49 research outputs found
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
Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students:Protocol for a Microrandomized Trial
Background: Many university students experience mental health problems such as anxiety and depression. To support their mental health, a transdiagnostic mobile app intervention has been developed. The intervention provides short exercises rooted in various approaches (eg, positive psychology, mindfulness, self-compassion, and acceptance and commitment therapy) that aim to facilitate adaptive emotion regulation (ER) to help students cope with the various stressors they encounter during their time at university. Objective: The goals of this study are to investigate whether the intervention and its components function as intended and how participants engage with them. In addition, this study aims to monitor changes in distress symptoms and ER skills and identify relevant contextual factors that may moderate the intervention’s impact. Methods: A sequential explanatory mixed methods design combining a microrandomized trial and semistructured interviews will be used. During the microrandomized trial, students (N=200) will be prompted via the mobile app twice a day for 3 weeks to evaluate their emotional states and complete a randomly assigned intervention (ie, an exercise supporting ER) or a control intervention (ie, a health information snippet). A subsample of participants (21/200, 10.5%) will participate in interviews exploring their user experience with the app and the completed exercises. The primary outcomes will be changes in emotional states and engagement with the intervention (ie, objective and subjective engagement). Objective engagement will be evaluated through log data (eg, exercise completion time). Subjective engagement will be evaluated through exercise likability and helpfulness ratings as well as user experience interviews. The secondary outcomes will include the distal outcomes of the intervention (ie, ER skills and distress symptoms). Finally, the contextual moderators of intervention effectiveness will be explored (eg, the time of day and momentary emotional states). Results: The study commenced on February 9, 2023, and the data collection was concluded on June 13, 2023. Of the 172 eligible participants, 161 (93.6%) decided to participate. Of these 161 participants, 137 (85.1%) completed the first phase of the study. A subsample of participants (18/172, 10.5%) participated in the user experience interviews. Currently, the data processing and analyses are being conducted. Conclusions: This study will provide insight into the functioning of the intervention and identify areas for improvement. Furthermore, the findings will shed light on potential changes in the distal outcomes of the intervention (ie, ER skills and distress symptoms), which will be considered when designing a follow-up randomized controlled trial evaluating the full-scale effectiveness of this intervention. Finally, the results and data gathered will be used to design and train a recommendation algorithm that will be integrated into the app linking students to relevant content.</p
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