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DTSTART:20070311T020000
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DTSTAMP:20260126T121242
DTSTART;TZID=America/Detroit:20260128T120000
DTEND;TZID=America/Detroit:20260128T130000
SUMMARY:Workshop / Seminar:Mathematical Biology Seminar: How much data is needed to validate multiscale models of viral infections?
DESCRIPTION:Uncertainty in parameter estimates from fitting mathematical models to empirical data limits the model’s ability to uncover mechanisms of interaction. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation\, I will present methodologies that can help determine when a model can reveal its parameters. I will apply them in the context of virus infections in animals and humans at within-host\, population\, and multiscale levels.  Using these approaches\, I will provide insight into the sources of uncertainty and provide guidelines for the types of model assumptions\, optimal experimental design\, and biological information needed for improved predictions.\n\nThis seminar is hybrid: meeting in Weiser 296 and via Zoom:\nhttps://umich.zoom.us/j/97725897086\nMeeting ID: 977 2589 7086\nPasscode: mathbio
UID:143969-21895434@events.umich.edu
URL:https://events.umich.edu/event/143969
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Biology,data,Life Science,Mathematical Biology,Mathematical Modeling,Mathematics
LOCATION:Weiser Hall - 296
CONTACT:
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DTSTAMP:20260107T161642
DTSTART;TZID=America/Detroit:20260128T120000
DTEND;TZID=America/Detroit:20260128T130000
SUMMARY:Lecture / Discussion:MPSDS / JPSM Seminar Series:  Sensitivity Analyses for Nonignorable Selection Bias When Estimating Subgroup Parameters in Nonprobability Samples: A Weighting Approach
DESCRIPTION:MPSDS / JPSM Seminar Series\nMPSDS M3 Series: Mastery\, Methodology\, Meetups\n\nIn person\, room 1070 Institute for Social Research\, and via Zoom. \nThe Zoom call will be locked 10 minutes after the start of the presentation.\n\nSensitivity Analyses for Nonignorable Selection Bias When Estimating Subgroup Parameters in Nonprobability Samples: A Weighting Approach\n\nSelection bias in survey estimates is a major concern\, affecting both nonprobability samples and probability samples with low response rates. The proxy-pattern mixture model (PPMM) offers a method for conducting a sensitivity that assumes a nonignorable selection mechanism\, where selection depends on survey outcomes of interest. This approach requires summary-level auxiliary information for the target population of interest from a reference data source. While PPMM methods have been successfully applied to derive overall population-level estimates\, extension to domain-level estimates is challenging when population-level summaries for the specific subgroup are unavailable. This occurs when the domain indicator is observed only in the survey\, or for complex intersectional subgroups where stable/reliable population-level auxiliary variable estimates are unavailable. To combat this issue\, we propose a novel approach: creating nonignorable selection weights based on the PPMM based on a re-expression of the PPMM as a selection model. These weights can be directly applied to calculate domain-level estimates\, circumventing the need for domain-specific population-level summaries of auxiliary variables. They rely on a single sensitivity parameter (ranging from 0 to 1) that captures a spectrum of nonresponse assumptions\, ranging from an ignorable mechanism to an extreme nonignorable mechanism. We discuss differences in weight construction for continuous versus binary outcomes\, describe the necessary assumptions for these weights to produce informative domain-level estimates\, and illustrate properties through simulation. We then apply the approach to the Census Household Pulse Survey to estimate various subgroup quantities under a range of assumptions on the selection mechanism.\n\nRebecca R. Andridge\, PhD\nThe Ohio State University\nCollege of Public Health\, Division of Biostatistics\nAssociate Dean for Undergraduate Studies\nProfessor of Biostatistics\n\nDr. Andridge's research is focused on imputation methods for missing data\, primarily when missingness is driven by the missing values themselves (missing not at random)\, and on measures of selection bias for nonprobability samples. She also works on statistical challenges that arise in analysis of data from group-randomized trials. She collaborates with researchers across campus\, including the Institute for Behavioral Medicine Research\, the Nisonger Center for Excellence in Developmental Disabilities\, and The OSU Comprehensive Cancer Center\, and serves as Lead Methodologist for several state-sponsored population-based surveys. She is an Elected Fellow of the American Statistical Association (2020).
UID:143425-21893147@events.umich.edu
URL:https://events.umich.edu/event/143425
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Anthropology,Biomedical,Biosciences,brown bag,Data,Data Analysis,Data Collection,Data Curation,Data Linkage,Data Management,Data Science,Discussion,Free,Health Data,In Person,Lecture,Public Health,seminar,Survey Methodology,Survey Methods,Survey Research,symposium,Virtual
LOCATION:Off Campus Location - Room 1070, Institute for Social Research
CONTACT:
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