Adjust for misclassification of an exposure variable in pooled data such as an individual participant data meta-analysis
Adjust for misclassification of an exposure variable in pooled data, such as in an individual participant data meta-analysis (IPDMA). Using Bayesian misclassification models, the potential misclassification of an exposure is accounted for, and the uncertainty is propagated to the variance and CI. Modeling of an single data set (without clustering) is also possible.
The misclassification model is fitted using misclass(). This package is intended to make using a misclassification model easier. Therefore, it is not entirely comprehensive, but it is designed to be flexible. The functions make.inits(), make.model() and make.monitors() can be used separately to tweak the model, but are otherwise called by misclass().
When using this package, please cite this page directly, or cite the paper for which this code was developed:
- de Jong VMT, Campbell H, Maxwell L, Jaenisch T, Gustafson P, Debray TPA. Adjusting for misclassification of an exposure in an individual participant data meta-analysis. arXiv:211101650 [stat]. 2021 Nov 2; Available from: https://doi.org/10.48550/arXiv.2111.01650
A pdf version of the manual is available on https://github.com/VMTdeJong/misclass-manual/blob/main/misclass-manual.pdf
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under ReCoDID grant agreement No 825746.
The views expressed in this paper are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the regulatory agency/agencies or organizations with which the authors are employed/affiliated.