Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random

Abstract

Population-based cohort studies are invaluable to health research because of the breadth of data collection over time, and the representativeness of their samples. However, they are especially prone to missing data, which can compromise the validity of analyses when data are not missing at random. Having many waves of data collection presents opportunity for participants’ responsiveness to be observed over time, which may be informative about missing data mechanisms and thus useful as an auxiliary variable. Modern approaches to handling missing data such as multiple imputation and maximum likelihood can be difficult to implement with the large numbers of auxiliary variables and large amounts of non-monotone missing data that occur in cohort studies. Inverse probability-weighting can be easier to implement but conventional wisdom has stated that it cannot be applied to non-monotone missing data. This paper describes two methods of applying inverse probability-weighting to non-monotone missing data, and explores the potential value of including measures of responsiveness in either inverse probability-weighting or multiple imputation. Simulation studies are used to compare methods and demonstrate that responsiveness in longitudinal studies can be used to mitigate bias induced by missing data, even when data are not missing at random.

Keywords

Missing data inverse probability-weighting multiple imputation cohort studies longitudinal studies missing at random not missing at random non-monotone missing data loss to follow-up cohort attrition

Get full access to this article

View all access options for this article.

References

Drake

Jonson-Reid

Poverty and child maltreatment. In: Korbin

Krugman

(eds). Handbook of child maltreatment, Netherlands: Springer, 2014, pp. 131–148.

Little

RJA

Rubin

. Statistical analysis with missing data, 2nd ed. Hoboken, NJ: John Wiley & Sons, 2002.

Seaman

White

. Review of inverse probability weighting for dealing with missing data. Stat Meth Med Res 2013; 22: 278–295.

Sterne

JAC

White

Carlin

et al.

Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ 2009; 338.

Schafer

Graham

. Missing data: Our view of the state of the art. Psychol Methods 2002; 7: 147–177.

Jaeger

On testing the missing at random assumption. In: Fürnkranz

Scheffer

Spiliopoulou

(eds). Machine learning: ECML 2006, Berlin-Heidelberg: Springer, 2006, pp. 671–678.

Rubin

. Multiple imputation for nonresponse in surveys, New York: John Wiley & Sons, 1987.

Muthén

Kaplan

Hollis

. On structural equation modeling with data that are not missing completely at random. Psychometrika 1987; 52: 431–462.

Allison

Estimation of linear models with incomplete data. In: Clogg

(ed). Sociological methodology, San Fancisco: Jossey-Bass, 1987, pp. 71–103.

10.

Cole

How to deal with missing data: Conceptual overview and details for implementing two modern methods. In: Osborne

(ed). Best practices in quantitative methods, Thousand Oaks, CA: SAGE, 2008, pp. 214–239.

11.

Wooldridge

. Inverse probability weighted estimation for general missing data problems. J Econom 2007; 141: 1281–1301.

12.

Mustillo S and Kwon S. Auxiliary variables in multiple imputation when data are missing not at random. J Math Sociol 2015; 39: 73–91.

13.

White IR, Royston P and Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2011; 30: 377–399.

14.

Fielding S, Fayers PM and Ramsay CR. Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches. Health Qual Life Outcomes 2009; 7: 57.

15.

Hawkes D and Plewis I. Modelling non-response in the National Child Development Study. J R Stat Soc Ser A 2006; 169: 479–91.

16.

Seaman S and White I. Inverse probability weighting with missing predictors of treatment assignment or missingness. Commun Stat Theory Methods 2014; 43: 3499–3515.

17.

Rubin DB. Inference and missing data. Biometrika 1976; 63: 581–592.

18.

Molenberghs G, Beunckens C, Sotto C and Kenward MG. Every missingness not at random model has a missingness at random counterpart with equal fit. J R Stat Soc Series B Stat Methodol 2008; 70: 371–88.

19.

Listing J and Schlittgen R. Tests if dropouts are missed at random. Biom J 1998; 40: 929–35.

20.

Fairclough DL. Design and analysis of quality of life studies in clinical trials. Boca Raton: CRC Press, 2002.

21.

Seaman SR, White IR, Copas AJ and Li L. combining multiple imputation and inverse-probability weighting. Biometrics 2012; 68: 129–37.

22.

Thomas C, Hypponen E and Power C. Prenatal exposures and glucose metabolism in adulthood: are effects mediated through birth weight and adiposity? Diabetes Care 2007; 30: 918–24.

23.

Rubin DB. Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Serv Outcomes Res Methodol 2001; 2: 169–188.

24.

Bernaards CA, Belin TR and Schafer JL. Robustness of a multivariate normal approximation for imputation of incomplete binary data. Stat Med 2007; 26: 1368–1382.

25.

Lee KJ and Carlin JB. Multiple imputation for missing data: Fully conditional specification versus multivariate normal imputation. Am J Epidemiol 2010; 171: 624–632.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.24 MB