the objective to build quality into trials in a traceable fashion. In a pilot study, the RI model has shown a high level of control over quality attributable to the early detection and early communication of issue.
Building Quality into Clinical Trials
Regulatory and economic incentives prompt clinical research organizations to integrate risk management strategies into clinical operations and to adopt technological enablers for the efficient monitoring of clinical trials. As quality management shifts from on-site monitoring to centralized monitoring, the new clinical trial monitoring paradigm involves data managers in the computing of Key Risk Indicators (KRI), their ongoing analysis and their reporting to different stakeholders.
With the Risk Index (RI) model, xlsmetrics introduces a method to Plan (identify factors critical to quality and their relative importance, develop risk mitigation strategies and set adapted monitoring intensity), Do (calculate risk metrics and compare them against set thresholds), Check (evaluate metrics progress and their respective thresholds and identify risk signals to be acted upon), and Act (verify what matters and perform proper mitigative actions) to build quality into clinical trials.