Sequential allocation trial design in anesthesia: an introduction to methods, modeling, and clinical applications

Abstract

Summary Estimation of the dose–response curve for new anesthetic protocols typically focuses on identifying minimum effective doses. The application of a sequential experimental method is appropriate, as it minimizes sample size requirements by updating dose assignments based on information accrued from successive subjects. One approach is the up‐and‐down method for estimating the median effective dose in a patient population (ED50). Designs better suited for achieving greater than 50% effectiveness, include the biased coin approach, and continual reassessment method. In this review we introduce different sequential design methods, provide examples of their use, and show through simulation how the method employed influences sample size and the accuracy of the estimated dose. Simulation studies are presented to illustrate the effects of dose parameter and stopping rule choice for up‐and‐down method and biased coin approach. For continual reassessment method, the effects of assumed dose–response model, prior guess, and cohort size are simulated. A binary response regression curve was fit to the data in Saidman and Eger's endtidal halothane dose‐finding study to provide a dose–response curve for generating simulations. A range of options exist when designing a study using sequential allocation with biased coin approach or continual reassessment method. Method choice influences the required sample size and confidence in estimated effect. In the halothane example, up‐and‐down method decreases the required sample size by 20–30% when the choice of design parameters is optimal. For both up‐and‐down method and biased coin approach designs, greater sample sizes, arising from adjusted stopping criteria, might be required to achieve reliable estimates. The continual reassessment method is only efficient if a limited range of doses can be chosen a priori. In conclusion the up‐and‐down method can be more efficient than nonsequential designs for the estimation of the median dose/intervention level for a given intervention (ED50). The biased coin approach or continual reassessment method are preferred for the estimation of higher or lower tail quantiles such as ED90 or ED10. Continual reassessment method may be superior if knowledge of the dose–response relationship is available for the drug of interest.

Comparison of ED95 of Butorphanol and Sufentanil for gastrointestinal endoscopy sedation: a randomized controlled trial

“…SPSS statistical software (IBM Corporation, version 19) was used for statistical analyses. The median effective dose (ED 50 ), ED 95 , and the 95% confidence intervals (CI) of butorphanol and sufentanil were determined by binary regression (probit) [16].…”

The ED95 of Nalbuphine in Outpatient‐Induced Abortion Compared to Equivalent Sufentanil

“…SPSS statistical software (IBM Corporation, version 19 Armonk, New York, USA) was used for the statistical analyses. The median effective dose (ED 50 ) and ED 95 and the 95% confidence intervals (CI) of nalbuphine were determined by binary regression (probit) .…”

Comparison of ED95 of Butorphanol with Sufentanil for Gastrointestinal Endoscopy Sedation: a randomized controlled trial

“…The standard of leaving the hospital is our outpatient operation standard [14] (including vital signs, pain, orientation, dizziness, and walking). Secondary outcomes included the demographic and medical data, including the incidence of respiratory depression (respiratory rate < 10 beats/min or SpO 2 < 90% in nasal catheter oxygenation with 3 L/min), the incidence of circulatory inhibition (MAP < 50 mmHg or HR < 45 beats /min), dosage of propofol, the incidence of failed sedation, fatigue severity scores were determined by binary regression (probit) [16].…”