Utilizing statistics for optimizing your clinical trial

Fancy statistical methods cannot rescue poor design. Many issues must be considered when designing clinical trials, the major is which study endpoints should be Selected. It is a responsibility of those who design and conduct trials to choose endpoints which will convince decision-makers such as clinicians and policymakers.

Generally, a single end point is limited to capturing the effect of an intervention, so multiple endpoints are usually selected and categorized as primary and secondary endpoints.

A wide variety of endpoints are used in clinical trials:

  • Continuous measurements: blood pressures, weight
  • Event times: time to… recurrence, survival
  • Counts: frequency of occurrence
  • Binary endpoints:  yes or no
  • Ordered categories:  mild moderate or severe pain, NYHA status
  • Unordered categories: categories of adverse experiences: GI, cardiac, etc.

In cases where there is more control over endpoint selection, the following considerations are important and have been demonstrated to reduce sample size by 10% to 50% and greatly increase the power for statistical testing:

  • Avoid binary endpoints such as response/non-response in favor of continuous endpoints such as size, mmHg, weight, etc.
  • Prefer continuous endpoints, ranking endpoints such as NYHA functional classification.
  • When obtaining patient reported outcome (PRO), select the validated scale with the greatest reliability (repeatability).
  • To the degree possible, analyze your efficacy endpoint using procedures that are sensitive to missing data, such as mixed model repeated measures (MMRM).

Additional methods for increasing your chances for a successful trial, with fewer resources include:

Adaptive design

Minimizing variance

Specifying matched subject designs

Designing single arm trial for regulatory approval

Non-inferiority hypothesis

Reducing “noise” by both appropriate measurements and statistical analysis

All the methods described above, used in combination or alone, will increase your trial’s efficiency while reducing costs and increasing the probability of success.

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