Time and change: Using survival analysis in clinical assessment and treatment evaluation

Psychological Assessment

Volumn 10, Issue 4, 1998, Pages 360-378

  Luke, Douglas A ^a,b   Homan, Sharon M ^a  

^a SAINT LOUIS UNIVERSITY   (United States)

^b SAINT LOUIS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ARTICLE; CLINICAL MEDICINE; CURVE FITTING; EVALUATION; HUMAN; LIFE TABLE; NONBIOLOGICAL MODEL; PREDICTION; PROBABILITY; STATISTICAL ANALYSIS; SUBSTANCE ABUSE; SURVIVAL;

EID: 0032421251     PISSN: 10403590     EISSN: None     Source Type: Journal    
DOI: 10.1037/1040-3590.10.4.360     Document Type: Article

References (35)

1. Allison, P. D. (1995). Survival analysis using the SAS System: A practical guide. Cary, NC: SAS Institute.
2. Blossfeld, H., Hamerle, A., & Mayer, K. U. (1989). Event history analysis. Hillsdale, NJ: Erlbaum.
3. Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147-158.
4. Bull, K., & Spiegelhalter, D. J. (1997). Tutorial in biostatistics: Survival analysis in observational studies. Studies in Medicine, 16, 1041-1074.
5. Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304-1312.
6. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B, 34, 187-202.
7. Cronbach, L. J., & Furby, L. (1970). How we should measure "Change" - or should we? Psychological Bulletin, 74, 68-80.
8. Derogatis, L. R., et al. (1974). The Hopkins Symptom Checklist (HSCL): A self-report symptom inventory. Behavioral Science, 15, 1-15.
9. Donovan, D. M. (1996). Theoretical perspectives on relapse: Assessment issues and domains in the prediction of relapse. Addiction, 91, S29-S36.
10. Gardner, W. (1990). Analyzing sequential categorical data: Individual variation in Markov chains. Psychometrika, 55, 263-275.
11. Goodall, C. (1990). A survey of smoothing techniques. In J. Fox & J. S. Long (Eds.), Modern methods of data analysis (pp. 126-176). Newbury Park, CA: Sage.
12. Greenhouse, J. B., Stangl, D., & Bromberg, J. (1989). An introduction to survival analysis: Statistical methods for analysis of clinical trial data. Journal of Consulting and Clinical Psychology, 57, 536-544.
13. Haccou, P., & Meelis, E. (1992). Statistical analysis of behavioural data: An approach based on time-structured models. Oxford, England: Oxford University Press.
14. Harrell, F. E., & Goldstein, R. (1997). A survey of microcomputer survival analysis software: The need for and integrated framework. The American Statistician, 51, 360-373.
15. Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457-481.
16. Kleinbaum, D. G. (1995). Survival analysis: A self-learning text. New York: Springer-Verlag.
17. Lee, E. T. (1992). Statistical methods for survival data analysis (2nd ed.). New York: Wiley.
18. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
19. Loftus, G. R. (1993). A picture is worth a thousand p values: On the irrelevance of hypothesis testing in the microcomputer age. Behavior Research Methods, Instruments, & Computers, 25, 250-256.
20. Luke, D. A. (1993). Charting the process of change: A primer on survival analysis. American Journal of Community Psychology, 21, 203-246.
21. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133.
22. McLellan, A. (1986). "Psychiatric severity" as a predictor of outcome from substance abuse treatments. In R. Meyer (Ed.), Psychopathology and addictive disorders (pp. 97-139). New York: Guilford Press.
23. Muthén, B. O., & Curran, P. J. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.
24. Nesselroade, J. R. (1991). Interindividual differences in intraindividual change. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 137-148). Washington, DC: American Psychological Association.
25. Neufeld, R. W. J. (1998). Introduction to the Special Section on process models in psychological assessment. Psychological Assessment, 10, 307-315.
26. Parmar, M. K. B., & Machin, D. (1995). Survival analysis: A practical approach. New York: Wiley.
27. Rogosa, D. R., & Willett, J. B. (1983). Demonstrating the reliability of the difference score in the measurement of change. Journal of Educational Measurement, 20, 335-343.
28. Slonim-Nevo, V., & Clark, V. A. (1989). An illustration of survival analysis: Factors affecting contraceptive discontinuation among American teenagers. Social Work Research and Abstracts, 25, 7-14.
29. Stoil, M. J., & Hill, G. (1996). Preventing substance abuse: Interventions that work. New York: Plenum.
30. Substance Abuse and Mental Health Services Administration (1997). Preliminary results from the 1996 National Household Survey on Drug Abuse, Washington, DC: U.S. Department of Health and Human Services.
31. Tufte, E. R. (1983). The visual display of quantitative information. Cheshire, CT: Graphics Press.
32. Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
33. Widaman, K. F. (1991). Qualitative transitions amid quantitative development: A challenge for measuring and representing change. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 204-217). Washington, DC: American Psychological Association.
34. Willett, J. B., & Singer, J. D. (1991). From whether to when: New methods for studying student dropout and teacher attrition. Review of Educational Research, 61, 407-450.
35. Yamaguchi, K. (1991). Event history analysis. Newbury Park, CA: Sage.