Frequently Asked Questions

What information should be included in the summary of clinical study findings?

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Clinical: Biopharma
Clinical Trials
Guidelines & Standards
Drug Trials

When including a detailed summary of study findings, the following elements should be addressed to the extent they contribute to practitioners’ understanding of drug effectiveness.

1. Disposition of Subjects

It is generally recommended that the discussion of disposition of subjects include the following:

  • The number of subjects enrolled
  • The number of subjects completing the study
  • The number of subjects discontinuing the study and the reasons for discontinuation
  • For a study with a run-in period or other distinct phases, the number of subjects entering each phase and the number of subjects not progressing to the next phase

2. Treatment Effect

It is recommended that the summary of findings describe the clinical outcome of the treatment relative to the comparator (e.g. placebo or active).

  • Absolute vs. Relative Difference: When presenting differences between study group and comparator, it is important to present the absolute difference between treatment groups for the endpoint measured, as well as the relative difference (e.g. relative risk reduction or hazard ratio). For example, if mortality is 6% in one study arm and 8% in the other, the absolute difference (2%) should be presented along with the 25% relative risk reduction.
  • Group Results and Individual Subject Data: In most cases, the treatment effect is presented as a mean or median result accompanied by a measure of uncertainty or distribution of results for the treated groups. However, providing individual subject data for all treatment groups can be a useful alternative for describing the clinical effect of a drug. This can be done by including a graphical presentation of the distribution or cumulative distribution of responses among individual subjects. Individual data can also be presented as categorical outcomes (e.g. the proportion of patients reaching a prospectively defined goal, such as systolic blood pressure of 120 mmHg).
  • Combined Data: In certain situations, analyses of data combined from multiple effectiveness studies can be useful for estimating the treatment effect. These analyses should be included only when they are scientifically appropriate and better characterize the treatment effect. Meta-analytic graphs can be useful for displaying confidence intervals from several studies.
  • Uncertainty of Treatment Effect: A confidence interval and a p-value provide complementary information, and both should usually be provided when describing uncertainty of the treatment effect. A confidence interval provides a better numerical description of the uncertainty of the treatment effect and provides some information about its size. A p-value better conveys the strength of the finding (i.e. how likely it is that the observed treatment effect is a chance finding). However, it is generally better not to use a p-value alone.

3. Describing Results Within Treatment Groups

In controlled trials, the change from baseline in a treatment group is usually not by itself informative. The comparison of the change from baseline between treatment groups is critical for understanding the treatment effect. Therefore, results for both the study drug and comparator should almost always be presented because the magnitude of the treatment effect is conveyed by the comparison. Presentation of results for both study drug and comparator is especially important for studies with large effects in the placebo group, where presentation of results uncorrected for the placebo group response can be highly misleading. When results from active control arms are discussed, a comparative claim should not be implied where one is not supported. The relevant statistical comparisons are those comparing the groups, not the comparison of the treated and baseline value within a group.

For continuous data, the presentation of results within a treatment group should include, where appropriate, information about the variability of individual subject responses within the treatment group. This variability can be described with standard deviations and illustrated with box plots.

4. Demographic and Other Subgroups

The "Clinical Studies" section should include a summary statement about the results of required explorations of treatment effects in age, gender, and racial subgroups. The summary statement should report the findings of analyses that had a reasonable ability to detect subgroup differences and should note when analyses were not useful because of inadequate sample size. The following are examples of appropriate summary statements.

  • The database was not large enough to assess whether there were differences in effects in age, gender, or race subgroups.
  • Examination of age and gender subgroups did not identify differences in response to (study drug) among these subgroups. There were too few African-American subjects to adequately assess differences in effects in that population.
  • Examination of age and gender subgroups suggested a larger treatment effect in women (possibly resulting from the larger mg/kg dose in women), but no age-related differences. There were too few African-American subjects to adequately assess differences in effects in that population.

Compelling results from analyses of other subgroups of established interest should also be presented, with a caution statement, where appropriate, about the inherent risks of unplanned subgroup analyses.