Balancing Efficacy and Computational Burden: Weighted Mean, Multiple Imputation, and Inverse Probability Weighting Methods For Item Non-response in Reliable Scales
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Moderator
Frances Hsu, BS, MS
Oregon Health & Science University
Presenter
Qingxia "Cindy" Chen, PhD
Vanderbilt University Medical Center
Statement of Purpose
In survey research, handling missing data is essential for producing valid inferences. Multiple imputation (MI) is widely regarded as the gold standard for addressing item non-response, while inverse probability weighting (IPW) is typically used to correct for unit non-response. However, MI requires substantial computational resources, especially when combined with downstream analyses, and IPW can yield unstable estimates due to high variability in the weights. As a computationally efficient alternative, weighted means (WMean) approach uses an individual's available responses to impute missing values, but may oversimplify the underlying missingness structure, potentially biasing results.
Motivated by the Social Determinants of Health module in the All of Us Research Program (AoURP), this research investigates the trade-offs among WMean, MI, and IPW for item non-response in surveys with high internal reliability. Through simulation studies, we evaluate their performance in terms of statistical accuracy, computational burden, and robustness, offering practical guidance for large-scale biobanks and survey platforms where efficiency and validity are both critical.
Learning Objective
- Identify and apply the most appropriate method for handling item non-response based on survey characteristics, analytic goals, and resource constraints.
Additional Information
The target audience for this activity includes physicians, nurses, other healthcare providers, and medical informaticians.
No commercial support was received for this activity.
Completion of this “Other Activity (Regularly Scheduled Series – RSS)” is demonstrated by participating in the live webinar or viewing the on-demand recording, engaging with presenters during the live session by submitting questions, and completing the evaluation survey at the conclusion of the course.
Learners may claim credit and download a certificate upon submission of the evaluation. Participation in additional resources and the course forum is encouraged but optional.
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this Other activity (Regularly Scheduled Series (RSS)) for a maximum of 12 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
- Nurse Planner (Content): Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN
- Approved Contact Hours: 12 CME/CNE
It is the policy of the American Medical Informatics Association (AMIA) to ensure that Continuing Medical Education (CME) activities are independent and free of commercial bias. To ensure educational content is objective, balanced, and guarantee content presented is in the best interest of its learners and the public, the AMIA requires that everyone in a position to control educational content disclose all financial relationships with ineligible companies within the prior 24 months. An ineligible company is one whose primary business is producing, marketing, selling, re-selling or distributing healthcare products used by or on patients. Examples can be found at accme.org.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to the planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
Faculty and planners who refuse to disclose any financial relationships with ineligible companies will be disqualified from participating in the educational activity.
For an individual with no relevant financial relationship(s), course participants must be informed that no conflicts of interest or financial relationship(s) exist.
Disclosures
Disclosures of relevant financial relationships of all planners and presenters of the Journal Club.
Planning Committee
The planning committee and reviewers reported that they have no relevant financial relationship(s) with ineligible companies to disclose.
- Joanna Abraham, PhD, FACMI, FAMIA
- Jifan Gao, MS
- Frances Hsu, BS, MS
- Sonish Sivarajkumar
- Song Wang, MS
- Faisal Yaseen
Presenter(s)
The following presenters have no relevant financial relationship(s) with ineligible companies to disclose.
- Qingxia “Cindy” Chen, PhD
AMIA Staff
The following staff have no relevant financial relationship(s) with ineligible companies to disclose.
- Jennifer Wahl
- Melissa Kauffman
