Conflict of Interest
Request Clinical TrialsAssociated Trial(s):
- NCT00210886 - A Multicenter, Double-blind, Randomized Study to Compare the Efficacy and Safety of Levofloxacin 750 mg Once Daily for Five Days Versus Ciprofloxacin Twice Daily for Ten Days in the Treatment of Complicated Urinary Tract Infection and Acute Pyelonephritis.
Request Clinical Trials
Data Request StatusStatus: Withdrawn/Closed
Project Title: Application of Bayesian Adaptive Designs in Phase III Clinical Trials
Background: In the United States, high prescription drug prices are a major consumer issue. Phase 3 clinical trials, generally requiring large numbers of participants studied over a long periods of time, are the most expensive part of the drug development process. Clinical trials designed with a Bayesian framework can be more efficient and frequently result in smaller and shorter trials (i.e. less expensive) than frequentist approaches.
Objective: Determine if Bayesian adaptive re-designs applied to an already completed Phase 3 clinical trial can generate the same conclusions in a more efficient (i.e. cheap, quicker) manner?
Study Design: The Levofloxacin vs Ciprofloxacin in treatment of cUTI or Acute Pyelonephritis trial will be re-designed using Bayesian adaptive design methods and virtually re-executed using the results from the actual trial to demonstrate that the designs could be applied in practice and result in the same statistical conclusion.
Participants: Participants will be the same participants as the Levofloxacin vs Ciprofloxacin in treatment of cUTI or Acute Pyelonephritis trial.
Main Outcome Measure: The main outcome measure will be the same as the original study, microbiologic eradication.
Statistical Analysis: The Bayesian sequential design will be constructed as a two-sided non-inferiority study (same as the original study ) to demonstrate similar eradication rates for Levofloxacine and Ciprofloxacin. A Non-Inferiority margin of 15% will be used for the analysis.
Brief Project Background and Statement of Project Significance:
In the United States, high prescription drug prices are a major consumer issue. Pricing for prescription pharmaceuticals in the United States, as opposed to pricing in many other developed countries, is generally governed by market forces. Payors are given free reign to negotiate pricing for individual pharmaceuticals directly with manufacturers. There have been several attempts to address the issue through legislation and regulation, the most recent of which include features of the Build Back Better Act that caps patients? out-of-pocket costs, establishes penalties for price increases outpacing inflation and expands the Department of Health and Human Service?s negotiating powers with regard to pharmaceutical companies . A paper published by RAND Corporation in 2021 estimates that drugs in the United States cost 250% more than in 37 of the other countries in the Organization for Economic Co-operation and Development.
These high prices are a major driver of profits that offer incentives for biopharmaceutical companies to invest in Research & Development (R&D) to discover and subsequently generate evidence for the safety and efficacy of new pharmaceuticals. In the 2021 Annual Membership Survey, Pharmaceutical Research and Manufacturers of America reported that US pharmaceutical companies invested $72 billion in research and development in 2020. Wouters et al. estimated the R&D cost to bring a drug to market to be 985 million. Phase 3 clinical trials, generally requiring large numbers of participants studied over a long periods of time, are the most expensive part of the drug development process.
Implementation of Bayesian methods in clinical trials has made significant progress in the past two decades. The relevant regulatory agencies have grown comfortable with the body of literature on the validity of Bayesian statistics as compared to traditional, frequentist statistics. Acceptance of data and conclusions derived from Bayesian methods is prevalent in early-stage clinical trials. However, the application of Bayesian methods in drug development has yet to reach its full potential. Late stage clinical trials remain the most expensive and arduous phase of drug development and contribute to the high cost of medical treatment in the United States. Further work on the application of Bayesian methods in Phase III clinical studies may result in increased acceptance and adoption. The breadth of therapeutic areas provide numerous opportunities to test re-designs of completed clinical trials and add to the body of evidence that suggests Bayesian adaptive features can produce statistically robust conclusions in the clinical development of therapeutics.
Specific Aims of the Project: This project will re-design the Levofloxacin vs Ciprofloxacin in treatment of cUTI or Acute Pyelonephritis trial using Bayesian adaptive design methods (likely early stopping for statistical success or futility). The re-designed trial will be re-executed in a virtual environment and the results analyzed to determine if the statistical conclusion is the same as in the original trial.
What is the purpose of the analysis being proposed? Please select all that apply.: Research on clinical trial methods
Software Used: R
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: The source of the data for this study will be the data generated from the trial titled "A Double-Blind, Randomized Comparison of Levofloxacin 750 mg Once-Daily for Five Days With Ciprofloxacin 400/500 mg Twice-Daily for 10 Days for the Treatment of Complicated Urinary Tract Infections and Acute Pyelonephritis." The inclusion/exclusion criteria will be that of the original trial.
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: The main outcome measure will be the same as the original study, microbiologic eradication.
Main Predictor/Independent Variable and how it will be categorized/defined for your study: The main predictor variable will be treatment arm. Subjects in the original trial were randomized 1:1 to receive either levofloxacin or ciprofloxacin. The arm they were allocated to in the main predictor variable.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study:
Statistical Analysis Plan:
The Bayesian sequential designs will be constructed as a two-sided non-inferiority study (same as the original study ) to demonstrate similar eradication rates for Levofloxacine and Ciprofloxacin. A Non-Inferiority margin of 15% will be used for the analysis.
The original study enrolled 1109 participants. The Bayesian sequential designs considered will be similar to those explored in Ryan et. al (4) and will include statistical stopping for success or futility assessed at pre-determined interim analysis points (see Ryan paper for decision criteria for success/failure). Simulations will be conducted to determine average sample size, average duration, type I error, and power for multiple scenarios regarding the true treatment effects of Levofloxacine and Ciprofloxacin.
Once the feasibility of the chosen Bayesian sequential designs is confirmed, the trial will be virtually re-executed using the actual data from the trial in the original sequence of patients in recruitment order. These re-executions represent the analysis of a single realization of the trial. The difference in microbiologic eradication as concluded at interim or final analysis (as determined by the given design) will be compared to the published difference in microbiologic eradication from the original trial to determine if the primary endpoint would have been met in a design that involved fewer patients and/or had a shorter duration. The hypothesis is that one or more Bayesian sequential re-designs will demonstrate the same statistical conclusion as the original trial.
Narrative Summary: Given the well-publicized issue of drug pricing and the high cost of development, researchers and sponsors have begun exploring methods to potentially reduce expense while retaining robust standards for safety and efficacy. Bayesian statistics, which makes better use of all available information as compared to the frequentist paradigm, can provide this opportunity. In this study, I intend to virtually simulate a re-execution of a previously completed clinical trial (designed with a frequentist framework) after re-designing the trial to incorporate Bayesian features. The statistical conclusion of the re-designed trial will be compared to the statistical conclusion of the original trial.
Project Timeline: The study will commence upon receipt of the dataset and the analysis will be completed by April 2022. The manuscript will be draft and submitted for review in May 2022. The results will be reported back to the YODA Project after the review process in completed.
Dissemination Plan: The findings of the study will be summarized as a journal-style manuscript and presented to professors at the New York University School of Global Public Health. Based on feedback from that presentation, it will be decided if the manuscript will be submitted to journals for publishing. If publication is pursued, potential journals that may be explored include Trials, BMC Medical Research Methodology, Contemporary Clinical Trials Communications and Pharmaceutical Statistics.
(1) Mulcahy, Andrew W., Christopher M. Whaley, Mahlet Gizaw, Daniel Schwam, Nathaniel Edenfield, and Alejandro U. Becerra-Ornelas, International Prescription Drug Price Comparisons: Current Empirical Estimates and Comparisons with Previous Studies. Santa Monica, CA: RAND Corporation, 2021. https://www.rand.org/pubs/research_reports/RR2956.html.
(2) Pharmaceutical Research and Manufacturers of America. 2021 PhRMA Annual Membership Survey. Washington DC. 2021. https://www.phrma.org/-/media/Project/PhRMA/PhRMA-Org/PhRMA-Org/PDF/M-O/…
(3) Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA. 2020;323(9):844?853. doi:10.1001/jama.2020.1166
(4) Ryan, E.G., Bruce, J., Metcalfe, A.J. et al. Using Bayesian adaptive designs to improve phase III trials: a respiratory care example. BMC Med Res Methodol 19, 99 (2019).