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Throughout my years of work in biostatistics, there are certain problems I see recurring repeatedly in clinical trials. The most common problems are incorrect study designs and a lack of sufficient exploration into study conduct processes that could affect analyses and interpretation.
When processes are ignored, or a trial does not have the expertise of an experienced statistician during study planning and design, we often receive a panicked call for help. The caller has reached the end of a study and has an unacceptable proportion of patients with missing data. They find that there are problems in analysis. Conclusions drawn from the data may be less precise and likely biased. Even if not biased they may not have enough evaluable patients to meet statistical objectives and project goals.
How did this happen, and what could have been done to minimize this missing information? Working with a qualified statistician seasoned in identifying study issues, will help you avoid problems. They will help facilitate a solid trial design by establishing primary endpoints and appropriate adjustments to sample size.
The statistician’s insight into how data are analyzed and the impact of missing data on planned analyses, allows one to consider the factors involved and how to avoid the pitfalls, such as the financial burden of enrolling more patients to compensate for issues.
Decisions on how missing data are to be handled must be discussed in the statistical analysis phase of a study. To provide for accurate trial results, the missing data discussion should occur during the study planning stages, not after a study has veered out of control with budgetary add-ons. The statistician can plan the appropriate analyses for the most plausible missing data process that may occur.
Innovation Key to Better Clinical Trials
The word innovation derives from the Latin word innovatus, which is the noun form of innovare, meaning, "to renew or change". Changing how you think about statisticians and the clinical trial process can lead to excellence.
As a vital part of your study team, the statistician is vital when considering all aspects of study design, and not just a resource to calculate sample size. They can provide innovative ideas and introduce new questions to the clinical team to explore. Statisticians can independently evaluate how and what data are to be collected, and provide valuable suggestions for obtaining the data needed for the analysis.
Statisticians can often see the pitfalls in sub-standard data collection systems, which can hinder research projects. Most clinical trials are shifting away from paper-based systems to software tools like web and mobile. For instance, Smartphone apps are being created for the biotech and life sciences industry to better capture data and share among remote team members. More importantly, innovation is not always about reinventing the wheel but instead finding better solutions for the wheel.
In real life, no one, especially a statistician, likes to find out that data they have been asked to analyze cannot support research objectives. By nature, we like to investigate data to find answers to questions, not come back and say 'I just can't know from this data'. Sometimes we find it is impossible to analyze objectives due to the information that was collected, sometimes in how it was collected, and the worst scenario, not knowing the why it was collected in the first place.
Experience: To quote an old adage, experience does not cost, it pays. Using an experienced statistician and qualified statistical team, pays off in the end. The result: a clinical trial with the potential for success.
Innovation: Consulting with an experienced statistician about the methods of trial design and capturing data is a sound strategy, one that can ultimately, lead to a successful trial.
Engage a statistician at the beginning of the clinical trial development process and be open to innovation, and you will soon be on your way to achieving excellence in your study.
Maureen R. Lyden, M.S. founded BioStat International, Inc. in 1994 with an extensive background in biostatistical analysis in pharmaceutical, biotechnical and medical device clinical research.