Handling Missing Data in the Modeling of Intensive Longitudinal Data

Ji, Linying; Chow, Sy-Miin; Schermerhorn, Alice C.; Jacobson, Nicholas C.; Cummings, E. Mark

doi:10.1080/10705511.2017.1417046

Cited by 74Statistical Methods and Bayesian InferenceMental Health Research TopicsPsychometric Methodologies and Testing

Handling Missing Data in the Modeling of Intensive Longitudinal Data

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Ji, Linying, Chow, Sy-Miin, Schermerhorn, Alice C., Jacobson, Nicholas C., Cummings, E. Mark·2018·Structural Equation Modeling: A Multidisciplinary Journal

DOI 10.1080/10705511.2017.1417046PMC PMC6625802

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Abstract

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children's influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).

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Simulation studies with intensive longitudinal data suggest that multiple imputation and full-information maximum likelihood reliably produce better parameter estimates than listwise deletion.
“More specifically, simulation studies with intensive longitudinal data suggest that multiple imputation and full-information maximum likelihood reliably produce better parameter estimates than listwise deletion59.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [59]

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Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701 · see citing works
mice: Multivariate Imputation by Chained Equations inR
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870 · see citing works
A Practical Guide to Splines
de Boor, C. (1978) · Applied mathematical sciences · doi:10.1007/978-1-4612-6333-3 · Cited by 12,089 · see citing works
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638 · see citing works
Inference and missing data
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925 · see citing works
Missing data analysis: making it work in the real world
Graham, J. W. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.58.110405.085530 · Cited by 6,163 · see citing works
Analysis of Incomplete Multivariate Data
Schafer, J. L. (1997) · doi:10.1201/9781439821862 · Cited by 6,012 · see citing works
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072 · see citing works
Multiple imputation: a primer
Schafer, J. L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800102 · Cited by 3,949 · see citing works
Journal of Statistical Software
de Leeuw, J. (2009) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.10 · Cited by 3,608 · see citing works
How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory
Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007) · Prevention Science · doi:10.1007/s11121-007-0070-9 · Cited by 2,795 · see citing works
Amelia II: A Program for Missing Data
Honaker, J., King, G., & Blackwell, M. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i07 · Cited by 2,552 · see citing works
A comparison of inclusive and restrictive strategies in modern missing data procedures.
Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.330 · Cited by 2,389 · see citing works
Multiple Imputation of Missing Values
Royston, P. (2004) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0400400301 · Cited by 2,340 · cited 2× · see citing works
“However, with more complicated models and longitudinal data, some researchers have also proposed using a larger number of imputations to improve the stability of confidence interval estimation (Royston, 2004), obtain more accountable conclusions with empirical data (Spratt et al.”
Roderick J.A. Little, Donald B. Rubin — Statistical Analysis with Missing Data
Bo., N., Little, R. J. A., & Rubin, D. B. (1988) · Population · doi:10.2307/1533221 · Cited by 2,155 · see citing works
A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures
Akima, H. (1970) · Journal of the ACM · doi:10.1145/321607.321609 · Cited by 2,097 · see citing works
A multivariate technique for multiply imputing missing values using a sequence of regression models
Raghunathan, T. E., Lepkowski, J. M., Hoewyk, J. V., & Solenberger, P. W. (2001) · Survey methodology · Cited by 1,981 · see citing works
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001) · American Political Science Review · doi:10.1017/s0003055401000235 · Cited by 1,834 · see citing works
Advanced Structural Equation Modeling: Issues and Techniques
Rigdon, E. E. (1998) · Applied Psychological Measurement · doi:10.1177/01466216980221008 · Cited by 1,631 · see citing works
Missing Data Techniques for Structural Equation Modeling.
Allison, P. D. (2003) · Journal of Abnormal Psychology · doi:10.1037/0021-843x.112.4.545 · Cited by 1,335 · see citing works
Untitled work
Higgins (2002) · doi:10.1002/(issn)1097-0258 · Cited by 1,248 · see citing works
zoo : S3 Infrastructure for Regular and Irregular Time Series
Zeileis, A., & Grothendieck, G. (2005) · Journal of Statistical Software · doi:10.18637/jss.v014.i06 · Cited by 1,229 · see citing works
Pattern-Mixture Models for Multivariate Incomplete Data
Little, R. J. A. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594302 · Cited by 1,026 · see citing works
Longitudinal Research in the Study of Behavior and Development
Nesselroade, J. R., & Baltes, P. B. (1979) · MPG.PuRe (Max Planck Society) · Cited by 1,004 · see citing works
What to do about missing values in time series cross- section data
Honaker, J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00447.x · Cited by 966 · see citing works
Application of random-effects pattern-mixture models for missing data in longitudinal studies.
Hedeker, D., & Gibbons, R. D. (1997) · Psychological Methods · doi:10.1037/1082-989x.2.1.64 · Cited by 907 · see citing works
On structural equation modeling with data that are not missing completely at random
Muthén, B., Kaplan, D., & Hollis, M. (1987) · Psychometrika · doi:10.1007/bf02294365 · Cited by 883 · see citing works
Multiple Imputation for Missing Data
Allison, P. D. (2000) · Sociological Methods & Research · doi:10.1177/0049124100028003003 · Cited by 797 · see citing works
Multiple-Imputation Inferences with Uncongenial Sources of Input
Meng, X. (1994) · Statistical Science · doi:10.1214/ss/1177010269 · Cited by 761 · see citing works
Multiple Imputation in Practice
Horton, N. J., & Lipsitz, S. R. (2001) · The American Statistician · doi:10.1198/000313001317098266 · Cited by 602 · see citing works
The use of multiple imputation for the analysis of missing data.
Sinharay, S., Stern, H. S., & Russell, D. (2001) · Psychological Methods · doi:10.1037/1082-989x.6.4.317 · Cited by 560 · see citing works
Statistical algorithms for models in state space using SsfPack 2.2
Koopman, S. J., Shephard, N., & Doornik, J. A. (1999) · Econometrics Journal · doi:10.1111/1368-423x.00023 · Cited by 479 · see citing works
Much Ado About Nothing
Horton, N., & Kleinman, K. (2007) · American Statistician · doi:10.1198/000313007x172556 · Cited by 476 · see citing works
Missing-Data Methods for Generalized Linear Models
Ibrahim, J. G., Chen, M., Lipsitz, S. R., & Herring, A. H. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001844 · Cited by 471 · see citing works
Multiple imputation: review of theory, implementation and software
Harel, O., & Zhou, X. (2007) · Statistics in Medicine · doi:10.1002/sim.2787 · Cited by 427 · see citing works
Forecasting, structural time series models and the kalman filter
Harvey, A. C. (2002) · Wilmott · doi:10.1002/wilm.42820020109 · Cited by 398 · see citing works
Strategies for multiple imputation in longitudinal studies.
Spratt, M., Carpenter, J., Sterne, J., Carlin, J., Heron, J., Henderson, J., & Tilling, K. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq137 · Cited by 395 · see citing works
Formalizing Subjective Notions About the Effect of Nonrespondents in Sample Surveys
Rubin, D. B. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480610 · Cited by 381 · see citing works
Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression
Jones, M. P. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476680 · Cited by 344 · see citing works
Applications of multiple imputation in medical studies: from AIDS to NHANES
Barnard, J., & Meng, X.-L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800103 · Cited by 340 · see citing works
Incomplete Data in Generalized Linear Models
Ibrahim, J. G. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10474938 · Cited by 321 · see citing works
Missing Observations in Multivariate Statistics I. Review of the Literature
Afifi, A. A., & Elashoff, R. M. (1966) · Journal of the American Statistical Association · doi:10.1080/01621459.1966.10480891 · Cited by 271 · see citing works
Estimation of Linear Models with Incomplete Data
Allison, P. D. (1987) · Sociological Methodology · doi:10.2307/271029 · Cited by 271 · see citing works
Modeling incomplete longitudinal and cross-sectional data using latent growth structural models.
McArdle, J. J., & Hamagami, F. (1991) · American Psychological Association eBooks · doi:10.1037/10099-017 · Cited by 225 · see citing works
Emotion as a Thermostat: Representing Emotion Regulation Using a Damped Oscillator Model.
Chow, S., Ram, N., Boker, S. M., Fujita, F., & Clore, G. L. (2005) · Emotion · doi:10.1037/1528-3542.5.2.208 · Cited by 218 · see citing works
Self-citation
Modeling incomplete longitudinal and cross-sectional data using latent growth structural models
McArdle, J. F., & Hamagami, F. (1992) · Experimental Aging Research · doi:10.1080/03610739208253917 · Cited by 172 · see citing works
Multiple Imputation: Theory and Method
Zhang, P. (2003) · International Statistical Review · doi:10.1111/j.1751-5823.2003.tb00213.x · Cited by 145 · see citing works
Estimation of Nonstationary EEG With Kalman Smoother Approach: An Application to Event-Related Synchronization (ERS)
Tarvainen, M. P., Hiltunen, J. K., Ranta-aho, P. O., & Karjalainen, P. A. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.821029 · Cited by 133 · see citing works
Progress in Ambulatory Assessment: Computer Assisted Psychological and Psychophysiological Methods in Monitoring and Field Studies
Meldrum, S. J. (2002) · Physiological Measurement · doi:10.1088/0967-3334/23/3/701 · Cited by 111 · see citing works
Investigating within-day and longitudinal effects of maternal stress on children's physical activity, dietary intake, and body composition: Protocol for the MATCH study
Dunton, G. F., Liao, Y., Dzubur, E., Leventhal, A. M., Huh, J., Gruenewald, T. L., Margolin, G., Koprowski, C., Tate, E. B., & Intille, S. S. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.05.007 · Cited by 103 · see citing works
A method of univariate interpolation that has the accuracy of a third-degree polynomial
Akima, H. (1991) · ACM Transactions on Mathematical Software · doi:10.1145/114697.116810 · Cited by 103 · see citing works
Equivalence and Differences Between Structural Equation Modeling and State-Space Modeling Techniques
Chow, S., Ho, M.-H. R., Hamaker, E. L., & Dolan, C. V. (2010) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511003661553 · Cited by 102 · see citing works
Self-citation
Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes
Wood, A. (2004) · International Journal of Epidemiology · doi:10.1093/ije/dyh297 · Cited by 96 · see citing works
Linear Regression Analysis with Missing Observations among the Independent Variables
Glasser, M. (1964) · Journal of the American Statistical Association · doi:10.1080/01621459.1964.10480730 · Cited by 94 · see citing works
A New Algorithm for Spline Smoothing Based on Smoothing a Stochastic Process
Kohn, R., & Ansley, C. F. (1987) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0908004 · Cited by 92 · see citing works
International Journal of Mathematical Analysis
Smith, T. A. (2021) · International Journal of Mathematical Analysis · doi:10.12988/ijma · Cited by 90 · see citing works
Book Reviews
(2002) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711002159653 · Cited by 88 · see citing works
Access to alcohol outlets and harmful alcohol consumption: a multi‐level study in Melbourne, Australia
Kavanagh, A. M., Kelly, M. T., Krnjacki, L. J., Thornton, L. E., Jolley, D., Subramanian, S. V., Turrell, G., & Bentley, R. (2011) · Addiction · doi:10.1111/j.1360-0443.2011.03510.x · Cited by 78 · see citing works
Sequential Analyses of Daily Symptoms in Women With Fibromyalgia Syndrome
Okifuji, A., Bradshaw, D. H., Donaldson, G. W., & Turk, D. C. (2010) · Journal of Pain · doi:10.1016/j.jpain.2010.05.003 · Cited by 67 · see citing works
A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems
Thoemmes, F. J., & Rose, N. (2014) · Multivariate Behavioral Research · doi:10.1080/00273171.2014.931799 · Cited by 58 · see citing works
Review of guidelines and literature for handling missing data in longitudinal clinical trials with a case study
Liu, M., Wei, L., & Zhang, J. (2006) · Pharmaceutical Statistics · doi:10.1002/pst.189 · Cited by 55 · see citing works
Age differences in dynamical emotion-cognition linkages
Chow, S.-M., Hamagani, F., & Nesselroade, J. R. (2007) · Psychology and Aging · doi:10.1037/0882-7974.22.4.765 · Cited by 53 · see citing works
Self-citation
Nonlinear Regime-Switching State-Space (RSSS) Models
Chow, S.-M., & Zhang, G. (2013) · Psychometrika · doi:10.1007/s11336-013-9330-8 · Cited by 48 · see citing works
Self-citation
Dynamic Structure of Emotions Among Individuals with Parkinson's Disease
Chow, S., Nesselroade, J. R., Shifren, K., & McArdle, J. J. (2004) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1207/s15328007sem1104_4 · Cited by 44 · see citing works
Self-citation
Developmental Family Processes and Interparental Conflict: Patterns of Micro-level Influences
Schermerhorn, A. C., Chow, S.-M., & Cummings, E. M. (2010) · Developmental Psychology · doi:10.1037/a0019662 · Cited by 39 · see citing works
Self-citation
Multivariate Adaptive Splines for Analysis of Longitudinal Data
Zhang, H. (1997) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1997.10474728 · Cited by 38 · see citing works
iVAR: A program for imputing missing data in multivariate time series using vector autoregressive models
Liu, S., & Molenaar, P. C. M. (2014) · Behavior Research Methods · doi:10.3758/s13428-014-0444-4 · Cited by 35 · see citing works
Model selection of generalized estimating equations with multiply imputed longitudinal data
Shen, C., & Chen, Y. (2013) · Biometrical Journal · doi:10.1002/bimj.201200236 · Cited by 33 · see citing works
Number of imputations needed to stabilize estimated treatment difference in longitudinal data analysis
Lu, K. (2017) · Statistical Methods in Medical Research · doi:10.1177/0962280214554439 · Cited by 32 · see citing works
Current evolutionary adaptiveness of psychiatric disorders: Fertility rates, parent−child relationship quality, and psychiatric disorders across the lifespan.
Jacobson, N. C. (2016) · Journal of Abnormal Psychology · doi:10.1037/abn0000185 · Cited by 29 · see citing works
Self-citation
A multiple imputation strategy for incomplete longitudinal data
Landrum, M. B., & Becker, M. P. (2001) · Statistics in Medicine · doi:10.1002/sim.740 · Cited by 26 · see citing works
Including auxiliary item information in longitudinal data analyses improved handling missing questionnaire outcome data
Eekhout, I. E., Enders, C. K., Twisk, J. W. R., de Boer, M. R., de Vet, H. C. W., & Heymans, M. W. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2015.01.012 · Cited by 12 · see citing works
KFAS: Kalman Filter and Smoother for Exponential Family State Space Models
Helske, J. (2009) · doi:10.32614/cran.package.kfas · Cited by 12 · see citing works
Continuous‐time modelling of irregularly spaced panel data using a cubic spline model
Chow, S., & Zhang, G. (2007) · Statistica Neerlandica · doi:10.1111/j.1467-9574.2007.00379.x · Cited by 9 · see citing works
Self-citation
Anxious Moods as a Risk Factor for Depressed Moods: an Ecological Momentary Assessment of Those with Clinical Anxiety and Depression
Jacobson, N. C. (2015) · Cited by 3 · see citing works
Self-citation
Multiple imputation of missing blood pressure covariates in survival analysis
van Buuren, S., Boshuizen, H. C., & Knook, D. L. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19990330)18:6

Flagged first, then by citations

Research works printed but not linked: 4

Allison PD (2002). Missing data: Quantitative applications in the social sciences. British Journal of Mathematical and Statistical Psychology, 55(1), 193– 196.
Chow S-M, Ram N, Boker S, Fujita F, Clore G, & Nesselroade J (2005). Capturing weekly fluctuation in emotion using a latent differential structural approach. Emotion, 5 (2), 208– 225.15982086
Little RJ, & Rubin DB (2014). Statistical analysis with missing data Hoboken, NJ: John Wiley & Sons.
Wahba G (1990). Spline models for observational data, volume 59 of CBMS-NSF Regional Conference Series in Applied Mathematics Philadelphia: SIAM.

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Considerations to address missing data when deriving clinical trial endpoints from digital health technologies
Di, J., Demanuele, C., Kettermann, A. E., Karahanoğlu, F. I., Cappelleri, J. C., Potter, A., Bury, D. P., Cedarbaum, J. M., & Byrom, B. (2021) · Contemporary Clinical Trials · doi:10.1016/j.cct.2021.106661 · Cited by 52 · see citing works
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Integration of discrete and global structures of affect across three large samples: Specific emotions within-persons and global affect between-persons.
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Ohlsen, S., Anderson, D., Bastounis, A., Bhui, K. S., Booth, A., Burton, C. D., Byng, R., Chico, T. J., Couperthwaite, L. M. Z., Crimlisk, H., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-117088

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Huque, M. H., Moreno‐Betancur, M., Quartagno, M., Simpson, J. A., Carlin, J. B., & Lee, K. J. (2020) · Biometrical Journal · doi:10.1002/bimj.201900051 · Cited by 61 · see citing works
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Lee, K. J., Roberts, G., Doyle, L. W., Anderson, P. J., & Carlin, J. B. (2016) · International Journal of Social Research Methodology · doi:10.1080/13645579.2015.1126486 · Cited by 82 · see citing works
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Huque, M. H., Carlin, J. B., Simpson, J. A., & Lee, K. J. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0615-6 · Cited by 321 · see citing works
Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal DesignsPreprint
Lustig, M., Lüdtke, O., Robitzsch, A., & Grund, S. (2026) · doi:10.31234/osf.io/8hb9u_v2

Authors: Ji, L., Chow, S.-M., Schermerhorn, A. C., Jacobson, N. C., & Cummings, E. M.
Affiliations: Pennsylvania State University (US)
University of Vermont (US)
University of Notre Dame (US)
Corresponding author: Ji, Linying
ORCID: Ji, Linying
Chow, Sy-Miin
Schermerhorn, Alice C.
Jacobson, Nicholas C.
Cummings, E. Mark
Venue: Structural Equation Modeling: A Multidisciplinary Journal
Publisher: Informa UK Limited
Published: 2018-02-08
Volume: 25
Issue: 5
Pages: 715-736
ISSN: 1070-5511 (ISSN-L)
1532-8007
DOI: 10.1080/10705511.2017.1417046
PMC: PMC6625802
OpenAlex: W2793011341
Cited by: 74
References: 76
Citations by year: 2026: 16 · 2025: 12 · 2024: 12 · 2023: 10 · 2022: 10 · 2021: 6 · 2020: 5 · 2019: 2 · 2018: 1
FWCI: 2.90
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Center for Advancing Translational Sciences (UL TR000127)
National Institutes of Health (R01GM105004)
National Science Foundation (BCS-0826844)
Penn State Quantitative Social Sciences Initiative.
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Keywords: missing data, intensive longitudinal data, multiple imputation, full information maximum likelihood, multivariate time series models, missing data mechanisms, model misspecification

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Handling Missing Data in the Modeling of Intensive Longitudinal Data

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