Propensity score-based adjustment for covariate effects on classification accuracy of bio-marker using ROC curve

Abstract

The potential performance of bio-marker in classifying diseased from healthy population may be affected by baseline covariates (X) that are associated with both the bio-marker (Y) and the disease status (D). Some existing approaches can be able to adjust for the effect of a single covariate at a time. However, several potential covariates can be available in practice for which simultaneous adjustment in the ROC curve is essential. This study proposed a propensity score (PS) based adjustment for the effects of several covariates in the ROC curve. The PS is first derived from a linear transformation of several covariates and the PS-adjusted (and PS-specific) ROC curve was then estimated using the existing non-parametric induced ROC regression framework. The method is illustrated for both continuous and binary bio-markers. The simulation study suggests that the PS-based adjustment performed well by providing a consistent estimate of the true ROC curve and showing robustness to the mis-specification of the propensity score model as well as to a non-linear function of covariates. Further, an application of the method is provided to evaluate the effectiveness of the body-mass-index in classifying patients with hypertension or diabetes after adjusting for the potential covariates such as age, sex, education, socio-economic status.

Keywords:

• Induced ROC regression
• non-parametric method
• body-mass-index
• classification

Acknowledgments

The authors acknowledge the authority of measures DHS for making available the data used here in a public domain. In addition, the authors acknowledge the proceeding of the ${62}^{nd}$ ISI World Statistics Congress, Kuala Lampur, Malaysia, 2019 published by the same authors, because some parts of this manuscript are completely matched with this conference proceedings.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval and consent to participate

As the dataset is freely available in a public domain and is permitted to use in research publication, the ethics approval and consent statement has been approved by the authority who made the data available for public use.

Data availability statement

The dataset used in this study can be downloaded freely from a public domain at https://dhsprogram.com/data/ under the authority of the DHS program.

Additional information

Notes on contributors

Muntaha Mushfiquee

Muntaha Mushfiquee completed BS honours and MS in Applied Statistics from the Institute of Statistical Research and Training, University of Dhaka, and did her 2nd MSc degree in Statistics at the Memorial University of Newfoundland, Canada. She is now working as a Health Data Analyst at BC PHSA, Canada.

M. Shafiqur Rahman

M. Shafiqur Rahman has a PhD in Medical Statistics, University College London and is currently working as a Professor of Applied Statistics at the Institute of Statistical Research and Training, University of Dhaka. His main research areas include casual inference, developing and validating risk prediction models, mixed effect models, focusing on development of new statistical methods for analyzing data in public health and medical research. So far he published a number of research papers in peer reviewed journals and scientific reports and guided a number of undergraduate and postgraduate students for preparing their projects and thesis. In addition to teaching and research, he is also involved with statistical consultancy services for various national and international organizations. He has been serving as an associate editor of a number of peer-reviewed journals. He is a member of the Bangladesh Statistical Association, International Biometric Society, International Statistical Institute, International Society of Clinical Biostatistics and an educational ambassador of the American Statistical Association from Bangladesh.