Clinipace

I often joke with colleagues that “statistics rule the world,” but I have to admit I’m only half joking. In polite society I usually receive a few chuckles when I make this statement, but within clinical research this mantra is an absolute. If your goal is to achieve a definitive conclusion, you must start with sample size planning. Any clinical study designed to investigate efficacy of a new treatment MUST know how many subjects are needed to arrive confidently at a definitive answer.

While this post is not intended to be an exhaustive guide, it is a reminder that clinical research is all about gathering data to draw conclusions about new treatments, confidently. Without confidence what have we really achieved?

There are three possible answers to evaluating the clinical effectiveness of a new treatment:

1) The study has sufficient information to confidently conclude the new treatment is more effective than its comparator;
2) The study has sufficient information to confidently conclude the new treatment is not more effective than its comparator; or
3) There is insufficient information to conclude with confidence whether the new treatment is more effective than its comparator.

Conclusions 1) and 2) represent definitive decisions that can be made with an acceptably high level of confidence.  Conclusion 3) often results when sample size planning is not done effectively and, at study end, the researcher finds out not enough subjects were studied to allow for a definitive conclusion.

A common misconception is that 30 subjects per treatment arm are adequate for many studies.  The number 30 is important in the statistics world but not for sample size decisions.  Many studies need hundred, even thousands, of study subjects to reach their goal, whereas some studies (such as crossover or tightly controlled lab studies) may need fewer subjects per treatment arm.  The needed sample size for each study is uniquely determined by:

• The primary research objective,
• Primary study endpoint,
• Expected relative efficacy of the investigational treatment relative to its comparator, and
• Measures of confidence desired for the resulting conclusion.

To better understand statistical concepts in clinical research I’ve compiled a couple of articles that are worth a read:

Truth, Lies and Statistical Tests: Powering the Study
Clinical trial design — for beginners

Ron Marks
Chief Scientific Officer

The end of a clinical study is usually quite hectic between trying to close a database and resolving all the outstanding study issues.  We believe there is a strong correlation between the amount of planning time upfront and the time and effort required on the back end of a study.

Proper planning upfront can greatly reduce the stress of closing a study.  For example, identifying questionable clinical data early in a site’s study activity can bring to light the need for clarification of study procedures or more site training.  Ultimately, sites will experience fewer overall errors and more efficient and quick resolution of any data errors that are found.  Likewise, risk-based remote clinical site monitoring can lead to earlier resolution of data errors, resulting in shorter time to database lock at the end of a study.

These are just a couple of examples of the types of upfront planning opportunities that can lead to reduced time and stress in closing a study.  But try as you might, it is all but impossible for a clinical study to be conducted exactly as planned.  A good example of this is the recruitment rate, since most studies take longer to enroll than expected.  Another is the dropout rate, which may be higher than expected resulting in the need to enroll additional subjects or run the study longer.

By identifying potential roadblocks like these upfront, you have a chance to think through contingency plans rather than being surprised and needing to respond quickly and risking a suboptimal resolution.  If considered ahead of time, it is possible to identify a potential roadblock sooner  and to fix it while it is a minor annoyance rather than a major problem requiring considerable time and expense to resolve.

The best way to avoid this situation is to have access to all study information at your fingertips and the opportunity to monitor potential roadblocks that have been identified during study planning.  This essential visibility can only be provided if your study is being conducted on a technology platform that provides you and your CRO access to ongoing, real time study performance data.

Ron Marks, PhD
Chief Scientific Officer
Clinipace Worldwide