Cited by 328Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsStatistical Methods in Epidemiology

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Fallacies of last observation carried forward analyses

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Lachin, John M·2016·Clinical Trials

DOI 10.1177/1740774515602688PMC PMC4785044

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Abstract

Background: Last observation carried forward is a common statistical approach to the analysis of longitudinal repeated measures data where some follow-up observations may be missing. In a last observation carried forward analysis, a missing follow-up visit value is replaced by (imputed as) that subject’s previously observed value, that is, the last observation is carried forward. The combination of the observed and imputed data is then analyzed as though there were no missing data. Purpose: There have been numerous statistical demonstrations of faults of this approach. In 2012, the National Research Council’s Panel on Handling Missing Data in Clinical Trials issued a report that raised concerns with the use of last observation carried forward and described alternative methods that offer greater statistical validity. Nevertheless, the method persists, and its use is rampant. A search of the key word “last observation carried forward” using Google Scholar yielded “about 2480” published citations during 2014 alone, the overwhelming majority presenting the results of scientific studies. However, there has not been a simple explanation of the statistical deficiencies of last observation carried forward. Such a description is presented herein. Results: A simple repeated measures model is described for quantitative observations at two times (e.g. 1 and 2 years), with complete values at 1 year that are used to impute by last observation carried forward the missing values at 2 years under the missing completely at random assumption. This results in a mixture distribution of observed and imputed values at 2 years with mean and variance that are a function of the mixture of the 1- and 2-year distributions. The expressions show that last observation carried forward is only unbiased when the distribution of the observed values at 1 year is exactly equal to the distribution of the missing values at 2 years, the latter, of course, being unknown. Limitations: When the values at 2 years are not randomly missing, no simple expressions for the mean and variance of the mixture distribution are possible without additional unverifiable assumptions. Conclusion: All analyses using last observation carried forward are of questionable veracity, if not being outright specious (definition: appearing to be true but actually false). It is hoped that future studies will make a more vigorous attempt to minimize the amount of missing data and that more valid statistical analyses will be employed in cases where missing data occur. Last observation carried forward should not be employed in any analyses.

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Additional file 2 of Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation
Malatesta, S., Weir, I. R., Weber, S., Bouton, T. C., Carney, T., Theron, D., MYERS, B. J., Horsburgh, C. R., Warren, R. M., Jacobson, K. R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21594102
Additional file 2 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · Figshare · doi:10.6084/m9.figshare.21213348
Additional file 8 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664037
Additional file 4 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664016
Additional file 1 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · Figshare · doi:10.6084/m9.figshare.21213342
Additional file 5 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · Figshare · doi:10.6084/m9.figshare.21213357
Additional file 1 of Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation
Malatesta, S., Weir, I. R., Weber, S., Bouton, T. C., Carney, T., Theron, D., MYERS, B. J., Horsburgh, C. R., Warren, R. M., Jacobson, K. R., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21594099
Additional file 9 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664040
Additional file 7 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664034
Additional file 3 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · Figshare · doi:10.6084/m9.figshare.21213351
Additional file 10 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664043
Additional file 5 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664019
Additional file 6 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664028
Additional file 11 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664046
Additional file 2 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.24954552
Additional file 1 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.24954495
Additional file 4 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Zeng, Z., Tang, X., Liu, Y., He, Z., & Gong, X. (2022) · Figshare · doi:10.6084/m9.figshare.21213354
Additional file 3 of Studying missingness in spinal cord injury data: challenges and impact of data imputation
Bourguignon, L., Lukas, L. P., Guest, J. D., Geisler, F. H., Noonan, V., Curt, A. E. P., Brüningk, S. C., & Jutzeler, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26664010
Brief Report: The Role of Ryan White HIV/AIDS Program Services in Sustaining Viral Suppression Among People Living with HIV: Insights from South Carolina
Baker, P. A., Xiao, J., Ma, Y., He, B., Olatosi, B., & Zhang, J. (2026) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000003831

Authors: Lachin, J. M.
Affiliations: George Washington University (US)
Corresponding author: Lachin, John M
ORCID: Lachin, John M
Venue: Clinical Trials
Publisher: SAGE Publications
Published: 2015-09-22
Volume: 13
Issue: 2
Pages: 161-168
ISSN: 1740-7745 (ISSN-L)
1740-7753
DOI: 10.1177/1740774515602688
PMC: PMC4785044
OpenAlex: W2134947937
Cited by: 328
References: 13
Citations by year: 2026: 42 · 2025: 46 · 2024: 52 · 2023: 34 · 2022: 40 · 2021: 24 · 2020: 30 · 2019: 17 · 2018: 15 · 2017: 15 · 2016: 11 · 2015: 1
FWCI: 12.46
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Clinical Trials (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Diabetes and Digestive and Kidney Diseases (U01DK098246)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: last observation carried forward, missing data, imputation, missing completely at random, bias, mixture distributions, repeated measures models, statistical validity
MeSH terms: Data Collection, Data Interpretation, Statistical, Follow-Up Studies, Longitudinal Studies, Research Design, Bias

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