Dealing with missing data in longitudinal studies

Lead Research Organisation: University of Bristol
Department Name: Social Medicine

Abstract

Longitudinal studies – studies in which individuals are followed over periods of many months or years – are of great importance in understanding how aspects of people’s lifestyle or environment influence their risk of disease. When individuals are followed over extended periods it is inevitable that measurements on particular variables are sometimes missing, for example because a measuring device broke down, or a subject did not answer certain questions or did not attend an examination. Missing values make analyses of data from longitudinal studies more complicated, because they can lead to results that are both biased (they differ from the results that would be observed if the missing values were taken into account) and inefficient (they are less precise than they would be if missing values were taken into account). New statistical methods that address these issues have been proposed, and have the potential to decrease bias and increase efficiency in analyses of longitudinal studies. However these methods can be complex and there is currently little practical experience of their use. We are investigating practical issues in applying these methods, in particular a method called multiple imputation, in order to demonstrate the circumstances in which they are useful, develop strategies for choosing models and confirming that they are appropriate, and compare different approaches and software. As well as publishing the results of the study in scientific journals, we will develop guidelines for people who use the methods in the future.

Technical Summary

Analyses of data from longitudinal studies are often complicated by the presence of missing values, caused by participant dropout or non-response. Failure to allow for this can lead to both biased and inefficient statistical analyses. Analyses ignoring problems caused by missing data are common. Statistical research has generated better ways to deal with these problems, but the methods are technically challenging. The proposed research will focus on the application of multiple imputation (MI) - the most flexible available method ? in longitudinal studies. We will demonstrate the potential of MI to reduce bias and increase precision in analyses of data from the ALSPAC birth cohort study and the ART-CC and ART-LINC HIV cohort collaborations. We will also clarify the circumstances in which analyses allowing for missing data are likely to have advantages over simpler methods. We will develop a framework for simulations that allow evaluation of characteristics of imputation procedures, and use both simulations and analyses of longitudinal data to examine how to deal with the model complexity that characterises application of these procedures. We will adapt existing software to improve model diagnostics that may alert the user to problems in imputation procedures and to facilitate sensitivity analyses that examine robustness of results to data that are missing not at random MNAR). We will work with members of the CONSORT and STROBE groups, and with journal editors, to provide guidelines on reporting analyses that deal with missing data.

Publications

10 25 50
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Antiretroviral Therapy Cohort Collaboration (2010) Causes of death in HIV-1-infected patients treated with antiretroviral therapy, 1996-2006: collaborative analysis of 13 HIV cohort studies. in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

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Hughes RA (2016) Comparison of imputation variance estimators. in Statistical methods in medical research

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Hughes RA (2014) Joint modelling rationale for chained equations. in BMC medical research methodology

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Spratt M (2010) Strategies for multiple imputation in longitudinal studies. in American journal of epidemiology

 
Description Citation in research paper
Geographic Reach Multiple continents/international 
Policy Influence Type Influenced training of practitioners or researchers
Impact Our 2009 BMJ paper provides guidelines on the conduct and reporting analyses that deal with missing data. It has been cited 55 times (according to Google Scholar) and has thus has a clear influence on the conduct and reporting of medical research
 
Description MRC Methodology Research
Amount £481,663 (GBP)
Organisation Medical Research Council (MRC) 
Sector Academic/University
Country United Kingdom
Start 04/2010 
End 04/2013
 
Title ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions 
Description Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I ("Risk Of Bias In Non-randomised Studies - of Interventions"), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool is particularly useful to those undertaking systematic reviews that include non-randomised studies. 
Type Of Material Improvements to research infrastructure 
Year Produced 2014 
Provided To Others? Yes  
Impact The online version of ROBINS-I (published under its previous name ACROBAT-NRSI) was highliy cited. We expect that the impact of the tool will become evidence in coming years. 
URL http://www.riskofbias.info
 
Description MiDiA 
Organisation London School of Hygiene and Tropical Medicine (LSHTM)
Department Faculty of Epidemiology and Population Health
Country United Kingdom 
Sector Academic/University 
PI Contribution This collaboration was atrted up as part of this MRC grant.
Collaborator Contribution Joint publication, discussion, further collaboration on MRC grant (PI Dr Tilling)Joint publication, collaboration on MRC grant (PI Dr Tilling)Discussions leading to methodological developmentscollaboration on MRC grant (PI Dr Tilling), co-authored papers.Discussions leading to methodological developments
Impact Co-authored publications
Start Year 2007
 
Description MiDiA 
Organisation Medical Research Council (MRC)
Department MRC Clinical Trials Unit
Country United Kingdom 
Sector Public 
PI Contribution This collaboration was atrted up as part of this MRC grant.
Collaborator Contribution Joint publication, discussion, further collaboration on MRC grant (PI Dr Tilling)Joint publication, collaboration on MRC grant (PI Dr Tilling)Discussions leading to methodological developmentscollaboration on MRC grant (PI Dr Tilling), co-authored papers.Discussions leading to methodological developments
Impact Co-authored publications
Start Year 2007
 
Description MiDiA 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution This collaboration was atrted up as part of this MRC grant.
Collaborator Contribution Joint publication, discussion, further collaboration on MRC grant (PI Dr Tilling)Joint publication, collaboration on MRC grant (PI Dr Tilling)Discussions leading to methodological developmentscollaboration on MRC grant (PI Dr Tilling), co-authored papers.Discussions leading to methodological developments
Impact Co-authored publications
Start Year 2007
 
Description MiDiA 
Organisation University of Cambridge
Department MRC Biostatistics Unit
Country United Kingdom 
Sector Public 
PI Contribution This collaboration was atrted up as part of this MRC grant.
Collaborator Contribution Joint publication, discussion, further collaboration on MRC grant (PI Dr Tilling)Joint publication, collaboration on MRC grant (PI Dr Tilling)Discussions leading to methodological developmentscollaboration on MRC grant (PI Dr Tilling), co-authored papers.Discussions leading to methodological developments
Impact Co-authored publications
Start Year 2007