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SUMMARY:Leveraging multi-study\, multi-outcome data to improve external va
 lidity and efficiency of clinical trials for managing schizophrenia - Cale
 b Miles (Columbia University)
DTSTART:20240426T143000Z
DTEND:20240426T160000Z
UID:TALK215911@talks.cam.ac.uk
CONTACT:Qingyuan Zhao
DESCRIPTION:As data sources have become more plentiful and readily accessi
 ble\, the practice of data fusion has become increasingly ubiquitous. Howe
 ver\, when the focus is on a causal effect on a particular outcome\, a maj
 or limitation is that this outcome may not be available in all data source
 s. In fact\, different randomized experiments or observational studies of 
 a common exposure will often focus on potentially related\, yet distinct o
 utcomes. One such example is the Database of Cognitive Training and Remedi
 ation Studies (DoCTRS)\, which consists of several randomized trials of th
 e effect of cognitive remediation therapy on various outcomes among patien
 ts with schizophrenia. We develop causally principled methodology for fusi
 ng data sets when multiple outcomes are observed across studies\, which le
 verages outcomes of secondary interest as informative proxies for the miss
 ing outcome of primary interest\, thereby maximizing power and efficiency 
 by making full use of the available data. As this methodology relies on a 
 key transportability assumption\, we also develop methods to assess the de
 gree of sensitivity to violations of this assumption. We apply this method
 ology to data from the DoCTRS trials to make improved causal inferences ab
 out the effectiveness of cognitive remediation therapy on cognition among 
 patients with schizophrenia.
LOCATION:MR12\, Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge
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