How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS

2005 ◽  
Vol 24 (15) ◽  
pp. 2401-2428 ◽  
Author(s):  
Paul C. Lambert ◽  
Alex J. Sutton ◽  
Paul R. Burton ◽  
Keith R. Abrams ◽  
David R. Jones
Keyword(s):  
Simulation Study ◽  
Prior Distributions ◽  
The Impact

scholarly journals The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App

2020 ◽  
Vol 11 ◽  
Author(s):  
Sarah Depaoli ◽  
Sonja D. Winter ◽  
Marieke Visser
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Study ◽  
Bayesian Methods ◽  
Multiple Regression Model ◽  
Specific Model ◽  
Estimation Methods ◽  
Prior Distributions ◽  
Shiny App ◽  
Novice Users ◽  
The Impact

The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation study, which illustrates the impact of priors on final model estimates. The findings from the simulation study highlight the importance of conducting a sensitivity analysis of priors. This concept is further extended through an interactive Shiny App that we developed. The Shiny App allows users to explore the impact of various forms of priors using empirical data. We introduce this Shiny App and thoroughly detail an example using a simple multiple regression model that users at all levels can understand. In this paper, we highlight how to determine the different settings for a prior sensitivity analysis, how to visually and statistically compare results obtained in the sensitivity analysis, and how to display findings and write up disparate results obtained across the sensitivity analysis. The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods. The intended audience is broad (e.g., undergraduate or graduate students, faculty, and other researchers) and can include those with limited exposure to Bayesian methods or the specific model presented here.

scholarly journals The impact of excluding or including Death Certificate Initiated (DCI) cases on estimated cancer survival: A simulation study

2021 ◽  
Vol 71 ◽  
pp. 101881
Author(s):  
Therese M.-L. Andersson ◽  
Tor Åge Myklebust ◽  
Mark J. Rutherford ◽  
Bjørn Møller ◽  
Isabelle Soerjomataram ◽  
...  
Keyword(s):  
Simulation Study ◽  
Cancer Survival ◽  
Death Certificate ◽  
The Impact

scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

Molecular Dynamics Study on the Impact of the Cutting Depth to the Titanium Nanometric Cutting

2014 ◽  
Vol 536-537 ◽  
pp. 1431-1434 ◽  
Author(s):  
Ying Zhu ◽  
Yin Cheng Zhang ◽  
Shun He Qi ◽  
Zhi Xiang
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Cutting Force ◽  
Simulation Study ◽  
Cutting Process ◽  
Work Piece ◽  
Cutting Depth ◽  
Nanometric Cutting ◽  
The Impact

Based on the molecular dynamics (MD) theory, in this article, we made a simulation study on titanium nanometric cutting process at different cutting depths, and analyzed the changes of the cutting depth to the effects on the work piece morphology, system potential energy, cutting force and work piece temperature in this titanium nanometric cutting process. The results show that with the increase of the cutting depth, system potential energy, cutting force and work piece temperature will increase correspondingly while the surface quality of machined work piece will decrease.

A simulation study on the impact of operating conditions on desulphurisation selectivity in high-sulphur gas sweetening

2013 ◽  
Vol 6 (3) ◽  
pp. 348 ◽  
Author(s):  
Kui Qiu ◽  
Lin Zhu ◽  
Miguel Bagajewicz ◽  
Sung Young Kim ◽  
Mesude Ozturk
Keyword(s):  
Simulation Study ◽  
Operating Conditions ◽  
Gas Sweetening ◽  
The Impact

scholarly journals Quantifying the Impact of Natural Immunity on Rotavirus Vaccine Efficacy Estimates: A Clinical Trial in Dhaka, Bangladesh (PROVIDE) and a Simulation Study

2018 ◽  
Vol 217 (6) ◽  
pp. 861-868 ◽  
Author(s):  
Elizabeth T Rogawski ◽  
James A Platts-Mills ◽  
E Ross Colgate ◽  
Rashidul Haque ◽  
K Zaman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Simulation Study ◽  
Vaccine Efficacy ◽  
Natural Immunity ◽  
Rotavirus Vaccine ◽  
The Impact
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