CoChat
Sign Up
Cited by 481Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologySurvey Methodology and Nonresponse

The DOI matches its Crossref record; 12 of the 13 references it cites were found in a registry or an index.

Imputation of missing longitudinal data: a comparison of methods

Engels, J·2003·Journal of Clinical Epidemiology
DOI 10.1016/s0895-4356(03)00170-7
Open this paper in CoChat — free account

Abstract

Background and objectives Missing information is inevitable in longitudinal studies, and can result in biased estimates and a loss of power. One approach to this problem is to impute the missing data to yield a more complete data set. Our goal was to compare the performance of 14 methods of imputing missing data on depression, weight, cognitive functioning, and self-rated health in a longitudinal cohort of older adults.Methods We identified situations where a person had a known value following one or more missing values, and treated the known value as a "missing value." This "missing value" was imputed using each method and compared to the observed value. Methods were compared on the root mean square error, mean absolute deviation, bias, and relative variance of the estimates.Results Most imputation methods were biased toward estimating the "missing value" as too healthy, and most estimates had a variance that was too low. Imputed values based on a person's values before and after the "missing value" were superior to other methods, followed by imputations based on a person's values before the "missing value." Imputations that used no information specific to the person, such as using the sample mean, had the worst performance.Conclusions We conclude that, in longitudinal studies where the overall trend is for worse health over time and where missing data can be assumed to be primarily related to worse health, missing data in a longitudinal sequence should be imputed from the available longitudinal data for that person.

Overview

Integrity signals

Registry

Crossref · 10.1016/s0895-4356(03)00170-7

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 13 references

Cited by

Sorted by citations

Items 401–481 of 481

Related

See all

Metadata

Authors
Engels, J.
Affiliations
University of Washington (US)
Corresponding author
Engels, J
Venue
Journal of Clinical Epidemiology
Publisher
Elsevier BV
Published
2003-10
Volume
56
Issue
10
Pages
968-976
ISSN
0895-4356 (ISSN-L)
DOI
10.1016/s0895-4356(03)00170-7
OpenAlex
W2111700774
Cited by
478
References
13
Citations by year
2026: 14 · 2025: 27 · 2024: 26 · 2023: 26 · 2022: 22 · 2021: 35 · 2020: 16 · 2019: 19 · 2018: 29 · 2017: 23 · 2016: 19 · 2015: 23 · 2014: 23 · 2013: 22 · 2012: 13
FWCI
14.33
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Journal of Clinical Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (n01hc085079-024, n01hc085086-022)
  • National Heart, Lung, and Blood Institute (N01 HC85086, N01-HC85079)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
Keywords
missing data imputation, longitudinal data, older adults, depression, weight, cognitive function, self-rated health, imputation method comparison
MeSH terms
Aged, Analysis of Variance, Coronary Disease, Data Interpretation, Statistical, Depression, Female, Health Status, Humans, Longitudinal Studies, Male, Research Design, Risk Factors, United States, Bias, Stroke

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review