Reducing bias due to systematic attrition in longitudinal studies: The benefits of multiple imputation

Most longitudinal studies are plagued by drop-out related to variables at earlier assessments (systematic attrition). Although systematic attrition is often analysed in longitudinal studies, surprisingly few researchers attempt to reduce biases due to systematic attrition, even though this is possible and nowadays technically easy. This is particularly true for studies of stability and the long-term prediction of developmental outcomes. We provide guidelines how to reduce biases in such cases particularly with multiple imputation. Following these guidelines does not require advanced statistical knowledge or special software. We illustrate these guidelines and the importance of reducing biases due to selective attrition with a 25-year longitudinal study on the long-term prediction of aggressiveness and delinquency.

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Long-Term Prediction of Some Officer-Effectiveness Measures from Aptitude Tests

PsycEXTRA Dataset ◽

10.1037/e594882009-001 ◽

1958 ◽

Author(s):

R. L. Thorndike ◽

Elizabeth P. Hagen

Keyword(s):

Aptitude Tests ◽

Term Prediction ◽

Long Term Prediction

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Novel robust time series analysis for long-term and short-term prediction

Scientific Reports ◽

10.1038/s41598-021-91327-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hiroshi Okamura ◽

Yutaka Osada ◽

Shota Nishijima ◽

Shinto Eguchi

Keyword(s):

Robust Regression ◽

Simulated Data ◽

New Method ◽

Short Term ◽

Least Absolute Deviations ◽

Term Prediction ◽

Regression Approach ◽

Short Term Prediction ◽

Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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