Sunday 16 September 2012

Importance of Clinical Data Management


Clinical data is one of the most valuable assets for a pharmaceutical company and these provide vital confirmation of a drug’s efficacy and safety. Massive amount of data is collected during the entire life cycle of clinical research. Data management serve as the basis for

  • Collection
  • Storage
  • Validation
  • Analysis
  • Submission
  • Approval
  • Labelling
  • Marketing of a compound

Clinical Trial data can come from a variety of sources:
  • Investigator sites.
  • Laboratories.
  • Directly from subjects and partners.

Clinical data management (CDM) system plays a vital role in clinical trials to ensure high-quality data are captured effectively and ethically by sites staff through paper case report form (CRF) or electronic case report form (eCRF). Data collected through various phases right from the phase zero trials. These captured data are designed and made accessible for early review. Collected data will be utilized to characterize the subject/patient population and also to evaluate the safety and efficacy of drug. Structured databases are used to collect, organize and analyze clinical data. Sponsor is responsible for the integrity and quality of data and this could be ensured by collecting and transferring data from study subjects to a clinical data management system.

The sponsor of the drug will rely on this data to secure the marketing rights. However, even in the case of a clinical trial producing negative results, clinical data provides certain informational advantages to its holder for e.g.,Clinical trial results can be applied to make adjustments to other R&D projects in the same or a related therapy area and also assist in forecasting the probability of the R&D programme leading to a marketable product. A key condition for the subsistence of database rights is that there must have been ‘a qualitative or quantitative substantial investment in either the obtaining, verification or presentation of the contents of the database.

(CDMS) must be monitored, to ensure a reliable and effective base for
  • New Drug application (NDA) Submission
  • Clinical Science Reports
  • Clinical Planning
  • Decision-Making
  • Process enhancement
According to the Data Warehousing Institute (DWI) estimates the cost of bad or ‘dirty' data exceeds $600 billion annually.

Cost per patient of Clinical Studies
The cost per patient of clinical studies of new pharmaceuticals 

No of patients involved
Phase 1
exceeds $26,000 per patients
Phase 2
$19,300 per patient
Phase 3
$15,700 per patient.

$46,784,000 per average trial

If all the data collected during the clinical trial are invalid or unacceptable. How much would be the cost? Therefore, it is vital that CDM follows a systemic process and an established mechanism to avoid colossal loss in terms of finance and valuable resources. In order to prevent these irreparable mistakes, data collection and verification process will undergo validation at every stage. This will ensure that the data is clean, useful and consistent

Reason For erroneous data

Errors inevitably occur during data entry. Most common errors include:
  • Typographical Errors
  • Copying Errors
  • Coding Errors

Prevention of erroneous data 
Prevention of erroneous data can be carried out through various set of established mechanism as below:
Edit checks and queries.
For example, it will not allow input of future date as "visit date", or enter any other gender then male or female etc.  

Data Entry tools
The effective use of data-capture tools ensures that high-quality data are available for early review and rapid decision-making 

Data clarification form should be maintained for clarification of queries that arises during discrepancy management

Trial monitoring is to verify

  1. The rights and well-being of human subjects are protected
  2. The reported trial data are accurate, complete and verifiable from source documents
  3. The conduct of the trial is in compliance with the currently approved protocol/amendment(s), with GCP, and with applicable regulatory requirements.
Sponsor should ensure that trials are properly monitored. Part of monitor responsibilities, depending on sponsor request, is to verify data consistency with the source data or documents:
  1. Dose or therapy modifications
  2. Adverse events
  3. Concomitant medications and/or intermittent illnesses
  4. Visits, labs, examinations and tests required by protocol, but failed to perform.
All the above mentioned process can be done in an effective manner if we have Data management plan (DMP) in place.

Benefits of DMP:-
The work to be done and responsibilities are clearly stated at the start of the study so everyone knows what is expected.

The expected documents are listed at the start of the study so they can actually be produced during the course of rather than after, the conduct of the study.

Benefits of Data Validation
Missing data identification.
Handling missing data for data cleaning purposes must answer following questions:

  • Was missing data collected on CRFs?
  • Was missing data lost during data load or database manipulations?
  • Was missing data loaded fully and correctly?

If all those questions are answered "Yes" then data cleaning job for missing data is completed.
Data reconciliation, which take place at the end of clinical trial and refers to a process that compares two sets of files to make sure they are in agreement
Queries that arise during the reconciliation of the data should be handled in the same manner in which clinical queries are handled Standard operating procedures (SOP) and quality analysis should be a part of every study

Conclusion:-But it is still a challenge for most of data managers and clinical team leads to distinguish "dirty data" from actual Protocol deviations. In fact, it is essential to have full, clean and accurate data to determine the Quality, safety and efficacy of the product.


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