Quint: An R package for the identification of subgroups of clients who differ in which treatment alternative is best for them

Behavior Research Methods

Volumn 48, Issue 2, 2016, Pages 650-663

  Dusseldorp, Elise ^a,b,c   Doove, Lisa ^a   Mechelen, Iven van ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b LEIDEN UNIVERSITY   (Netherlands)

^c TNO   (Netherlands)

Author keywords

Computer software; Moderator; Regression trees; Subgroup analysis; Treatment efficacy; Treatment subgroup interaction

Indexed keywords

ALGORITHM; BREAST NEOPLASMS; CLUSTER ANALYSIS; CONVALESCENCE; FEMALE; HUMAN; METHODOLOGY; RANDOMIZED CONTROLLED TRIAL (TOPIC); SOFTWARE; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; TREATMENT OUTCOME;

ALGORITHMS; BREAST NEOPLASMS; CLUSTER ANALYSIS; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RECOVERY OF FUNCTION; RESEARCH DESIGN; SOFTWARE; TREATMENT OUTCOME;

EID: 84931841084     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-015-0594-z     Document Type: Article

References (31)

1. Baguley, T. (2009). Standardized or simple effect size: What should be reported? British Journal of Psychology, 100(3), 603–617.
2. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. New York, NY: Chapman and Hall.
3. Byar, D. (1985). Assessing apparent treatment-covariate interactions in randomized clinical trials. Statistics in Medicine, 4, 255–263.
4. Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2nd edn. Hillsdale, NJ: Lawrence Erlbaum.
5. Cohen, J., Cohen, P., West, S., & Aiken, L. (2003). Applied multiple regression/correlation analysis for the behavioral sciences., 3rd edn. Maywah, NJ: Lawrence Erlbaum.
6. Doove, L., Dusseldorp, E., Van Deun, K., & Van Mechelen, I. (2014). A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions. Advances in Data Analysis and Classification, 8(4), 403–425.
7. Dusseldorp, E., Conversano, C., & Van Os, B. J. (2010). Combining an additive and tree-based regression model simultaneously: Stima. Journal of Computational and Graphical Statistics, 19(3), 514–530. doi:10.1198/jcgs.2010.06089
8. Dusseldorp, E., & Meulman, J. J. (2004). The regression trunk approach to discover treatment covariate interaction. Psychometrika, 69(3), 355–374.
9. Dusseldorp, E., & Van Mechelen, I. (2014). Qualitative interaction trees: A tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, 33, 219–237.
10. Fierz, W. (2004). Challenge of personalized health care: To what extent is medicine already individualized and what are the future trends? Medical Science Monitor, 10, RA111–123.
11. Foster, J., Taylor, J., & Ruberg, S. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine, 30(24), 2867–2880.
12. Fritz, H. L., & Helgeson, V. S. (1998). Distinctions of unmitigated communion from communion: self-neglect and over-involvement with others. Journal of Personality and Social Psychology, 75(1), 121.
13. Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2001). The elements of statistical learning. New York: Springer.
14. Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2009). Controlled experiments on the web: survey and practical guide. Data mining and knowledge discovery, 18(1), 140–181.
15. Kraemer, H., Wilson, G., Fairbun, C., & Agras, W. (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry, 59, 877–883.
16. LeBlanc, M., & Crowley, J. (1993). Survival trees by goodness of split. Journal of the American Statistical Association, 88, 457–467.
17. Lipkovich, I., Dmitrienko, A., Denne, J., & Enas, G. (2011). Subgroup identification based on differential effect search–a recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine, 30(21), 2601– 2621.
18. Lubin, A. (1961). The interpretation of significant interaction. Educational and Psychological Measurement, 21, 807–817.
19. R Core Team (2014). R: A language and environment for statistical computing [Manuel de logiciel]. Vienna, Austria. Retrieved from http://www.R-project.org/
20. Radloff, L. S. (1977). The CES-D scale a self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.
21. Roth, A., & Fonagy, P. (2006). What works for whom?: A critical review of psychotherapy research. New York, NY: Guilford Press.
22. Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the life orientation test. Journal of Personality and Social Psychology, 67(6), 1063.
23. Scheier, M. F., Helgeson, V. S., Schulz, R., Colvin, S., Berga, S., Bridges, M. W., Knapp, J., Gerszten, K., & Pappert, W. S. (2005). Interventions to enhance physical and psychological functioning among younger women who are ending nonhormonal adjuvant treatment for early stage breast cancer. Journal of Clinical Oncology, 23(19), 4298–4311.
24. Scheier, M. F., Helgeson, V. S., Schulz, R., Colvin, S., Berga, S. L., Knapp, J., & Gerszten, K. (2007). Moderators of interventions designed to enhance physical and psychological functioning among younger women with early stage breast cancer. Journal of Clinical Oncology, 25(36), 5710–4.
25. Shaffer, J. (1991). Probability of directional errors with disordinal (qualitative) interaction. Psychometrika, 56, 29–38.
26. Shuster, J., & van Eys, J. (1983). Interaction between prognostic factors and treatment. Controlled Clinical Trials, 4(1), 209– 214.
27. Su, X., Tsai, C. L., Wang, H., Nickerson, D. M., & Li, B. (2009). Subgroup analysis via recursive partitioning. The Journal of Machine Learning Research, 10, 141–158.
28. Taylor, B. G., Davis, R. C., & Maxwell, C. D. (2001). The effects of a group batterer treatment program: a randomized experiment in Brooklyn. Justice Quarterly, 18(1), 171–201.
29. Ware Jr, J. E., & Sherbourne, C. D. (1992). The MOS 36-item shortform health survey (SF-36): I. conceptual framework and item selection. Medical Care, 30, 473–483.
30. Wyrwich, K. W., Bullinger, M., Aaronson, N., Hays, R. D., Patrick, D. L., & Symonds, T. (2005). Estimating clinically significant differences in quality of life outcomes. Quality of Life Research, 14(2), 285–295.
31. Zeileis, A., Hothorn, T., & Hornik, K. (2008). Model-based recursive partitioning. Journal of Computational and Graphical Statistics, 17(2), 492–514.