A closed-form estimator for meta-analysis and surrogate markers evaluation

References

• Alonso, A., Molenberghs, G. (2007). Surrogate marker evaluation from an information theory perspective. Biometrics 63(1):180–186.
   PubMed Web of Science ®Google Scholar
• Alonso Abad, A., Bigirumurame, T., Burzykowski, T., Buyse, M., Molenberghs, G., Muchene, L., Perualila, N., Shkedy, Z., Van der Elst, W. (2016). Applied Surrogate Endpoint Evaluation with SAS and R. Boca Ratón, FL: Chapman / Hall/CRC.
   Google Scholar
• Burzykowski, T., Molenberghs, G., Buyse, M. (2005). The Evaluation of Surrogate Endpoints. New York: Springer-Verlag.
   Google Scholar
• Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., Geys, H. (2000). The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics 1(1):49–67.
   PubMedGoogle Scholar
• Chen, H., Manning, A., Dupuis, J. (2012). A method of moments estimator for random effect multivariate meta-analysis. Biometrics 68(4):1278–1284.
   PubMed Web of Science ®Google Scholar
• Hermans, L., Nassiri, V., Molenberghs, G., Kenward, M., Van der Elst, W., Aerts, M., Verbeke, G. (2017). Fast, closed-form, and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes. Communications in Statistics - Simulation and Computation 47(5):1492–1505.
   Google Scholar
• Higham, N. (2002). Computing the nearest correlation matrix - a problem from finance. IMA Journal of Numerical Analysis 22:329–343.
   Web of Science ®Google Scholar
• Jackson, D., White, I., Riley, R. (2013). A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biometrical Journal 55(2):231–245.
   PubMed Web of Science ®Google Scholar
• Laird, N., Lange, N. (1987). [prediction of future observations in growth curve models]: Comment. Statistical Science 2(4):451–454.
   Google Scholar
• Laird, N., Ware, J. (1983). Random-effects models for longitudinal data. Biometrics 38(4):963–974.
   Web of Science ®Google Scholar
• Lindstrom, M., Bates, D. (1988). Newton-raphson and EM algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association 83(404):1014–1022.
   Web of Science ®Google Scholar
• Molenberghs, G., Burzykowski, T., Alonso, A., Assam, P., Tilahun, A., Buyse, M. (2010). A unified framework for the evaluation of surrogate endpoints in mental-health clinical trials. Statistical Methods in Medical Research 19(3):205–236.
   PubMed Web of Science ®Google Scholar
• Molenberghs, G., Hermans, L., Nassiri, V., Kenward, M., Van der Elst, W., Aerts, M., Verbeke, G. (2018). Clusters with random size: Maximum likelihood versus weighted estimation. Statistica Sinica 28(3): 1107–1132.
   Google Scholar
• Molenberghs, G., Kenward, M., Aerts, M., Verbeke, G., Tsiatis, A., Davidian, M., Rizopoulos, D. (2014). On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: Sequential trials, random sample sizes, and missing data. Statistical Methods in Medical Research 23(1):11–41.
   PubMed Web of Science ®Google Scholar
• Molenberghs, G., Verbeke, G., Iddi, S. (2011). Pseudo-likelihood methodology for partitioned large and complex samples. Statistics & Probability Letters 81(7):892–901.
   Web of Science ®Google Scholar
• Reinsel, G. (1985). Mean squared error properties of empirical bayes estimators in a multivariate random effects general linear model. Journal of the American Statistical Association 80(391):642–650.
   Web of Science ®Google Scholar
• Rousseeuw, P. J., Molenberghs, G. (1993). Transformation of non positive semidefinite correlation matrices. Communications in Statistics, Theory and Methods 22(4):965–984.
   Web of Science ®Google Scholar
• Shah, A., Laird, N., Schoenfeld, D. (1997). A random-effects model for multiple characteristics with possibly missing data. Journal of the American Statistical Association 92(438):775–779.
   Web of Science ®Google Scholar
• Tibaldi, F., Cortiñas Abrahantes, J., Molenberghs, G., Renard, D., Burzykowski, T., Buyse, M., Parmar, M., Stijnen, T., Wolfinger, R. (2003). Simplified hierarchical linear models for the evaluation of surrogate endpoints. Journal of Statistical Computation and Simulation 73(9):643–658.
   Web of Science ®Google Scholar
• Van der Elst, W., Hermans, L., Verbeke, G., Kenward, M., Nassiri, V., Molenberghs, G. (2016). Unbalanced cluster sizes and rates of convergence in mixed-effects models for clustered data. Journal of Statistical Computation and Simulation 86(11):2123–2139.
   Web of Science ®Google Scholar
• Van der Elst, W., Meyvisch, P., Alonso, A., Hannah, M. E., Christopher, J. W., Molenberghs, G. (2017). Surrogate: Evaluation of surrogate endpoints in clinical trials. [R Package Version 1.1]. https://CRAN.R-project.org/package=Surrogate
   Google Scholar
• Verbeke, G., Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. Springer,New York.
   Google Scholar
• Vonesh, E., Carter, R. (1987). Efficient inference for random-coefficient growth curve models with unbalanced data. Biometrics 43(3):617–628.
   PubMed Web of Science ®Google Scholar
• West, B., Welch, K., Galecki, A. (2014). Linear Mixed Models: A Practical Guide Using Statistical Software, 2nd ed. Boca Ratón, FL: Chapman & Hall/CRC.
   Google Scholar