Interpreting Clinical Trial Outcomes for Optimal Patient Care: A Survey of Clinicians and Trainees

Abstract

Background: Evaluation of the clinical importance of outcomes in research studies is an essential element of clinical decision making.

Objective: To understand how clinicians and trainees weigh the importance of different types of clinical outcomes in drug trials.

Methods: A self-administered paper survey contained 4 scenarios asking participants to rate (1, "no proof" to 10, "good proof") the extent to which 4 study outcomes provided "proof that the new drug might help people." Outcomes included (1) a surrogate outcome; (2) a surrogate-enriched composite outcome; (3) stroke mortality; and (4) all-cause mortality. The primary study metrics were mean ratings for each of the 4 outcome types, and the proportion ranking outcome importance of all-cause mortality > stroke mortality > surrogate-enriched composite or surrogate alone.

Results: A convenience sample of 549 clinicians and trainees at 2 medical centers completed the survey (response rate: 87% medical students, 80% internal medicine residents, 69% general medicine faculty, and 41% physician experts). The surrogate-enriched composite outcome and stroke mortality were rated the most important evidence for benefit (6.6 and 6.4, respectively), with all-cause mortality and a surrogate outcome being rated significantly lower (5.2 and 4.6, respectively). In addition, 48% of clinicians rated improvement in all-cause mortality as more valuable than an improvement in a surrogate marker. Only 21% rated all-cause mortality as more valuable than a surrogate-enriched composite outcome.

Conclusions: These findings raise concerns that clinicians and trainees may not interpret trial evidence in a way that promotes the best care for patients.

FIGURE

Combined Probability of Ranking 3, 4, or 5 Comparisons Correctly^a by Clinical Background and Statistical Numeracy ^a “Correctly” defined by the following pairwise rankings: stroke mortality > surrogate; all-cause mortality > surrogate; stroke mortality > composite; all-cause mortality > composite; and all-cause mortality > stroke mortality. The combined probability of answering 3, 4, or 5 pairwise comparisons “correctly” is presented (ie, “majority correct”). ^b P < .05 in the ordinal logistic regression model.