curtail: Design of One- and Two-Stage Clinical Trials with Curtailed...



The curtail package is used for the planning of one- and two- stage clinical trials with curtailed sampling. Under curtailed sampling, an early decision in the trial is allowed as soon as any predefined statistical endpoint is reached. The package provides functions to help select a design, including visualizations to compare criteria for different choices of parameters, and functions to calculate power, significance, and expected sample size, among others.

In the one-stage design, patients are assumed to be enrolled into the trial sequentially up to a maximum number of patients. A critical value of observed patient successes needed to deem the therapy superior is set prior to the start of the study. The power_significance_plot and power_significance_ROC functions provide visualizations to compare power and significance levels for various choices of critical values. Also, the critical_values function will calculate the critical value to maintain a desired significance level. Under curtailed sampling, the study ends as soon as enough the observed patient successes meets the critical value or as soon as too many patient failures have been observed. The smallest number of patient enrollees needed to reach a decision under curtailed sampling is described by the Stopped Negative Binomial distribution. This package also provides density and other related functions for the Stopped Negative Binomial Distribution.

The two-stage design presented in this package is a modification of Simon's two-stage design with separate, but nested, criteria for early stopping in Stage 1 and efficacy in Stage 2. The two-stage design has two critical values which can both be determined with the critical_values function to maintain a desired significance level and probability of early stopping. These critical values are set prior to the start of the study to determine the number of patient successes in the first stage needed to continue the trial to the second stage and the critical number of efficacy successes throughout the trial needed to deem the therapy superior. Under curtailed sampling, early decisions can be made in Stage 1 and Stage 2. The best_designs function finds the optimal and minimax design for a fixed total sample size. Other functions are provided to calculate the expected sample size, power, significance, and probability of early stopping in the trial given a choice of design parameters.

One-Stage Design Function Calls

#' Stopped Negative Binomial Distribution Function Calls

Two-Stage Design Function Calls

Acknowledgements: This work was partially supported through a Patient-Centered Outcomes Research Institute (PCORI) Award (ME-1511-32832).

Disclaimer: All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors or Methodology Committee.

kaneplusplus/curtail documentation built on May 24, 2019, 2:04 a.m.