Analytical approaches to reporting long-term clinical trial data

Curr Med Res Opin. 2008 Jul;24(7):2001-8. doi: 10.1185/03007990802215315. Epub 2008 Jun 4.


Background and scope: Long-term clinical studies are essential for monitoring the effectiveness and safety of a drug. Information provided by long-term clinical studies complements the results of short-term, randomized, controlled trials, which often form the basis of regulatory approval for a new drug application. As the duration of a study increases and the number of patients continuing in the study declines, missing data become more of a problem: they may bias the results. Therefore, standard analytical strategies used in short-term randomized, controlled trials (intent-to-treat, per-protocol) may not always be appropriate for data generated in long-term studies.

Objective: To review commonly used analytical approaches in the assessment of clinical trial data and to identify and address issues related to these approaches in the analysis of long-term study data.

Findings: The authors suggest the use of an intent-to-observe population in long-term studies, applying at least three different analytical methods for handling missing data, testing for bias as a sensitivity analysis and reporting results of more than one method if they differ from one another.

Limitations: Statistical approaches to data analysis are not addressed in this review.

Conclusion: The use of multiple analyses is supported by regulatory authority and expert guidelines, although it has not been widely adopted in the medical literature. Given the inherent limitations of accounting for missing data with each method, the multiple-analysis approach provides more information with which to make better informed decisions, and clearly defined multiple analytical methods may prevent misleading conclusions from being drawn.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Data Interpretation, Statistical
  • Guidelines as Topic
  • Humans
  • Randomized Controlled Trials as Topic / statistics & numerical data*