Erica E. M. Moodie’s research while affiliated with McGill University Health Centre and other places

Dynamic treatment regimes describe a class of treatments or interventions, often given sequentially, that are tailored to the clinical, demographic, and related characteristics of an individual patient at the time the treatment is selected. In this article, we introduce dynamic treatment rules and point to important data sources for estimating outcomes under alternative treatment strategies. We also briefly discuss estimation strategies and recent innovations in the field.

Identification of optimal dose combinations in early-phase dose-finding trials is challenging, due to the trade-off between precisely estimating the many parameters required to flexibly model the possibly nonmonotonic dose-response surface, and the small sample sizes in early-phase trials. This difficulty is even more pertinent in the context of personalized dose-finding, where patient characteristics are used to identify tailored optimal dose combinations. To overcome these challenges, we propose the use of Bayesian optimization for finding optimal dose combinations in standard (one size fits all) and personalized multi-agent dose-finding trials. Bayesian optimization is a method for estimating the global optima of expensive-to-evaluate objective functions. The objective function is approximated by a surrogate model, commonly a Gaussian process, paired with a sequential design strategy to select the next point via an acquisition function. This work is motivated by an industry-sponsored problem, where the focus is on optimizing a dual-agent therapy in a setting featuring minimal toxicity. To compare the performance of the standard and personalized methods under this setting, simulation studies are performed for a variety of scenarios. Our study concludes that taking a personalized approach is highly beneficial in the presence of heterogeneity.

Traditional Randomized Controlled Trials often fall short in addressing the specific needs of clinical practice due to their one-size-fits-all treatment approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) offer a dynamic and adaptive approach, allowing for multiple randomizations based on patient responses and evolving conditions. SMARTs enable personalized treatment pathways, such as in the trial for antiretroviral therapy (ART) in South Africa, which adjusts treatment based on patient outcomes. Despite these advantages, the use of SMARTs in infectious diseases remains limited. Greater adoption of SMARTs could promote more personalized treatment approaches, improve flexibility in response to public health needs, and enhance the effectiveness of interventions. However, challenges such as recruitment and increased expertise needed for more complex analyses must be addressed. Additionally, combining SMARTs with other adaptive designs could further improve the relevance and outcomes of clinical research.

Typical simulation approaches for evaluating the performance of statistical methods on populations embedded in social networks may fail to capture important features of real-world networks. It can therefore be unclear whether inference methods for causal effects due to interference that have been shown to perform well in such synthetic networks are applicable to social networks which arise in the real world. Plasmode simulation studies use a real dataset created from natural processes, but with part of the data-generation mechanism known. However, given the sensitivity of relational data, many network data are protected from unauthorized access or disclosure. In such case, plasmode simulations cannot use released versions of real datasets which often omit the network links, and instead can only rely on parameters estimated from them. A statistical framework for creating replicated simulation datasets from private social network data is developed and validated. The approach consists of simulating from a parametric exponential family random graph model fitted to the network data and resampling from the observed exposure and covariate distributions to preserve the associations among these variables.

Estimating the COVID‐19 infection fatality rate, inferring the latent incidence and predicting the future epidemic evolution are critical to public health surveillance, but often challenging due to limited data availability or quality. Recently, a Bayesian framework combining time series deconvolution of deaths with a parametric Susceptible–Infectious–Recovered (SIR) model was proposed by Irons and Raftery, 2021. We assess the parameter identifiability of the model using the profile likelihood approach and simulations, when only the time series of deaths and seroprevalence survey data are available. The robustness of the model to the more complex but also more realistic Susceptible–Exposed–Infectious–Recovered (SEIR)‐based epidemics is evaluated through simulations; the influence of potential biases in the serosurveys on the inference is also investigated. We use a stationary first‐order autoregressive prior to account for the variability of transmission rate over time. The results suggest that the model is relatively robust to SEIR‐based epidemics, especially when the reproductive number is low, given sufficient information from serosurveys or priors. However, the lack of parameter identifiability under limited data availability cannot be neglected. We apply the model to infer the COVID‐19 infections in Ontario and Quebec, Canada during the Omicron era.

Individualized treatment rules tailor treatments to patients based on clinical, demographic, and other characteristics. Estimation of individualized treatment rules requires the identification of individuals who benefit most from the particular treatments and thus the detection of variability in treatment effects. To develop an effective individualized treatment rule, data from multisite studies may be required due to the low power provided by smaller datasets for detecting the often small treatment-covariate interactions. However, sharing of individual-level data is sometimes constrained. Furthermore, sparsity may arise in two senses: different data sites may recruit from different populations, making it infeasible to estimate identical models or all parameters of interest at all sites, and the number of non-zero parameters in the model for the treatment rule may be small. To address these issues, we adopt a two-stage Bayesian meta-analysis approach to estimate individualized treatment rules which optimize expected patient outcomes using multisite data without disclosing individual-level data beyond the sites. Simulation results demonstrate that our approach can provide consistent estimates of the parameters which fully characterize the optimal individualized treatment rule. We estimate the optimal Warfarin dose strategy using data from the International Warfarin Pharmacogenetics Consortium, where data sparsity and small treatment-covariate interaction effects pose additional statistical challenges.

This article has been retracted and replaced. Please see: https://doi.org/10.1093/infdis/jiae358