Laura F White

Laura F White

she/her

Professor of Biostatistics; infectious diseases; substance use disorder research

TB projects

We work in many TB related research projects. This describes some of our modeling and methods work in this area. More information on our work using whole genome sequencing to understand transmission can be found on this page.

TB-STATIS: Measures of tuberculosis disease severity

Tuberculosis is the leading cause of death due to infectious disease globally and antibiotic treatment regimens can take months to years to successfully treat the disease. There is growing interest in treatment shortening regimens for individuals with less severe disease. With that in mind, we developed a rigorous statistical approach to estimate TB disease severity using available data at diagnosis and event-based modeling (popular in the study of cognitive decline). Our software is available here and the paper is under review.

Inferential methods with data from Respondent Driven Sampling (RDS)

RDS is a popular method for sampling hard to reach populations. We have implemented this approach in South Africa in a study to understand TB and HIV among people who smoke illicit drugs (PWSD).

While there are several methods to estimate prevalence from RDS surveys, there are limited methods to perform statistical tests. We have developed a test to do basic two group comparisons and the R package to run this method can be found here. This work has been published in The Journal of the Royal Statistical Society, Series A.

imputeTBculture: methods to handle missing data in serially collected culture samples

In clinical trials and cohort studies of TB, it is common to collect culture samples at multiple time points during follow-up. Missing data is common in these studies and there was no well-established approach to handle this missing data. We have published a paper in BMC Medical Research Methods describing best practices for this problem and created code for practitioners to use.

Backcalculation methods for estimating TB incidence

We have developed a Bayesian implementation of backcalculation methods for estimating TB incidence, using information obtained from TB prevalence surveys. The code to implement this method is found here. This work is under review.

Contribution of reinfection to the annual rate of infection (ARI)

We developed a model to determine the impact of reinfection on measured annual rates of infection (ARI) and found that when there is heterogeneity in infection risk, which is recognized to be most realistic, measured ARI is underestimated. The code for our model is found here and the paper which has been published in Clinical Infectious diseases, can be found here.

Data driven targets for reducing TB burden

We developed a model that uses data from countries with two prevalence surveys to determine the amount of prevalent TB disease that needs to be treated in order to meaningfully decrease TB disease burden. The method also estimates the duration of infectiousness for individuals who eventually receive treatment. The code for this model can be found here. This paper has recently been accepted to the International Journal of Tuberculosis and Lung Disease.

Cost-Effectiveness of a Nutritional Intervention for TB in India

We have developed a model to test the cost effectiveness of mnutritional supplementation for individuals wiht TB in India. The model explores a range of parameter inputs stochastically and determines the cost and benefits of this type of intervention. Full code fo the model can be found here.

References