Mplus provides new methods for the analysis of data from randomized trials (clinical trials, randomized preventive interventions):
Complier-average causal effect (CACE) modeling--assessing treatment effects among compliers in the treatment group as compared to potential compliers in the control group.
Mplus setup for CACE modeling. Mplus Examples.
- Sobel, M. & Muthén, B. (2012). Compliance mixture modelling with a zero effect complier class and missing data. Biometrics, 68, 1037-1045. DOI: 10.1111/j.1541-0420.2012.01791.x
- Jo, B., Asparouhov, T. & Muthén, B. (2008). Intention-to-treat analysis in cluster randomized trials with noncompliance. Statistics in Medicine, 27, 5565-5577.
- Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27, 385-409.
Growth mixture modeling--allowing treatment effects to vary across latent trajectory classes and among individuals within classes. View setup for growth mixture modeling.
- Muthén, B., Brown, C.H., Hunter, A., Cook, I.A. & Leuchter, A.F. (2011). General approaches to analysis of course: Applying growth mixture modeling to randomized trials of depression medication. In P.E. Shrout (ed.), Causality and Psychopathology: Finding the Determinants of Disorders and their Cures (pp. 159-178). New York: Oxford University Press.
- Muthén, B. & Brown, H. (2009). Estimating drug effects in the presence of placebo response: Causal inference using growth mixture modeling. Statistics in Medicine, 28, 3363-3385.
- Muthén, B., Brown, C.H., Masyn, K., Jo, B., Khoo, S.T., Yang, C.C., Wang, C.P., Kellam, S., Carlin, J., & Liao, J. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475.
download paper. Mplus inputs and outputs.
For more information, visit our General Description page.
For more papers see our Randomized Trials paper topics.