4 papers
Proximal Causal Inference for Time Series from N-of-1Mobile Device Data
Fanyu Cui, Charlotte Fowler, Xiaoxuan Cai +3
Unmeasured confounding challenges causal inference in intensive longitudinal studies, potentially biasing treatment effect estimates. The proximal causal inference (PCI) framework…
Missing data in non-stationary multivariate time series from digital studies in Psychiatry
Xiaoxuan Cai, Charlotte R. Fowler, Li Zeng +6
Mobile technology (e.g., mobile phones and wearable devices) provides scalable methods for collecting physiological and behavioral biomarkers in patients' naturalistic settings, as…
Individual causal effect estimation accounting for latent disease state modification among bipolar participants in mobile health studies
Charlotte R. Fowler, Xiaoxuan Cai, Habiballah Rahimi-Eichi +4
Individuals with bipolar disorder tend to cycle through disease states such as depression and mania. The heterogeneous nature of disease across states complicates the evaluation of…
Causal estimands and identification of time-varying effects in non-stationary time series from N-of-1 mobile device data
Xiaoxuan Cai, Li Zeng, Charlotte Fowler +5
Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures and covariates, presented as intensive longitudinal or mul…