Contemporary Clinical Trials Communications (Dec 2016)

Design and conduct of a provider survey to determine a clinically persuasive effect size in planning VA Cooperative Study #590 (Li+)

  • Kelly M. Harrington,
  • Matthew H. Liang,
  • Keri Hannagan,
  • Soe Soe Thwin,
  • Ryan E. Ferguson,
  • Natalie Morgenstern,
  • Erick Flores,
  • Ira R. Katz

DOI
https://doi.org/10.1016/j.conctc.2016.08.004
Journal volume & issue
Vol. 4, no. C
pp. 149 – 154

Abstract

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Background: The estimation of an effect size is an important step in designing an adequately powered, feasible clinical trial intended to change clinical practice. During the planning phase of VA Cooperative Study #590, “Double-Blind Placebo-Controlled Study of Lithium for Preventing Repeated Suicidal Self-Directed Violence in Patients with Depression or Bipolar Disorder (Li+),” it was not clear what effect size would be considered large enough to influence prescribing behavior among practicing clinicians. Methods: We conducted an online survey of VA psychiatrists to assess their interest in the study question, their clinical experience with lithium, and their opinion about what suicide reduction rate would change their prescribing habits. The 9-item survey was hosted on SurveyMonkey© and VA psychiatrists were individually emailed an invitation to complete an anonymous online survey. Three email waves were sent over three weeks. Results: Overall, 862 of 2713 VA psychiatrists (response rate = 31.8%) responded to the anonymous survey. 74% of the respondents would refer a patient to the proposed trial, 9% would not, and 17% were unsure. Presented with suicide reduction rates in 10% increments ranging from 10 to 100%, 61% of respondents indicated that they would use lithium if suicide attempts were reduced by at least 40%; 83% would use lithium if it reduced attempts by at least 50%. Conclusions: Even with the limitations of response bias and the reliability of responses on future prescribing behavior, a survey of potential users of a clinical trial's results offers a convenient, empirical method for determining and justifying clinically relevant effect sizes.

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