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Statistics in the Service of Quality
During and since World War II, the use of statistics blossomed in American industry. By the mid-1970s, Japan had caught up and then expanded the use of data and analysis for product quality. Following Dr. W. Edwards Deming's philosophy, great progress was made in minimizing variability and waste.
However, statistics have not been used consistently by the pharmaceutical industry. Historically, large firms such as Abbott, Baxter, Smith Kline and French, and others had staff formally trained in statistical quality and process control. But often, small to mid-sized firms did not have anyone on staff with statistical training. These companies frequently received FDA 483 citations and warning letters for not meeting the statistical requirements of cGMPs.
The situation has changed little today. Many companies do not have anyone on staff who can implement a control chart correctly or explain the concepts behind sampling plans. This is true even though cGMPs require that staff "... shall have education, training, and experience, or any combination thereof, to enable that person to perform the assigned functions" (1).
There are indications that this may be changing in the future. FDA's guidance for industry on process validation and the International Committee for Harmonization's (ICH) guidelines for product development and risk management have attracted the attention of influential people in the industry as well as other departments in FDA (2). FDA has been training its staff in statistics for some time and apparently expects to continue. Questions concerning the application of statistics to product quality may be coming to a 483 near you soon.
In the meantime, what should personnel working in pharmaceutical quality know about applied statistics? At a minimum, the quality department should have a team with at least one person who is a certified quality engineer (CQE), as recognized by the American Society for Quality (ASQ), or who has a Bachelor's degree with a major in statistics. The person should also have a background in engineering or a physical science.
In addition to the necessary training in applied statistics for quality, the team should be acquainted with the statistical
issues in the following industry documents:
The quality team also should have access to the current edition of the United States Pharmacopeia (USP) and the Pharmaceutical Forum. The following chapters are the minimum that should be reviewed for their statistical content:
Every member of the quality team must be acquainted with the ICH documents as well. One or more of the team must be expert
in the statistics contained in the following ICH harmonized quality guidelines (available online at www.ich.org):
Sampling plans are the weakest area in pharmaceutical quality. The team must be trained and competent. Not to do so is to expose the company to great risk of material and product failures.
The team must have copies of the following documents for immediate reference when questions arise.
There are 30 ASTM International standards for statistics; far too many to list here. Readers are encouraged to consider them all for application. One new standard of particular note though is E2709, Standard Practice for Demonstrating Capability to Comply with a Lot Acceptance Procedure.
Other quality experts and statisticians will have additional topics they consider important but the material mentioned here is an excellent start. Not everyone needs be an expert in everything, but the quality team must be able to defend their performance in these complex topics with authority, competence, and confidence.
Statistics in the service of quality is not for the unqualified. All clinical trials are designed and analyzed by professional statisticians. We can do no less in the quality department—patients' lives depend upon it.
1. Code of Federal Regulations, Title 21, Food and Drugs (Government Printing Office, Washington, DC), Part 211.25.
2. FDA, Guidance for Industry: Process Validation: General Principles and Practices (CDER, Silver Spring, MD, Jan. 2011).