Bigotry in Last Observation Carried Forward (LOCF) Analysis

Sampoornam W.*

Professor, Mental Health Nursing Department, The Tamilnadu Dr. M. G. R. Medical University, Chennai,

Dhanvantri College of Nursing Pallakkapalayam, Namakkal, Tamilnadu, India.

*Corresponding Author E-mail: sampoornamwebster@yahoo.in

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.

KEYWORDS: Last Observation Carried Forward (LOCF), Bias, Disadvantages.

INTRODUCTION:

One method of handling missing data is simply to impute or fill in values based on existing data. A standard method to do this is the Last-Observation-Carried-Forward (LOCF) method. The LOCF method allows for the analysis of the data. However, recent research shows that this method gives a biased estimate of the treatment effect and underestimates the variability of the estimated result (Salim and Molnar, etal., 2008).

LOCF is used to maintain the sample size and to reduce the bias caused by the attrition of participants in a study (Neil J, 2010). Paradoxically all analyses using last observation carried forward are of questionable veracity, if not being outright deceptive.

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 (Lachin JM, 2016).

ASSUMPTIONS:

The basic assumption underlying LOCF is that patients who are given treatments get better, which makes treating missing data as if the past had continued unchanged conservative and is often not true. In course of clinical trials this technique replaces a participant's missing values after dropout with the last available measurement and assumes that the participant's responses (e.g., outcome measures) would have been stable from the point of dropout to trial completion, rather than declining or improving further (Mallinckrodt CH, 2003).

As an example, assume that there are 8 weekly assessments after the baseline observation. If a patient drops out of the study after the third week, then this value is "carried forward" and assumed to be his or her score for the 5 missing data points. The assumption is that the patients improve gradually from the start of the study until the end, so that carrying forward an intermediate value is a conservative estimate of how well the person would have done had he or she remained in the study.

ADVANTAGES OF LOCF

· It minimizes the number of the subjects who are eliminated from the analysis

· It allows the analysis to examine the trends over time, rather than focusing simply on the endpoint

DISADVANTAGES OF LOCF:

The use of LOCF is statistically un-principled, with assumptions that are only occasionally legitimate. Whilst this method may be applicable in rare circumstances, the alternatives should be promoted for all epidemiological researchers and may hopefully result in better quality inferences and therefore more accurate results to translate into clinical practice (Shoop SJW, 2015).

Furthermore, the overestimation of the precision might lead to underestimation of the standard error and inflation of the type I error. There are several published examples where LOCF does poorly (Cook RJ, 2004 and Lane P, 2008).

CONCLUSION:

The results showed that LOCF has the largest bias and the poorest 95% coverage probability in most cases under MAR (Missing at Random), MCAR (Missing Completely at Random) and MNAR (Missing Not at Random) missing mechanisms. Hence, LOCF should not be used in a longitudinal data analysis.

REFERENCES:

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Received on 24.11.2021 Modified on 22.12.2021

Int. J. Nur. Edu. and Research. 2022; 10(1):19-20.

DOI: 10.52711/2454-2660.2022.00005