ABSTRACT:
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. To accurately estimate the magnitude of treatment efficacy, caution should be observed regarding the LOCF analytical bias. To conciliate with this challenge, several imputation methods have been developed in the literature to handle missing values where the most commonly used are complete case method, mean imputation method, last observation carried forward (LOCF) method and multiple imputation (MI) method. Subsequently after rigorous review, this paper concludes that LOCF method has more bias than the other three methods in most situations.
Cite this article:
Sampoornam W. Bigotry in Last Observation Carried Forward (LOCF) Analysis. International Journal of Nursing Education and Research. 2022; 10(1):19-0. doi: 10.52711/2454-2660.2022.00005
Cite(Electronic):
Sampoornam W. Bigotry in Last Observation Carried Forward (LOCF) Analysis. International Journal of Nursing Education and Research. 2022; 10(1):19-0. doi: 10.52711/2454-2660.2022.00005 Available on: https://ijneronline.com/AbstractView.aspx?PID=2022-10-1-5
REFERENCES:
1. Salim, Agus; MacKinnon, Andrew; Christensen, Helen; Griffiths, Kathleen (2008). "Comparison of data analysis strategies for intent-to-treat analysis in pre-test–post-test designs with substantial dropout rates". Psychiatry Research. 160(3): 335–345.
2. Molnar, F. J.; Hutton, B.; Fergusson, D. (2008). "Does analysis using "last observation carried forward" introduce bias in dementia research?". Canadian Medical Association Journal. 179(8): 751–753.
3. Gadbury GL, Coffey CS, Allison DB. Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF. Obes Rev 2003; 4: 175-84.
4. Mallinckrodt CH, Clark WS, Carroll RJ, et al. Assessing response profiles from incomplete longitudinal clinical trial data under regulatory considerations. J Biopharm Stat 2003; 13: 179-90.
5. Mallinckrodt CH, Clark WS, David SR. Accounting for dropout bias using mixed-effects models. J Biopharm Stat 2001; 11: 9-21.
6. Neil J. Salkind Published: 2010. Last Observation Carried Forward. Encyclopedia of Research Design
7. Lachin JM. Fallacies of last observation carried forward analyses. Clin Trials. 2016; 13(2): 161-8.
8. Simpson HB, Petkova E, Cheng J, Huppert J, Foa E, Liebowitz MR. Statistical choices can affect inferences about treatment efficacy: a case study from obsessive-compulsive disorder research. J Psychiatr Res. 2008; 42(8): 631–638.
9. Cook RJ, Zeng L, Yi GY. Marginal analysis of incomplete longitudinal binary data: a cautionary note on LOCF imputation. Biometrics. 2004; 60(3): 820–828.
10. Lane P. Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches. Pharm Stat. 2008; 7(2): 93–106.
11. Shoop SJW. Should we Ban the use of ‘Last Observation Carried forward’ Analysis in Epidemiological Studies? SM J Public Health Epidemiol. 2015; 1(1): 1004.