Article
| Journal of Invasive Cardiology
|
January 2020
The Market Reacts Quickly: Changes in Paclitaxel Vascular Device Purchasing Within the Ascension Healthcare System
by
Peter P. Monteleone, Subhash Banerjee, Priya Kothapalli, Ariel Dora Stern, Daniel Fehder, Ron Ginor, Dominic Vollmar, Edward T. A. Fry and Mark J. Pirwitz
Background. A meta-analysis of trials in endovascular therapy suggested an increased mortality associated with treatment exposure to paclitaxel. Multiple publications and corrections of prior data were performed, and the United States Food and Drug Administration has issued multiple advisories regarding paclitaxel use. We analyzed how this controversy impacted device purchasing and related utilization patterns in the period immediately following publication of the meta-analysis.
Methods and Results. Ascension Healthcare System purchase data over a 14-month period were synthesized across centers for both paclitaxel and non-paclitaxel devices. A fixed-effects regression model and a binary regression model with facility-level controls were used to compare purchasing patterns before and after the meta-analysis. Purchase volumes of each paclitaxel device fell. Pooled purchase volumes of all paclitaxel devices decreased from a 14-month peak of 631 devices in October 2018 to a 14-month nadir of 359 devices in February 2019. An F-test comparing the pooled-month specific fixed effects for the months before vs. after the publication of the meta-analysis has an F-statistic of 11.64, suggesting that average purchasing levels in the two periods are statistically different (P<.001). Utilization of non-paclitaxel devices did not decline.
Conclusions. Purchase volumes of paclitaxel devices decreased immediately during the months following publication of the related meta-analysis. Total Ascension-wide paclitaxel device purchase volume in February 2019 demonstrated a 43.1% reduction from peak monthly purchase volume during the assessed period and a 32.5% reduction compared with November 2019, the last month preceding publication of the meta-analysis.
This Technical Note provides an introduction to Bayes’ Rule and the statistical intuition that stems from it. In this note, we review the concepts that underlie Bayesian statistics, and we offer several simple mathematical examples to illustrate applications of Bayes’ Rule. Instructors can assign this note either as a standalone reading to familiarize students with Bayesian statistics or as a supplement to accompany “Adaptive Platform Trials: The Clinical Trial of the Future?” (HBS Case No. 618-025). This case introduces the concept of an adaptive platform clinical trial, in which Bayesian statistical methods are used to expedite the investigation and regulatory approval of promising new therapies, such as cancer treatments.
Chandra, Amitabh, and Ariel Dora Stern. "Technical Note on Bayesian Statistics and Frequentist Power Calculations." Harvard Business School Technical Note 620-032, December 2019.
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Samantha F. Sanders, Mats Terwiesch, William J. Gordon and Ariel Dora Stern
The development of intelligent machines holds great promise for making health care delivery more accurate, efficient, and accessible, but challenges remain for incorporating AI into clinical and administrative settings.
This case provides an update to the (A) case, which introduces students to adaptive platform trials, an ambitious, more efficient type of clinical trial that increases access to therapies. The A case centers on Dr. Brian Alexander’s efforts to launch an adaptive platform trial for studying potential therapies for glioblastoma (GBM), an aggressive form of brain cancer. The (B) case provides an update and explores the strategies that Alexander is considering for sustainably financing this trial.
Stern, Ariel D., and Sarah Mehta. "Adaptive Platform Trials: The Clinical Trial of the Future? (B)." Harvard Business School Case 620-001, September 2019.
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