Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials

Personalized adaptive interventions offer the opportunity to increase patient benefits, however, there are challenges in their planning and implementation. Once implemented, it is an important question whether personalized adaptive interventions are indeed clinically more effective compared to a fixed gold standard intervention. In this paper, we present an innovative N-of-1 trial study design testing whether implementing a personalized intervention by an online reinforcement learning agent is feasible and effective. Throughout, we use a new study on physical exercise recommendations to reduce pain in endometriosis for illustration. We describe the design of a contextual bandit recommendation agent and evaluate the agent in simulation studies. The results show that adaptive interventions add complexity to the design and implementation process, but have the potential to improve patients' benefits even if only few observations are available. In order to quantify the expected benefit, data from previous interventional studies is required. We expect our approach to be transferable to other interventions and clinical interventions

Multimodal Outcomes in N-of-1 Trials: Combining Unsupervised Learning and Statistical Inference

N-of-1 trials are randomized multi-crossover trials in single participants with the purpose of investigating the possible effects of one or more treatments. Research in the field of N-of-1 trials has primarily focused on scalar outcomes. However, with the increasing use of digital technologies, we propose to adapt this design to multimodal outcomes, such as audio, video, or image data or also sensor measurements, that can easily be collected by the trial participants on their personal mobile devices. We present here a fully automated approach for analyzing multimodal N-of-1 trials by combining unsupervised deep learning models with statistical inference. First, we train an autoencoder on all images across all patients to create a lower-dimensional embedding. In the second step, the embeddings are reduced to a single dimension by projecting on the first principal component, again using all images. Finally, we test on an individual level whether treatment and non-treatment periods differ with respect to the component. We apply our proposed approach to a published series of multimodal N-of-1 trials of 5 participants who tested the effect of creams on acne captured through images over 16 days. We compare several parametric and non-parametric statistical tests, and we also compare the results to an expert analysis that rates the pictures directly with respect to their acne severity and applies a t-test on these scores. The results indicate a treatment effect for one individual in the expert analysis. This effect was replicated with the proposed unsupervised pipeline. In summary, our proposed approach enables the use of novel data types in N-of-1 trials while avoiding the need for manual labels. We anticipate that this can be the basis for further explorations of valid and interpretable approaches and their application in clinical multimodal N-of-1 trials.Comment: 11 pages, 4 figure

Genetic Association Besides Classical HLA Class II Genes

Type 1 diabetes is an autoimmune disease with rising incidence in high-income countries. Genetic and environmental predisposing factors contribute to the etiology of the disease, although their interaction is not sufficiently understood to allow for preventive action. Strongest known associations with genetic variation map to classical HLA class II genes. Because of its genetic complexity, the HLA region has been under-represented in genome-wide association studies, having potentially hindered the identification of relevant associations underlying the etiology of the disease. Here, we performed a comprehensive HLA-wide genetic association analysis of type 1 diabetes including multi-allelic and rare variants. We used high-density whole-exome sequencing data of the HLA region in the large UK Biobank dataset to apply gene-based association tests with a carefully defined type 1 diabetes phenotype (97 cases and 48,700 controls). Exon-based and single-variant association tests were used to complement the analysis. We replicated the known association of type 1 diabetes with the classical HLA-DQ gene. Tailoring the analysis toward rare variants, we additionally identified the lysine methyl transferase EHMT2 as associated. Deeper insight into genetic variation associated with disease as presented and discussed in detail here can help unraveling mechanistic details of the etiology of type 1 diabetes. More specifically, we hypothesize that genetic variation in EHMT2 could impact autoimmunity in type 1 diabetes development

Anytime-valid inference in N-of-1 trials

App-based N-of-1 trials offer a scalable experimental design for assessing the effects of health interventions at an individual level. Their practical success depends on the strong motivation of participants, which, in turn, translates into high adherence and reduced loss to follow-up. One way to maintain participant engagement is by sharing their interim results. Continuously testing hypotheses during a trial, known as "peeking", can also lead to shorter, lower-risk trials by detecting strong effects early. Nevertheless, traditionally, results are only presented upon the trial's conclusion. In this work, we introduce a potential outcomes framework that permits interim peeking of the results and enables statistically valid inferences to be drawn at any point during N-of-1 trials. Our work builds on the growing literature on valid confidence sequences, which enables anytime-valid inference with uniform type-1 error guarantees over time. We propose several causal estimands for treatment effects applicable in an N-of-1 trial and demonstrate, through empirical evaluation, that the proposed approach results in valid confidence sequences over time. We anticipate that incorporating anytime-valid inference into clinical trials can significantly enhance trial participation and empower participants

Analyzing Population-Level Trials as N-of-1 Trials: an Application to Gait

Studying individual causal effects of health interventions is of interest whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. In this study, we propose to re-analyze existing population-level studies as N-of-1 trials as an alternative, and we use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. We first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian linear mixed models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses showed nuanced differences between participants. Baseline values of the gait parameters varied largely among all participants, and the changes induced by fatigue and cognitive task performance were also highly heterogeneous, with some individuals showing effects in opposite direction. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.Comment: 25 pages, 11 figures, including supplementary material

Directed acyclic graphs and causal thinking in clinical risk prediction modeling

Background: In epidemiology, causal inference and prediction modeling methodologies have been historically distinct. Directed Acyclic Graphs (DAGs) are used to model a priori causal assumptions and inform variable selection strategies for causal questions. Although tools originally designed for prediction are finding applications in causal inference, the counterpart has remained largely unexplored. The aim of this theoretical and simulation-based study is to assess the potential benefit of using DAGs in clinical risk prediction modeling. Methods: We explore how incorporating knowledge about the underlying causal structure can provide insights about the transportability of diagnostic clinical risk prediction models to different settings. We further probe whether causal knowledge can be used to improve predictor selection in clinical risk prediction models. Results: A single-predictor model in the causal direction is likely to have better transportability than one in the anticausal direction in some scenarios. We empirically show that the Markov Blanket, the set of variables including the parents, children, and parents of the children of the outcome node in a DAG, is the optimal set of predictors for that outcome. Conclusions: Our findings provide a theoretical basis for the intuition that a diagnostic clinical risk prediction model including causes as predictors is likely to be more transportable. Furthermore, using DAGs to identify Markov Blanket variables may be a useful, efficient strategy to select predictors in clinical risk prediction models if strong knowledge of the underlying causal structure exists or can be learned

Multimodal N-of-1 trials: A Novel Personalized Healthcare Design

N-of-1 trials aim to estimate treatment effects on the individual level and can be applied to personalize a wide range of physical and digital interventions in mHealth. In this study, we propose and apply a framework for multimodal N-of-1 trials in order to allow the inclusion of health outcomes assessed through images, audio or videos. We illustrate the framework in a series of N-of-1 trials that investigate the effect of acne creams on acne severity assessed through pictures. For the analysis, we compare an expert-based manual labelling approach with different deep learning-based pipelines where in a first step, we train and fine-tune convolutional neural networks (CNN) on the images. Then, we use a linear mixed model on the scores obtained in the first step in order to test the effectiveness of the treatment. The results show that the CNN-based test on the images provides a similar conclusion as tests based on manual expert ratings of the images, and identifies a treatment effect in one individual. This illustrates that multimodal N-of-1 trials can provide a powerful way to identify individual treatment effects and can enable large-scale studies of a large variety of health outcomes that can be actively and passively assessed using technological advances in order to personalized health interventions

The Wooster Voice (Wooster, OH), 1946-10-04

The football team has sustained an abundance of injuries over the past week. Both staff and students are having difficulties with not have enough housing spaces, nor enough classrooms. The first year senate chair will be Don Shawver. Students have voted in favor for having a homecoming queen by 757 for and 128 against. On the thirteenth of October, the Inter-Club Council will be hosting a tea for any freshman girl who is interested in joining a section. A feature is written about how a Latin American student views the College of Wooster. Emory Anderson writes about how squirrels are slowly dominating the world.https://openworks.wooster.edu/voice1941-1950/1128/thumbnail.jp