Statistical methods for assessing drug interactions using observational data

With advances in medicine, many drugs and treatments become available. On the one hand, polydrug use (i.e. using more than one drug at a time) has been used to treat patients with multiple morbid conditions, and polydrug use may cause severe side effects. On the other hand, combination treatments have been successfully developed to treat severe diseases such as cancer and chronic diseases. Observational data, such as electronic health record data, may provide useful information for assessing drug interactions. In this article, we propose using marginal structural models to assess the average treatment effect and causal interaction of two drugs by controlling confounding variables. The causal effect and the interaction of two drugs are assessed using the weighted likelihood approach, with weights being the inverse probability of the treatment assigned. Simulation studies were conducted to examine the performance of the proposed method, which showed that the proposed method was able to estimate the causal parameters consistently. Case studies were conducted to examine the joint effect of metformin and glyburide use on reducing the hospital readmission for type 2 diabetic patients, and to examine the joint effect of antecedent statins and opioids use on the immune and inflammatory biomarkers for COVID-19 hospitalized patients.</p

Data for Agent-Based Modeling of the interaction between CD8+ T cells and Beta cells in Type 1 Diabetes

This dataset contains data for the figures given in the following publication:<div><br></div><div><blockquote><div><div>Agent-Based Modeling of the interaction between CD8+ T cells and Beta cells in Type 1 Diabetes<br></div><div><br></div><div>M. C. Ozturk, Q. Xu, and A. Cinar</div><div><br></div><div>PLoS ONE (2017) </div><div><br></div><div>DOI: http://dx.doi.org/10.1371/journal.pone.0190349</div></div></blockquote></div

Model parameters, units and the ranges considered in the simulations.

<p>Model parameters, units and the ranges considered in the simulations.</p

An example simulation output for a scenario where the basement membrane strength was set to 20160, Beta cell proliferation was 5% per day, islet density was medium and the initial T cell count was 3 with a 2:1 effector:naive T cell ratio.

<p>Labeled arrows show (A) t = 5 days (B) t = 8 days (C) t = 18 days (D) t = 26 days. Insets show the first 7 days. Note that t = 0 days corresponds to 4 weeks of age of the mouse.</p

Comparison of the three basement membrane strength scenarios in the presence or absence of Beta cell regeneration.

<p>In all cases, the islet density was medium and the initial T cell count was 3 with a 2:1 effector:naive T cell ratio.</p

Simulation results for the scenario with a basement membrane strength of 20160. Beta cell regeneration was not allowed, islet density was medium and the initial T cell count was 3 with a 2:1 effector:naive T cell ratio. Note that t = 0 days corresponds to 4 weeks of age of the mouse.

<p>Simulation results for the scenario with a basement membrane strength of 20160. Beta cell regeneration was not allowed, islet density was medium and the initial T cell count was 3 with a 2:1 effector:naive T cell ratio. Note that t = 0 days corresponds to 4 weeks of age of the mouse.</p

State of the simulation at the labeled arrows shown in Fig 2.

<p>Each tick on the x and y axes corresponds to 10 <i>μ</i>m. The basement membrane strength was set to 20160, Beta cell proliferation was 5% per day, islet density was medium and the initial T cell count was 3 with a 2:1 effector:naive T cell ratio. (A) t = 5 days, (B) t = 8 days (C) t = 18 days (D) t = 26 days. Note that t = 0 days corresponds to 4 weeks of age of the mouse. Color bar shows the intactness of the basement membrane. Beta cells are shown as green circles within the islets, while T cells can be seen as diamonds (◊, naive T cells), squares (□, effector T cells), and asterisks (*, memory T cells) outside the islets.</p

Simulation results for the scenario with a basement membrane strength of 20160. Beta cell proliferation was 5% per day, islet density was medium and the initial T cell count was 9 with a 2:1 effector:naive T cell ratio. Note that t = 0 days corresponds to 4 weeks of age of the mouse.

<p>Simulation results for the scenario with a basement membrane strength of 20160. Beta cell proliferation was 5% per day, islet density was medium and the initial T cell count was 9 with a 2:1 effector:naive T cell ratio. Note that t = 0 days corresponds to 4 weeks of age of the mouse.</p

T cell proliferation process in the model.

<p>Here, p denotes percent probability. Rules were adapted from Kinjyo et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190349#pone.0190349.ref048" target="_blank">48</a>].</p

Comparison of the different initial T cell count scenarios. In these simulations, the basement membrane strength was set to 20160, Beta cell regeneration was not allowed, and the islet density was medium.

<p>Comparison of the different initial T cell count scenarios. In these simulations, the basement membrane strength was set to 20160, Beta cell regeneration was not allowed, and the islet density was medium.</p