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DTSTART:20241103T020000
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DESCRIPTION:NOTE: BIS 526 students are required to attend in person. Other
 s are invited to attend in person\, but may also attend via Zoom. Charlot
 te Fowler\, PhD candidate Department of Biostatistics Columbia University
  Mailman School of Public Health ABSTRACT Data from smartphones and weara
 ble devices provide rich longitudinal information on participants and all
 ow for causal inference for daily exposures and outcomes. However\, infor
 mative missingness\, latent variables\, and unmeasured confounding are co
 mmon in mobile health studies an introduce bias. In addition\, there are 
 likely violations to stationarity\, a key assumption for traditional long
 itudinal methods. To overcome this challenge\, we first propose an expect
 ation maximization algorithm to adapt the conventional test for unit root
  non-stationarity to a context with missing data and develop a sensitivit
 y analysis for data missing not at random. Using our method\, we identify
  a patient with bipolar spectrum disorder who has a unit root in their da
 ily negative mood score data. We hypothesize the non-stationarity may res
 ult from the underlying latent disease states such as mania or depression
 \, and thus we additionally develop a model to identify and control for l
 atent modification and confounding. Specifically\, we propose a hidden Ma
 rkov model for individual causal inference which handles missing data in 
 the outcome through marginalization and multiple imputation. We compare t
 he performance of our proposed model with a frequentist and a Bayesian im
 plementation to a naive approach in a simulation and application to a mul
 ti-year smartphone study of bipolar patients. We employ the approach to e
 valuate the individual effect of digital social activity on self-reported
  loneliness across different latent disease states.\n\nSpeaker:\nCharlott
 e Fowler\n\nAdmission:\nFree\n\nDetails URL:\nhttps://medicine.yale.edu/e
 vent/iostatisticseminar1-16/\n
DTEND;TZID=America/New_York:20240116T130000
DTSTAMP:20261027T070212Z
DTSTART;TZID=America/New_York:20240116T120000
GEO:41.302961;-72.931638
LOCATION:106 A & B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar: "Individual causal inference and time 
 series methods for health studies with missing data"
UID:e03735e6-6186-41fd-b7f2-17ee11d34b21
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