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

Prevention Science

Volumn 19, Issue , 2018, Pages 95-108

  Siddique, Juned ^a   de Chavez, Peter J ^a   Howe, George ^b   Cruden, Gracelyn ^a   Brown, C Hendricks ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

^b GEORGE WASHINGTON UNIVERSITY   (United States)

Author keywords

Data synthesis; Individual participant data meta analysis; Multiple imputation; Posterior predictive checking

Indexed keywords

DATA SYNTHESIS; HUMAN; META ANALYSIS; MODEL; PRACTICE GUIDELINE; ADOLESCENT; CHILD; DATA ANALYSIS; DEPRESSION; FEMALE; MALE; META ANALYSIS (TOPIC); RESEARCH SUBJECT; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA;

ADOLESCENT; BIAS; BIG DATA; CHILD; DATA ANALYSIS; DEPRESSION; FEMALE; HUMANS; MALE; META-ANALYSIS AS TOPIC; RESEARCH SUBJECTS;

EID: 85014016627     PISSN: 13894986     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11121-017-0760-x     Document Type: Article

References (43)

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. Curran, P.J. (2009). The seemingly quixotic pursuit of a cumulative psychological science: Introduction to the special issue. Psychological Methods, 14, 77–80.
7. Curran, P.J., & Hussong, A.M. (2009). Integrative data analysis: The simultaneous analysis of multiple data sets. Psychological Methods, 14, 81–100.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. Gelman, A., Meng, X.-L., & Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica, 6, 733–760.
15. Gelman, A., & Rubin, D.B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 457–472.
16. 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.
17. 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.
18. Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62.
19. Harel, O., & Zhou, X.-H. (2007). Multiple imputation: Review of theory, implementation and software. Statistics in Medicine, 26, 3057–3077.
20. 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.
21. 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.
22. 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.
23. 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.
24. Kline, D., Andridge, R., & Kaizar, E. (2015). Comparing multiple imputation methods for systematically missing subject-level data. Research Synthesis Methods, 1–13.
25. Kovacs, M. (1984). The Children’s Depression Inventory CDI. Psychopharmacology Bulletin, 21, 995–998.
26. 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.
27. 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]).
28. 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]).
29. 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.
30. Poznanski, E.O., Freeman, L.N., & Mokros, H.B. (1985). Children’s depression rating scale-revised (September 1984). Psychopharmacology Bulletin, 21, 979–989.
31. Quay, H.C., & Peterson, D.R. (1996). Revised Behavior Problem Checklist. Odessa, FL: Psychological Assessment Resources.
32. 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).
33. 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.
34. 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.
35. Rässler, S (2003). A non-iterative Bayesian approach to statistical matching. Statistica Neerlandica, 57, 58–74.
36. 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.
37. 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.
38. 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.
39. Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: John Wiley and Sons.
40. 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.
41. Schifeling, T.A., & Reiter, J.P (2015). Incorporating marginal prior information in latent class models.
42. 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.
43. Zhao, J.H., & Schafer, J.L. (2013). Pan: Multiple imputation for multivariate panel or clustered data [Computer software manual]. (R package version 0.9).