Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Some Conceptual and Statistical Issues in Analysis of Longitudinal Psychiatric Data: Application to the NIMH Treatment of Depression Collaborative Research Program Dataset

Some Conceptual and Statistical Issues in Analysis of Longitudinal Psychiatric Data: Application... Abstract L studies have a prominent role in psychiatric research; however, statistical methods for analyzing these data are rarely commensurate with the effort involved in their acquisition. Frequently the majority of data are discarded and a simple end-point analysis is performed. In other cases, so called repeated-measures analysis of variance procedures are used with little regard to their restrictive and often unrealistic assumptions and the effect of missing data on the statistical properties of their estimates. We explored the unique features of longitudinal psychiatric data from both statistical and conceptual perspectives. We used a family of statistical models termed random regression models that provide a more realistic approach to analysis of longitudinal psychiatric data. Random regression models provide solutions to commonly observed problems of missing data, serial correlation, time-varying covariates, and irregular measurement occasions, and they accommodate systematic person-specific deviations from the average time trend. Properties of these models were compared with traditional approaches at a conceptual level. The approach was then illustrated in a new analysis of the National Institute of Mental Health Treatment of Depression Collaborative Research Program dataset, which investigated two forms of psychotherapy, pharmacotherapy with clinical management, and a placebo with clinical management control. Results indicated that both person-specific effects and serial correlation play major roles in the longitudinal psychiatric response process. Ignoring either of these effects produces misleading estimates of uncertainty that form the basis of statistical tests of hypotheses. References 1. Bock RD. Multivariate Statistical Methods in Behavioral Research. New York, NY: McGraw-Hill International Book Co; 1975. 2. Ekstrom D, Quade D, Golden R. Statistical analysis of repeated measures in psychiatry research . Arch Gen Psychiatry . 1990;47:770-774.Crossref 3. Elkin I, Shea MT, Watkins JT, Imber SD, Sotsky SM, Collins JS, Glass DR, Pilkonis PA, Leber WR, Docherty JP, Fiester SJ, Parloff MB. National Institute of Mental Health Treatment of Depression Collaborative Research Program:general effectiveness of treatments . Arch Gen Psychiatry . 1989;46:971-982.Crossref 4. Kraemer H. Coping strategies in psychiatric clinical research . J Consult Clin Psychol . 1981;49:309-319.Crossref 5. Cook NR, Ware JH. Design and analysis methods for longitudinal research . Annu Rev Public Health . 1983;4:1-24.Crossref 6. Lavori P. ANOVA, MANOVA, my black hen . Arch Gen Psychiatry . 1990;47:775-778.Crossref 7. Fleiss JL. The Design and Analysis of Clinical Experiments . New York, NY: John Wiley & Sons Inc; 1986. 8. Cook TD, Campbell DT. Quasi-Experimentation: Design and Analysis Issues for Field Settings . Boston, Mass: Houghton Mifflin Co; 1979. 9. Brogan DR, Kutner MH. Comparative analyses of pretest-posttest research design . Am Stat . 1980;34:229-232. 10. Winer BJ. Statistical Principles in Experimental Design . 2nd ed. New York, NY: McGraw-Hill International Book Co; 1971. 11. Box GEP. Some theorems on quadratic forms applied in the study of analysis of variance problems, II: effects of inequality of variance and correlation between errors in the two-way classification . Ann Math Stat . 1954;25:484-498.Crossref 12. Geisser S, Greenhouse SW. An extension of Box's results on the use of the F distribution in multivariate analysis . Ann Math Stat . 1958;29:885-891.Crossref 13. Greenhouse SW, Geisser S. On methods in the analysis of profile data . Psychometrika . 1959;24:95-112.Crossref 14. Huynh H, Feldt LS. Estimation of the Box correction for degrees of freedom from sample data in randomized block and split-plot designs . J Educ Stat . 1976;1:69-82.Crossref 15. Wallenstein S, Fleiss JL. Repeated measurements analysis of variance when the correlations have a certain pattern . Psychometrika . 1979;44:229-233.Crossref 16. Hearne EM, Clark GM, Hatch JP. A test for serial correlation in univariate repeated-measures analysis . Biometrics . 1983;39:237-243.Crossref 17. Collier RO, Baker FB, Mandeville GK, Hayes TF. Estimates of test size for several test procedures based on conventional variance ratios in the repeated measures design . Psychometrika . 1967;32:339-353.Crossref 18. Hand DJ, Taylor CC. Multivariate Analysis of Variance and Repeated Measures . New York, NY: Chapman and Hall; 1987. 19. Bock RD. Univariate and multivariate analysis of time-structured data . In: Nesselroade JR, Baltes PB, eds. Longitudinal Research in the Study of Behavior and Development . Orlando, Fla: Academic Press; 1979:199-231. 20. Dempster AP, Rubin DB, Tsutakawa RK. Estimation in covariance components models . J Am Stat Assoc . 1981;76:341-353.Crossref 21. Laird NM, Ware JH. Random effects models for longitudinal data . Biometrics . 1982;38:963-974.Crossref 22. Ware J. Linear models for the analysis of longitudinal studies . Am Stat . 1985;39:95-101.Crossref 23. Maritz TS. Empirical Bayes Methods . London, England: Methuen; 1970. 24. Casella G. An introduction to empirical Bayes data analysis . Am Stat . 1985;39:83-87.Crossref 25. Bryk AS, Raudenbush SW. Application of hierarchical linear models to assessing change . Psychol Bull . 1987;101:147-158.Crossref 26. Goldstein H. Multilevel Models in Educational and Social Research . London, England: Oxford University Press; 1987. 27. Willett JB, Ayoub CC, Robinson D. Using growth modeling to examine systematic differences ingrowth: an example of change in the functioning of families at risk of maladaptive parenting, child abuse, or neglect . J Consult Clin Psychol . 1991;59:38-47.Crossref 28. Bock RD. Within-subject experimentation in psychiatric research . In: Gibbons RD, Dysken MW, eds. Statistical and Methodological Advances in Psychiatric Research . New York, NY: Spectrum Books; 1983:59-90. 29. Bock RD. The discrete Bayesian . In: Wainer H, Messick S, eds. Principles of Modern Psychological Measurement . Hillsdale, NJ: Lawrence J Earlbaum Assoc; 1983:103-115. 30. Bock RD. Measurement of human variation: a two-stage model . In: Bock RD, ed. Multilevel Analysis of Educational Data . Orlando, Fla: Academic Press; 1989:319-342. 31. Jennrich RI, Schluchter MD. Unbalanced repeatedmeasures models with structured covariance matrices . Biometrics . 1986;42:805-820.Crossref 32. Gibbons RD, Hedeker D, Waternaux CM, Davis JM. Random regression models: a comprehensive approach to the analysis of longitudinal psychiatric data . Psychopharmacol Bull . 1988;24:438-443. 33. Hedeker D, Gibbons RD, Waternaux CM, Davis JM. Investigating drug plasma levels and clinical response using random regression models . Psychopharmacol Bull . 1989;25:227-231. 34. Hedeker D. Random Regression Models With AutocorrelatedErrors . Chicago, III: University of Chicago; 1989. Doctoral dissertation. 35. Chi EM, Reinsel GC. Models of longitudinal data with random effects and AR(1) errors . J Am Stat Assoc . 1989;84:452-459.Crossref 36. Laird NM. Missing data in longitudinal studies . Stat Med . 1988;7:305-315.Crossref 37. Little R, Rubin D. Statistical Analysis With Missing Data . New York, NY: John Wiley & Sons Inc; 1987. 38. Waternaux CM, Laird NM, Ware JH. Methods for analysis of longitudinal data: blood lead concentrations and cognitive development . J Am Stat Assoc . 1989;84:33-41.Crossref 39. Gibbons RD, Book RD. Trend in correlated proportions . Psychometrika . 1987;52:113-124.Crossref 40. Stiratelli R, Laird NM, Ware JH. Random-effects models for serial observations with binary response . Biometrics . 1984;40:961-971.Crossref 41. Schluchter MD. 5V: unbalanced repeated measures models with structured covariance matrices . In: Dixon WJ, chief ed. BMDP Statistical Software Manual . Berkeley, Calif: University of California Press; 1988;2:1081-1114. 42. Prosser R, Rasbash J, Goldstein H. ML3 Software for Three Level Analysis, Users' Guide for v .2. London, England: Institute of Education, University of London; 1991. 43. Bryk AS, Raudenbush SW, Seltzer M, Congdon RJ. An Introduction to HLM: Computer Program and Users' Guide. Chicago, III: Scientific Software Inc; 1989. 44. Longford NT. VARCL—interactive software for variance component analysis . Professsional Stat . 1986;74:817-827. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of General Psychiatry American Medical Association

Some Conceptual and Statistical Issues in Analysis of Longitudinal Psychiatric Data: Application to the NIMH Treatment of Depression Collaborative Research Program Dataset

Loading next page...
 
/lp/american-medical-association/some-conceptual-and-statistical-issues-in-analysis-of-longitudinal-ZU8PC8rUnY
Publisher
American Medical Association
Copyright
Copyright © 1993 American Medical Association. All Rights Reserved.
ISSN
0003-990X
eISSN
1598-3636
DOI
10.1001/archpsyc.1993.01820210073009
Publisher site
See Article on Publisher Site

Abstract

Abstract L studies have a prominent role in psychiatric research; however, statistical methods for analyzing these data are rarely commensurate with the effort involved in their acquisition. Frequently the majority of data are discarded and a simple end-point analysis is performed. In other cases, so called repeated-measures analysis of variance procedures are used with little regard to their restrictive and often unrealistic assumptions and the effect of missing data on the statistical properties of their estimates. We explored the unique features of longitudinal psychiatric data from both statistical and conceptual perspectives. We used a family of statistical models termed random regression models that provide a more realistic approach to analysis of longitudinal psychiatric data. Random regression models provide solutions to commonly observed problems of missing data, serial correlation, time-varying covariates, and irregular measurement occasions, and they accommodate systematic person-specific deviations from the average time trend. Properties of these models were compared with traditional approaches at a conceptual level. The approach was then illustrated in a new analysis of the National Institute of Mental Health Treatment of Depression Collaborative Research Program dataset, which investigated two forms of psychotherapy, pharmacotherapy with clinical management, and a placebo with clinical management control. Results indicated that both person-specific effects and serial correlation play major roles in the longitudinal psychiatric response process. Ignoring either of these effects produces misleading estimates of uncertainty that form the basis of statistical tests of hypotheses. References 1. Bock RD. Multivariate Statistical Methods in Behavioral Research. New York, NY: McGraw-Hill International Book Co; 1975. 2. Ekstrom D, Quade D, Golden R. Statistical analysis of repeated measures in psychiatry research . Arch Gen Psychiatry . 1990;47:770-774.Crossref 3. Elkin I, Shea MT, Watkins JT, Imber SD, Sotsky SM, Collins JS, Glass DR, Pilkonis PA, Leber WR, Docherty JP, Fiester SJ, Parloff MB. National Institute of Mental Health Treatment of Depression Collaborative Research Program:general effectiveness of treatments . Arch Gen Psychiatry . 1989;46:971-982.Crossref 4. Kraemer H. Coping strategies in psychiatric clinical research . J Consult Clin Psychol . 1981;49:309-319.Crossref 5. Cook NR, Ware JH. Design and analysis methods for longitudinal research . Annu Rev Public Health . 1983;4:1-24.Crossref 6. Lavori P. ANOVA, MANOVA, my black hen . Arch Gen Psychiatry . 1990;47:775-778.Crossref 7. Fleiss JL. The Design and Analysis of Clinical Experiments . New York, NY: John Wiley & Sons Inc; 1986. 8. Cook TD, Campbell DT. Quasi-Experimentation: Design and Analysis Issues for Field Settings . Boston, Mass: Houghton Mifflin Co; 1979. 9. Brogan DR, Kutner MH. Comparative analyses of pretest-posttest research design . Am Stat . 1980;34:229-232. 10. Winer BJ. Statistical Principles in Experimental Design . 2nd ed. New York, NY: McGraw-Hill International Book Co; 1971. 11. Box GEP. Some theorems on quadratic forms applied in the study of analysis of variance problems, II: effects of inequality of variance and correlation between errors in the two-way classification . Ann Math Stat . 1954;25:484-498.Crossref 12. Geisser S, Greenhouse SW. An extension of Box's results on the use of the F distribution in multivariate analysis . Ann Math Stat . 1958;29:885-891.Crossref 13. Greenhouse SW, Geisser S. On methods in the analysis of profile data . Psychometrika . 1959;24:95-112.Crossref 14. Huynh H, Feldt LS. Estimation of the Box correction for degrees of freedom from sample data in randomized block and split-plot designs . J Educ Stat . 1976;1:69-82.Crossref 15. Wallenstein S, Fleiss JL. Repeated measurements analysis of variance when the correlations have a certain pattern . Psychometrika . 1979;44:229-233.Crossref 16. Hearne EM, Clark GM, Hatch JP. A test for serial correlation in univariate repeated-measures analysis . Biometrics . 1983;39:237-243.Crossref 17. Collier RO, Baker FB, Mandeville GK, Hayes TF. Estimates of test size for several test procedures based on conventional variance ratios in the repeated measures design . Psychometrika . 1967;32:339-353.Crossref 18. Hand DJ, Taylor CC. Multivariate Analysis of Variance and Repeated Measures . New York, NY: Chapman and Hall; 1987. 19. Bock RD. Univariate and multivariate analysis of time-structured data . In: Nesselroade JR, Baltes PB, eds. Longitudinal Research in the Study of Behavior and Development . Orlando, Fla: Academic Press; 1979:199-231. 20. Dempster AP, Rubin DB, Tsutakawa RK. Estimation in covariance components models . J Am Stat Assoc . 1981;76:341-353.Crossref 21. Laird NM, Ware JH. Random effects models for longitudinal data . Biometrics . 1982;38:963-974.Crossref 22. Ware J. Linear models for the analysis of longitudinal studies . Am Stat . 1985;39:95-101.Crossref 23. Maritz TS. Empirical Bayes Methods . London, England: Methuen; 1970. 24. Casella G. An introduction to empirical Bayes data analysis . Am Stat . 1985;39:83-87.Crossref 25. Bryk AS, Raudenbush SW. Application of hierarchical linear models to assessing change . Psychol Bull . 1987;101:147-158.Crossref 26. Goldstein H. Multilevel Models in Educational and Social Research . London, England: Oxford University Press; 1987. 27. Willett JB, Ayoub CC, Robinson D. Using growth modeling to examine systematic differences ingrowth: an example of change in the functioning of families at risk of maladaptive parenting, child abuse, or neglect . J Consult Clin Psychol . 1991;59:38-47.Crossref 28. Bock RD. Within-subject experimentation in psychiatric research . In: Gibbons RD, Dysken MW, eds. Statistical and Methodological Advances in Psychiatric Research . New York, NY: Spectrum Books; 1983:59-90. 29. Bock RD. The discrete Bayesian . In: Wainer H, Messick S, eds. Principles of Modern Psychological Measurement . Hillsdale, NJ: Lawrence J Earlbaum Assoc; 1983:103-115. 30. Bock RD. Measurement of human variation: a two-stage model . In: Bock RD, ed. Multilevel Analysis of Educational Data . Orlando, Fla: Academic Press; 1989:319-342. 31. Jennrich RI, Schluchter MD. Unbalanced repeatedmeasures models with structured covariance matrices . Biometrics . 1986;42:805-820.Crossref 32. Gibbons RD, Hedeker D, Waternaux CM, Davis JM. Random regression models: a comprehensive approach to the analysis of longitudinal psychiatric data . Psychopharmacol Bull . 1988;24:438-443. 33. Hedeker D, Gibbons RD, Waternaux CM, Davis JM. Investigating drug plasma levels and clinical response using random regression models . Psychopharmacol Bull . 1989;25:227-231. 34. Hedeker D. Random Regression Models With AutocorrelatedErrors . Chicago, III: University of Chicago; 1989. Doctoral dissertation. 35. Chi EM, Reinsel GC. Models of longitudinal data with random effects and AR(1) errors . J Am Stat Assoc . 1989;84:452-459.Crossref 36. Laird NM. Missing data in longitudinal studies . Stat Med . 1988;7:305-315.Crossref 37. Little R, Rubin D. Statistical Analysis With Missing Data . New York, NY: John Wiley & Sons Inc; 1987. 38. Waternaux CM, Laird NM, Ware JH. Methods for analysis of longitudinal data: blood lead concentrations and cognitive development . J Am Stat Assoc . 1989;84:33-41.Crossref 39. Gibbons RD, Book RD. Trend in correlated proportions . Psychometrika . 1987;52:113-124.Crossref 40. Stiratelli R, Laird NM, Ware JH. Random-effects models for serial observations with binary response . Biometrics . 1984;40:961-971.Crossref 41. Schluchter MD. 5V: unbalanced repeated measures models with structured covariance matrices . In: Dixon WJ, chief ed. BMDP Statistical Software Manual . Berkeley, Calif: University of California Press; 1988;2:1081-1114. 42. Prosser R, Rasbash J, Goldstein H. ML3 Software for Three Level Analysis, Users' Guide for v .2. London, England: Institute of Education, University of London; 1991. 43. Bryk AS, Raudenbush SW, Seltzer M, Congdon RJ. An Introduction to HLM: Computer Program and Users' Guide. Chicago, III: Scientific Software Inc; 1989. 44. Longford NT. VARCL—interactive software for variance component analysis . Professsional Stat . 1986;74:817-827.

Journal

Archives of General PsychiatryAmerican Medical Association

Published: Sep 1, 1993

References