A C++ program to calculate sample sizes for cost-effectiveness trials in a Bayesian framework.

Comput Methods Programs Biomed

Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary, University of London, UK.

Published: June 2013

Cost-Effectiveness Analysis (CEA) has become an increasingly important component of clinical trials. However, formal sample size calculations for such studies are not common. One of the reasons for this might be due to the absence of readily available computer software to perform complex calculations, particularly in a Bayesian setting. In this paper, a C++ program (using NAG library functions/subroutines) is presented to estimate the sample sizes for cost-effectiveness clinical trials in a Bayesian framework. The program can equally be used to calculate sample sizes for efficacy trials. The Bayesian approach to sample size calculation is based on that of O'Hagan and Stevens (A. O'Hagan, J.W. Stevens, Bayesian assessment of sample size for clinical trials of cost-effectiveness, Medical Decision Making 21 (2001) 219-230). With this program, the user can calculate sample sizes for various thresholds of willingness to pay and under various assumptions of the correlations between cost and effects. Under some prior, the program produces frequentist sample size as well. The program runs under windows environment and running time is very short.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cmpb.2013.01.008DOI Listing

Publication Analysis

Top Keywords

sample sizes
16
sample size
16
calculate sample
12
trials bayesian
12
clinical trials
12
c++ program
8
sample
8
sizes cost-effectiveness
8
bayesian framework
8
o'hagan stevens
8

Similar Publications

A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997-1998 to 2021-2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention.

View Article and Find Full Text PDF

Bats are recognized as natural reservoirs for an array of diverse viruses, particularly coronaviruses, which have been linked to major human diseases like SARS-CoV and MERS-CoV. These viruses are believed to have originated in bats, highlighting their role in virus ecology and evolution. Our study focuses on the molecular characterization of bat-derived coronaviruses (CoVs) in Canada.

View Article and Find Full Text PDF

SARS-CoV-2 infection induces a humoral immune response, producing virus-specific antibodies such as IgM, IgG, and IgA. IgA antibodies are present at mucosal sites, protecting against respiratory and other mucosal infections, including SARS-CoV-2, by neutralizing viruses or impeding attachment to epithelial cells. Since SARS-CoV-2 spreads through the nasopharynx, the specific IgAs of SARS-CoV-2 are produced quickly after infection, effectively contributing to virus neutralization.

View Article and Find Full Text PDF

Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; training and inference processes consume significant computational resources. Thus, developing a lightweight and suitable fault diagnosis algorithm for small samples is particularly crucial.

View Article and Find Full Text PDF

Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review.

Sensors (Basel)

December 2024

REMIT (Research on Economics, Management and Information Technologies), IJP (Instituto Jurídico Portucalense), Universidade Portucalense, Rua Dr. António Bernardino de Almeida, 541-619, 4200-072 Porto, Portugal.

Some previous studies have focused on using physiological signals to detect stress in individuals with ASD through wearable devices, yet few have focused on how to design such solutions. Wearable technology may be a valuable tool to aid parents and caregivers in monitoring the emotional states of individuals with ASD who are at high risk of experiencing very stressful situations. However, effective wearable devices for individuals with ASD may need to differ from solutions for those without ASD.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!