ArticleMayo Clinic proceedings. Innovations, quality & outcomes2021
Do Missing Values Influence Outcomes in a Cross-sectional Mail Survey?
Article in Mayo Clinic proceedings. Innovations, quality & outcomes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Hybrid machine learning approach for landslide prediction, Uttarakhand, India.Scientific reports · 2022Trial
- Social context of drinking moderates associations between descriptive norms and alcohol consumption among college students during the COVID-19 pandemic.Journal of American college health : J of ACH · 2026Article
- Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.Frontiers in psychiatry · 2025Article
- The Association of Cultural Identification with Obesity among African Americans.Journal of racial and ethnic health disparities · 2024Article
- Advancing equitable access to digital mental health in the Asia-Pacific region in the context of the COVID-19 pandemic and beyond: A modified Delphi consensus study.PLOS global public health · 2024Article
- Staying in or out? COVID-19-induced healthcare utilization avoidance and associated socio-demographic factors in rural India.BMC public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveTo determine the effects of missing and inconsistent data on a weight management mail survey results. PATIENTS AND
methodsWeight management surveys were sent to 5000 overweight and obese individuals in the Learning Health System Network. Survey information was collected between October 27, 2017, and March 1, 2018. Some participants reported body mass index (BMI) values inconsistent with the intended overweight and obese sampling cohort. Analyses were performed after excluding these surveys and also performed again after setting these low BMI values to missing. Models were run after imputing missing values using expectation-maximization, Markov chain Monte Carlo, random forest imputation, multivariate imputation by chained equations, and multiple imputation and replacing missing BMI values with the minimum, maximum, mean, or median of the known BMI values.
resultsOf 2799 surveys, 222 (8%) had missing BMI values and 155 (6%) reported invalid BMI values. Overall, 725 of these 2799 surveys (26%) were missing at least 1 variable that was essential to the main analyses. Different imputation methods consistently found that BMI was related to age, sex, race, marital status, and education. Patients with a BMI of 35.0 kg/m
conclusionAnalyses using different imputation methods were consistent with the original published results. Missing data likely did not affect the study results.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.