arXiv subjects

Paramita Saha-Chaudhuri

Publications and source records attributed to Paramita Saha-Chaudhuri.

3 recordsLinked to original sources

arXiv2021

Model-based ROC (mROC) curve: examining the effect of case-mix and model calibration on the ROC plot

The performance of risk prediction models is often characterized in terms of discrimination and calibration. The Receiver Operating Characteristic (ROC) curve is widely used for evaluating model discrimination. When evaluating the performance of a risk prediction model in a new sample, the shape of the ROC curve is affected by both case-mix and the postulated model. Further, compared to discrimination, evaluating calibration has not received the same level of attention. Commonly used methods for model calibration involve subjective specification of smoothing or grouping. Leveraging the familiar ROC framework, we introduce the model-based ROC (mROC) curve to assess the calibration of a pre-specified model in a new sample. mROC curve is the ROC curve that should be observed if a pre-specified model is calibrated in the sample. We show the empirical ROC and mROC curves for a sample converge asymptotically if the model is calibrated in that sample. As a consequence, the mROC curve can be used to assess visually the effect of case-mix and model mis-calibration. Further, we propose a novel statistical test for calibration that does not require any smoothing or grouping. Simulations support the adequacy of the test. A case study puts these developments in a practical context. We conclude that mROC can easily be constructed and used to evaluate the effect of case-mix and model calibration on the ROC plot, thus adding to the utility of ROC curve analysis in the evaluation of risk prediction models. R code for the proposed methodology is provided (https://github.com/msadatsafavi/mROC/).

stat.ME↗

arXiv2016

Data Privacy and Specimen Pooling: Using an old tool for New Challenges

Background: In the context of ongoing debate over data confidentiality versus shared use of research data, as raised following the new EU General Data Protection Regulation, we seek to find alternate techniques that can balance these two issues. In particular, we demonstrate that an existing epidemiologic tool, specimen pooling, can be adapted as a privacy-preserving method to enable data analysis while maintaining data confidentiality. Specimen pooling is a cost-effective tool in studying the effect of an expensive-to-measure exposure on a disease outcome, for both unmatched and matched case-control designs. We propose the technique in a new context to analyze confidential data and demonstrate that it can be successfully used to estimate OR of covariates based on aggregate data when individual patient data cannot be shared. Methods: We demonstrate the application of specimen pooling based on aggregate covariate level and show that aggregated covariate levels can be used in a conditional logistic regression model to estimate individual-level odds ratio parameters of interest. We then show how to adapt the technique as a privacy-preserving method to analyze data from a matched case-control design. A similar approach can be applied for an unmatched design and unconditional logistic regression. Results: The parameter estimates from the standard conditional logistic regression are compared to those based on aggregated data. The parameter estimates are similar and have similar standard errors and confidence interval coverage. Conclusions: Pooling can be used effectively to analyze confidential data arising from distributed data networks and will be extremely useful in pharmacoepidemiology.

stat.AP↗

arXiv2016

Covariate Microaggregation for Logistic Regression: An Application for Analysis of Confidential Data

In the recent past, electronic health records and distributed data networks emerged as a viable resource for medical and scientific research. As the use of confidential patient information from such sources become more common, maintaining privacy of patients is of utmost importance. For a binary disease outcome of interest, we show that the techniques of microaggregation (equivalent to specimen pooling) and \underline{Po}oled \underline{Lo}gistic \underline{R}egression (PoLoR) could be applied for analysis of large and/or distributed data while respecting patient privacy. PoLoR is exactly the same as standard logistic regression, but instead of using individual covariate level, the analysis uses microaggregated covariate level when microaggregation is conditional on the outcome status. Aggregate levels of covariates can be passed from the nodes of the network to the analysis center without revealing individual-level microdata and can be used very easily with standard softwares for estimation of disease odds ratio associated with a set of categorical or continuous covariates. Microaggregation of covariates allows for consistent estimation of the parameters of logistic regression model that can include confounders and transformation of exposure. Additionally, since the microdata can be accessed within nodes, effect modifiers can be accommodated and consistently estimated. For analysis of confidential health data, covariate microaggregation for logistic regression will provide a practical and straightforward alternative to more complicated existing options.

stat.AP↗