BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Dana-Farber Cancer Institute - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ds.dfci.harvard.edu
X-WR-CALDESC:Events for Dana-Farber Cancer Institute
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230509T130000
DTEND;TZID=America/New_York:20230509T140000
DTSTAMP:20260420T202237
CREATED:20230410T125338Z
LAST-MODIFIED:20230515T123922Z
UID:4170-1683637200-1683640800@ds.dfci.harvard.edu
SUMMARY:Model-robust and Efficient Covariate Adjustment for Cluster-randomized Experiments
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday May 9\, 2023\n1:00PM Eastern Time \nJoin the Zoom. \nFan Li\, PhD\nAssistant Professor of Biostatistics\, Yale School of Public Health \nCluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions\, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However\, the validity of model-based covariate adjustment is unclear when the working models are misspecified\, leading to ambiguity of estimands and risk of bias. In this article\, we first adapt two conventional model-based methods\, generalized estimating equations and linear mixed models\, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. Furthermore\, we propose an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and\, if ignored\, can lead to bias of model-based estimators. For our proposed estimator\, we prove that when the nuisance functions are consistently estimated by machine learning algorithms\, the estimator is consistent\, asymptotically normal\, and efficient. When the nuisance functions are estimated via parametric working models\, the estimator is triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
URL:https://ds.dfci.harvard.edu/event/model-robust-and-efficient-covariate-adjustment-for-cluster-randomized-experiments/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/04/fanli-scaled-e1681131195600.jpeg
END:VEVENT
END:VCALENDAR