Using Statistics to Improve Data Quality and Maximize Trial Success

December 4, 2019
Francois Torche
Applied Clinical Trials

Centralized monitoring offers sponsors and CROs the program-wide oversight they need to successfully develop the medications of the future, says CluePoints CEO, Francois Torche.

Poor quality data has the potential to destroy the validity of a clinical trial, wasting precious time and resources while introducing sizable roadblocks into the drug development pathway. But centralized monitoring offers sponsors and contract research organizations (CROs) the program-wide oversight they need to successfully develop the medications of the future. 

The statistical analysis of real-time data can identify atypical data early, giving study organizers the opportunity to investigate and rectify issues such as fraud, sloppiness, training needs, or faulty equipment before they have a chance to impact on the trial’s outcome.

Why does it matter? 

At the base level, centralized monitoring matters because it is a key component of the 2017 ICH E6 (R2) addendum. But its potential benefits make complying to this requirement much more than a box ticking exercise. 

Today’s complex, fragmented studies make data oversight increasingly difficult. Yet reviewing information as it accumulates, says the addendum, can identify missing, inconsistent or outlying data, spot protocol deviations, and examine trends in the range, consistency and variability of data both within and across sites. It can analyze site characteristics and performance metrics, evaluate for systemic errors in data collection and flag up potential data manipulation or integrity problems.

All this makes it an extremely valuable adjuvant to traditional review activities, such as those performed by clinical data management and data reviewers. By operating at a more contextual level, it can identify site and study issues that are likely to be significantly more impactful than those discovered during transactional, record-by-record inspection processes like data cleaning.

In short, centralized monitoring that utilises sophisticated data analytics and data visualization offers previously inaccessible oversights that can be continuously used to drive up data quality.

How it works

The primary goal of statistics-based centralized monitoring is identifying operational and clinical study risks. It does that by highlighting atypical patterns of data that could signal issues related to study conduct.

This approach allows sponsors and CROs to identify issues such as fraud, tampering or sloppiness in data entry, or problems with training or study equipment, and investigate them before they impact on the trial’s integrity.

Data trends

By taking a whole-program view, statistical data monitoring can examine the entirety of a trial’s data to spot outlying data trends that could signal a problem (as opposed to Key Risk Indicators that are limited in number).

It can use statistical tests to identify sites recording an atypical mean for a given measurement or atypical variability in measurements between patients, for example. Discreet, or list, tests can identify sites reporting atypical proportions of a particular value or option, and day/time tests can, among other things, spot sites with an atypical distribution of patient assessment times.

A sponsor of a phase III study in a chronic disease, for example, was alerted to fraud by looking at date/time tests on metadata. These tests highlighted that one site had entered the ePRO diary entries of all its 15 patients at the same time. This was obviously suspicious, and the subsequent investigation led to all 15 patients being removed from the analysis-with the consequences you can imagine on the statistical power of the trial. 

In another trial, a diabetes study involving 300 patients, centralized monitoring revealed one site had an unusual lack of variability across patient vital signs measurements. It transpired upon investigation that staff were inventing missed vital signs measurements by interpolating.

Targeted data analytics 

Industry-leading centralized monitoring software will also allow sponsors and CROs to set Quality Tolerance Limit (QTL) predictors similar to KRIs but at the study level.

ICH-E6 (R2) calls for trials to contain predefined QTLs and stipulates that any deviation from these limits triggers an evaluation to determine any necessary remedial action. QTLs, which are set trial-wide, are designed to identify systemic errors, and any breach must be recorded in the clinical study report (CSR). 

KRIs and QTLs are particularly useful because they can alert sponsors to potential problems early, meaning they can be investigated and rectified before they impact on safety or become a breach.

Typical KRI examples are: visit-to-eCRF entry cycle time, missed assessment rate, auto-query rate, early termination rate and (Serious) adverse event (AE) rate. 

It could be cause for concern, for example, if the rate of AEs was 3 per person and per visit at one site while the overall trial is reporting no more than 1.2. It could be indicative of either sloppy reporting (and therefore a need for re-training) or safety issue. The KRI alert will give sponsors the opportunity to investigate and address the issue before it impacts in the validity of the study.

Indications of quality 

Using data analytics and data visualization to oversee the entirety of a clinical protocol or program gives sponsors and CROs an important tool to protect trial integrity. 

It utilises the realms of data sponsors and CROs have at their fingertips to spot potential issues early on so they can make the informed decisions that will keep their studies on track.

Data might not give us all the answers, but it can tell us which questions to ask-and therein lies the value of using statistics-based centralized monitoring to improve data quality in clinical trials.

 

Francois Torche is the CEO at CluePoints.