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Prevention Science

Erschienen in:

27.02.2017

Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis

verfasst von: Juned Siddique, Peter J. de Chavez, George Howe, Gracelyn Cruden, C. Hendricks Brown

Erschienen in: Prevention Science | Sonderheft 1/2018

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Abstract

Individual participant data (IPD) meta-analysis is a meta-analysis in which the individual-level data for each study are obtained and used for synthesis. A common challenge in IPD meta-analysis is when variables of interest are measured differently in different studies. The term harmonization has been coined to describe the procedure of placing variables on the same scale in order to permit pooling of data from a large number of studies. Using data from an IPD meta-analysis of 19 adolescent depression trials, we describe a multiple imputation approach for harmonizing 10 depression measures across the 19 trials by treating those depression measures that were not used in a study as missing data. We then apply diagnostics to address the fit of our imputation model. Even after reducing the scale of our application, we were still unable to produce accurate imputations of the missing values. We describe those features of the data that made it difficult to harmonize the depression measures and provide some guidelines for using multiple imputation for harmonization in IPD meta-analysis.

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Achenbach, T.M. (1991). Manual for the Child Behavior Checklist/4-18 and 1991 profile [Computer software manual]. Burlington VT: University of Vermont, Department of Psychiatry.

Bauer, D.J., & Hussong, A.M. (2009). Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models. Psychological Methods, 14, 101–125.CrossRefPubMedPubMedCentral

Brown, C.H., Brincks, A., Huang, S., Perrino, T., Cruden, G., Pantin, H., & Sandler, I. (2016). Two-year in impact of prevention programs on adolescent depression: An integrative data analysis approach. Prevention Science. doi:10.1007/s11121-016-0737-1.

Clarke, G.N., Lewinsohn, P.M., Hops, H., & Seeley, J.R. (1992). A self-and parent-report measure of adolescent depression: The Child Behavior Checklist Depression scale (CBCL-D). Behavioral Assessment, 14.

Cowles, M.K., & Carlin, B.P. (1996). Markov chain monte carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91, 883–904.CrossRef

Curran, P.J. (2009). The seemingly quixotic pursuit of a cumulative psychological science: Introduction to the special issue. Psychological Methods, 14, 77–80.CrossRefPubMedPubMedCentral

Curran, P.J., & Hussong, A.M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological Methods, 14, 81–100.CrossRefPubMedPubMedCentral

Curran, P.J., Hussong, A.M., Cai, L., Huang, W., Chassin, L., Sher, K. J., & Zucker, R.A. (2008). Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis. Developmental Psychology, 44, 365–380.CrossRefPubMedPubMedCentral

Dagne, G.A., Brown, C.H., Howe, G., Kellam, S.G., & Liu, L. (2016). Testing moderation in network meta-analysis with individual participant data. Statistics in Medicine, 34, 2485–2502.CrossRef

Eaton, W.W., Smith, C., Ybarra, M., Muntaner, C., & Tien, A. (2004). Center for epidemiologic studies depression scale: review and revision (CESD and CESD-r). In Maruish, M. E. (Ed.) The Use of Psychological Testing for Treatment Planning and Outcomes Assessment: Volume 3: Instruments for Adults. 3rd edn (pp. 363–377). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Ebesutani, C., Bernstein, A., Martinez, J.I., Chorpita, B.F., & Weisz, J.R. (2011). The youth self report: Applicability and validity across younger and older youths. Journal of Clinical Child and Adolescent Psychology, 40, 338–346.CrossRefPubMed

Gelman, A., Carlin, J.B., Stern, H.S., & Rubin, D.B. (2004). Bayesian data analysis, 2nd edn. Boca Raton, FL: Chapman and Hall/CRC press.

Gelman, A., King, G., & Liu, C. (1998). Not asked and not answered: Multiple imputation for multiple surveys. Journal of the American Statistical Association, 93, 846–857.

Gelman, A., Meng, X.-L., & Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica, 6, 733–760.

Gelman, A., & Rubin, D.B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 457–472.

Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bernado, J. M., Berger, J. O. , Dawid, A. P., & Smith, A. F. (Eds.) Bayesian statistics, (Vol. 4 pp. 169–193). Oxford: Clarendon Press.

Griffith, L.E., Van Den Heuvel, E., Fortier, I., Sohel, N., Hofer, S.M., Payette, H., & et al. (2015). Statistical approaches to harmonize data on cognitive measures in systematic reviews are rarely reported. Journal of Clinical Epidemiology, 68, 154–162.

Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62.CrossRefPubMedPubMedCentral

Harel, O., & Zhou, X.-H. (2007). Multiple imputation: Review of theory, implementation and software. Statistics in Medicine, 26, 3057–3077.CrossRefPubMed

He, Y., & Zaslavsky, A.M. (2012). Diagnosing imputation models by applying target analyses to posterior replicates of completed data. Statistics in Medicine, 31, 1–18.CrossRefPubMed

Helsel, W.J., & Matson, J.L. (1984). The assessment of depression in children: The internal structure of the Child Depression Inventory CDI. Behaviour Research and Therapy, 22, 289–298.CrossRefPubMed

Howe, G.W., Dagne, G., Brown, C.H., Brincks, A., & Beardslee, W. (2017). Evaluating construct equivalence and harmonizing measurement of adolescent depression when synthesizing results across multiple studies. In preparation.

Hussong, A. M., Curran, P. J., & Bauer, D. J. (2013). Integrative data analysis in clinical psychology research. Annual Review of Clinical Psychology, 9, 61–89.CrossRefPubMedPubMedCentral

Kline, D., Andridge, R., & Kaizar, E. (2015). Comparing multiple imputation methods for systematically missing subject-level data. Research Synthesis Methods, 1–13.

Kovacs, M. (1984). The Children’s Depression Inventory CDI. Psychopharmacology Bulletin, 21, 995–998.

Mayes, T.L., Bernstein, I.H., Haley, C.L., Kennard, B.D., & Emslie, G.J. (2010). Psychometric properties of the Children’s Depression Rating Scale-revised in adolescents. Journal of Child and Adolescent Psychopharmacology, 20, 513–516.CrossRefPubMedPubMedCentral

National Institutes of Health (2003). Final NIH statement on sharing research data. (http://grants.nih.gov/grants/guide/notice-files/NOT-OD-03-032.html [Accessed 3-March-2014]).

National Science Foundation (2011). Dissemination and sharing of research results. (http://www.nsf.gov/pubs/policydocs/pappguide/nsf11001/aag_6.jsp#VID4 [Accessed 3-March-2014]).

Perrino, T., Howe, G., Sperling, A., Beardslee, W., Sandler, I., Shern, D., & Brown, C.H. (2013). Advancing science through collaborative data sharing and synthesis. Perspectives on Psychological Science, 8, 433–444.CrossRefPubMed

Poznanski, E.O., Freeman, L.N., & Mokros, H.B. (1985). Children’s depression rating scale-revised (September 1984). Psychopharmacology Bulletin, 21, 979–989.

Quay, H.C., & Peterson, D.R. (1996). Revised Behavior Problem Checklist. Odessa, FL: Psychological Assessment Resources.

R Core Team (2012). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project.org/ (ISBN 3-90005 1-07-0).

Radloff, L.S. (1977). The CES-D scale a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.CrossRef

Radloff, L.S. (1991). The use of the Center for Epidemiologic Studies Depression Scale in adolescents and young adults. Journal of Youth and Adolescence, 20, 149–166.CrossRefPubMed

Rässler, S (2003). A non-iterative Bayesian approach to statistical matching. Statistica Neerlandica, 57, 58–74.CrossRef

Resche-Rigon, M., White, I.R., Bartlett, J.W., Peters, S.A., & Thompson, S.G. (2013). Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Statistics in Medicine, 32, 4890–4905.CrossRefPubMed

Riley, R.D., Lambert, P.C., & Abo-Zaid, G. (2010). Meta-analysis of individual participant data: Rationale, conduct, and reporting. BMJ: British Medical Journal, 521–525.

Rodwell, L., Lee, K.J., Romaniuk, H., & Carlin, J.B. (2014). Comparison of methods for imputing limited-range variables: A simulation study. BMC Medical Research Methodology, 14, 57.CrossRefPubMedPubMedCentral

Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: John Wiley and Sons.CrossRef

Schafer, J.L., & Yucel, R.M. (2002). Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics, 11, 437– 457.CrossRef

Schifeling, T.A., & Reiter, J.P (2015). Incorporating marginal prior information in latent class models.

Siddique, J., Reiter, J.P., Brincks, A., Gibbons, R.D., Crespi, C.M., & Brown, C.H. (2015). Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis. Statistics in Medicine, 34, 3399– 3414.CrossRefPubMedPubMedCentral

Zhao, J.H., & Schafer, J.L. (2013). Pan: Multiple imputation for multivariate panel or clustered data [Computer software manual]. (R package version 0.9).

Titel: Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis
verfasst von: Juned Siddique
Peter J. de Chavez
George Howe
Gracelyn Cruden
C. Hendricks Brown
Publikationsdatum: 27.02.2017
Verlag: Springer US
Erschienen in: Prevention Science / Ausgabe Sonderheft 1/2018
Print ISSN: 1389-4986
Elektronische ISSN: 1573-6695
DOI: https://doi.org/10.1007/s11121-017-0760-x

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