Can Statistical monitoring really improve data integrity? A consolidated review of multiple analyses using JMP Clinical Chris Wells Copenhagen May 4th 2017

Summary What is ‘Data Integrity’? What is the opposite of ‘Data Integrity’? Objective Method Details of the 10 tests run Challenges Results Conclusion

What is ‘Data Integrity’? Fundamental component of information security Broadly, accuracy and consistency of data stored in a database over its entire lifecycle but it may have widely different meanings depending on the specific context. Within context of a clinical trial data being accurate, complete and unique to the associated ‘living’ patient. Data Integrity is needed to protect the well-being of study participants and maintain the integrity of the final analysis results, so that patients, regulators and all interested parties can have belief in the data

What is the opposite of ‘Data Integrity’? Data corruption, which is a form of data loss. Many methods for reviewing the impact of ‘missing data’ . Data recorded incorrectly results in a loss of data for that patient or falsified data results in bias of the trial results Missing data: methods exist to review impact of data corruption, but not readily utilised within the clinical trial setting, therefore it is important to be vigilant and apply statistical monitoring of the data in order to keep data corruption/loss to a minimum

Objective Review of overall findings from 20 clinical studies using JMP Clinical,report on the level of findings and how they contributed to enhancing the overall data integrity.

Method Review demographic distributions Data Quality and Fraud tab of the JMP Clinical Tool, displays further tests that can be run. All 10 tests were run, with the addition of further looking at patients who have no Adverse Events reported plus further graphical representations of multivariate inliers/outliers

Details of the 10 tests run

Used Version 5.0 JMP Clinical, on a Citrix Server.. SV dataset not used in house No training courses available initially. Used Version 5.0 JMP Clinical, on a Citrix Server. Difficulty in applying Holidays/Events in a standard way due to current computing environment. SV dataset not used in-house (Statistical Programmers derive a visit dataset, therefore SV datasets are often of poor quality) No training courses available initially so difficulty understanding available options within the tool and complex understanding of interpretation of outputs.

Based on 20 studies, median number of patients = 549 (min:max (22;3080) median number of sites = 99 (min;max 4; 353), highlighted 32 pairs of possible duplicate patients of whom 18 warranted further investigation. 1 study not included in further analysis due to low number of patients and lack of data / findings

The following results were found in a small number of sites within each study. 14/19 studies displayed evidence of possible carryover or calibration issues, 10/19 studies displayed evidence of possible estimation of a minor parameter 9/19 studies displayed evidence of rounding error, whilst 12/19 appeared to show duplication of results across 3 or more parameters between 2 or more patients All studies had data issues flagged that would have been queried but 12 studies were identified as having unusual data points that may not normally be flagged by clinical data management, including 6 studies where patterns were observed in the multivariate space. 7/19 studies (3394 patients) were reviewed and it was found that 499 patients (14.9%) had not had adverse events recorded. This could be due to several reasons including lag time at site or length of time in study, as well as other reasons that are worthy of investigation

Conclusion Ideal model for implementation of the task still under discussion Eagerly awaiting IT to finish their validation of version 6 Still challenges in interpreting output and feeding back to study teams in a way that issues can be addressed. This exercise has enabled a valuable insight into the condition of our clinical data. Overall it appears that our data is of high quality but it has enabled us to identify priority areas for investment of time/training or process changes to further enhance the already high data integrity that currently exists.