Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out: Table 1.

Biometrika ◽  
2016 ◽  
Vol 103 (3) ◽  
pp. 683-700 ◽  
Author(s):  
Peisong Han
2004 ◽  
Vol 23 (14) ◽  
pp. 2215-2226 ◽  
Author(s):  
Carole Dufouil ◽  
Carol Brayne ◽  
David Clayton

2009 ◽  
Vol 195 (3) ◽  
pp. 249-256 ◽  
Author(s):  
Dieter Wolke ◽  
Andrea Waylen ◽  
Muthanna Samara ◽  
Colin Steer ◽  
Robert Goodman ◽  
...  

BackgroundParticipant drop-out occurs in all longitudinal studies, and if systematic, may lead to selection biases and erroneous conclusions being drawn from a study.AimsWe investigated whether drop out in the Avon Longitudinal Study of Parents And Children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders.MethodTeacher reports of disruptive behaviour among currently participating, previously participating and never participating children aged 8 years in the ALSPAC longitudinal study were collected. Data on family factors were obtained in pregnancy. Simulations were conducted to explain the impact of selective drop-out on the strength of prediction.ResultsDrop out from the ALSPAC cohort was systematic and children who dropped out were more likely to suffer from disruptive behaviour disorder. Systematic participant drop-out according to the family variables, however, did not alter the association between family factors obtained in pregnancy and disruptive behaviour disorder at 8 years of age.ConclusionsCohort studies are prone to selective drop-out and are likely to underestimate the prevalence of psychiatric disorder. This empirical study and the simulations confirm that the validity of regression models is only marginally affected despite range restrictions after selective drop-out.


2021 ◽  
Author(s):  
Katherine Laura Best ◽  
Lydia Gabriela Speyer ◽  
Aja Louise Murray ◽  
Anastasia Ushakova

Identifying predictors of attrition is essential for designing longitudinal studies such that attrition bias can be minimised, and for identifying the variables that can be used as auxiliary in statistical techniques to help correct for non-random drop-out. This paper provides a comparative overview of predictive techniques that can be used to model attrition and identify important risk factors that help in its prediction. Logistic regression and several tree-based machine learning methods were applied to Wave 2 dropout in an illustrative sample of 5000 individuals from a large UK longitudinal study, Understanding Society. Each method was evaluated based on accuracy, AUC-ROC, plausibility of key assumptions and interpretability. Our results suggest a 10% improvement in accuracy for random forest compared to logistic regression methods. However, given the differences in estimation procedures we suggest that both models could be used in conjunction to provide the most comprehensive understanding of attrition predictors.


2004 ◽  
Vol 23 (9) ◽  
pp. 1455-1497 ◽  
Author(s):  
Joseph W. Hogan ◽  
Jason Roy ◽  
Christina Korkontzelou

2008 ◽  
Vol 27 (30) ◽  
pp. 6276-6298 ◽  
Author(s):  
Peter M. Philipson ◽  
Weang Kee Ho ◽  
Robin Henderson

2014 ◽  
Vol 38 (5) ◽  
pp. 453-460 ◽  
Author(s):  
Jens B. Asendorpf ◽  
Rens van de Schoot ◽  
Jaap J. A. Denissen ◽  
Roos Hutteman

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.


2011 ◽  
Vol 70 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Ben (C) Fletcher ◽  
Jill Hanson ◽  
Nadine Page ◽  
Karen Pine

Two 3-month longitudinal studies examined weight loss following a 1-month behavioral intervention (FIT-DSD) focusing on increasing participants’ behavioral flexibility and breaking daily habits. The goal was to break the distal habits hypothesized as playing a role in unhealthy dietary and activity behaviors. The FIT-DSD intervention required participants to do something different each day and to engage in novel weekly activities to expand their behavioral repertoire. These activities were not food- or exercise-related. In Study 1, the FIT-DSD program was compared with a control condition where participants engaged in daily tasks not expected to influence behavioral flexibility. Study 2 used an active or quasicontrol group in which half the participants were also on food diets. Measures in both studies were taken pre-, post-, and post-postintervention. In Study 1, FIT-DSD participants showed greater weight loss that continued post-postintervention. In Study 2, all participants on the FIT-DSD program lost weight, weight loss continued post-postintervention, and participants who were also dieting lost no additional weight. A dose relationship was observed between increases in behavioral flexibility scores and weight loss, and this relationship was mediated by calorie intake. Corresponding reductions in BMI were also present. Increasing behavioral flexibility may be an effective approach for tackling obesity and also provides affective and potential life-skill benefits.


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