Aerts, M., Guys, H., Molenberghs, G. and Ryan, L. M. (2002). Topics in Modelling of Clustered Data. Chapman and Hall, London.
Carpenter, J. R., Kenward, M. G., and Vansteelandt, S. (2006). A comparison of multiple imputation and doubly robust estimation for analyses with missing data. Journal of the Royal Statistical Society, Series A, 169: 571–584.
Carpenter, J. R. and Kenward, M. G. (2007). Missing Data in Clinical Trials: A Practical Guide. Birmingham: National Health Service Coordinating.
Centre for Research Methodology. (2009). Online at: JH17 MK.shtml (accessed May 28, 2009).
Cnaan, A., Laird, N. M., and Slasor, P. (1997). Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in Medicine, 16 (20): 2349–2380.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112: 155–159.
Collins, L. M., Schafer, J. L., and Kam, C. M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychology Methods, 6 (4): 330–351.
Committee for Medicinal Products for Human Use (CHMP). (2010). Guideline on missing data in confirmatory clinical trials. EMA/CPMP/EWP/1776/99 Rev. 1.
Cook, R. D., and Weisberg, S. (1982). Residuals and Influence in Regression. New York: Chapman & Hall.
Copas, J. B., and Li, H. G. (1997). Inference for non-random samples (with discussion). Journal of the Royal Statistical Society B, 59: 55–96.
Detke, M. J., Lu, Y., Goldstein, D. J., McNamara, R. K., and Demitrack, M. A. (2002). Duloxetine 60 mg once daily dosing versus placebo in the acute treatment of major depression. Journal of Psychiatric Research, 36: 383--390.
Diggle, P. D., and Kenward, M. G. (1994). Informative dropout in longitudinal data analysis (with discussion). Applied Statistics, 43: 49–93.
Diggle, P. J., Heagerty, P., Liang, K. Y., and Seger, S. L. (2002). The Analysis of Longitudinal Data, 2nd Edition. Oxford: Oxford University Press.
Draper, D. (1995). Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society B, 57: 45.
Fahrmeir, L., and Tutz, G. (2001). Multivariate Statistical Modelling Based on Generalized Linear Models. Heidelberg: Springer.
Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2004). Applied Longitudinal Analysis. Hoboken, NJ: Wiley Interscience.
Fedorov, V. V., and Liu, T. 2007. Enrichment design. Wiley Encyclopedia of Clinical Trials, 1–8.
Fleming, T. R. (2011). Addressing missing data in clinical trials. Annals of Internal Medicine, 154: 113–117.
Hamilton, M: A rating scale for depression. J Neurol Neurosurg Psychiatry 1960, 23: 56--61.
Harville, David A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72 (358): 320–338.
Hogan, J. W., and Laird, N. M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine, 16: 239–258.
Horvitz, D. G., and Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47: 663–685.
ICH guidelines. Online at:
Jansen, I., Beunckens, C., Molenberghs, G., Verbeke, G., Mallinckrodt, C. H. (2006a). Analyzing incomplete binary longitudinal clinical trial data. Statistical Science, 21 (1): 52–69.
Jansen, I., Hens, N., Molenberghs, G., Aerts, M., Verbeke, G., and Kenward, M. G. (2006b). The nature of sensitivity in missing not at random models. Computational Statistics and Data Analysis, 50: 830–858.
Kenward, M. G. (1998). Selection models for repeated measurements with non-random dropout: an illustration of sensitivity. Statistics in Medicine, 17 (23): 2723–2732.
Kim, Y. (2011). Missing data handling in chronic pain trials. Journal of Biopharmaceutical Statistics, 21 (2): 311–325.
Landin, R., DeBrota, D. J., DeVries, T. A., Potter, W. Z., and Demitrack, M. A. (2000). The impact of restrictive entry criterion during the placebo lead-in period. Biometrics, 56 (1): 271–278.
Laird, N. M., and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38 (4): 963–974.
Laird, N. M. (1994). Informative dropout in longitudinal data analysis. Applied Statistics, 43: 84.
Leon, A. C., Hakan, D., and Hedeken, D. (2007). Bias reduction with an adjustment for participants’ intent to drop out of a randomized controlled clinical trial. Clinical Trials, 4: 540–547.
Liang, K. Y., and Zeger, S. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73 (1): 13–22.
Liang, K. Y., and Zeger, S. (2000). Longitudinal data analysis of continuous and discrete responses for pre-post designs. Sankhya: The Indian Journal of Statistics, 62 (Series B): 134–148.
Lipkovich, I., Duan, Y., and Ahmed, S. (2005). Multiple imputation compared with restricted pseudo-likelihood and generalized estimating equations for analysis of binary repeated measures in clinical studies. Pharmaceutical Statistics, 4 (4): 267–285.
Little, R. J. A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of American Statistical Association, 88 (421): 125–134.
Little, R. J. A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika, 81 (3): 471–483.
Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated measures studies. Journal of American Statistical Association, 90 (431): 1112–1121.
Little, R. J. A., and Rubin, D. B. (2002). Statistical Analysis with Missing Data, 2nd Edition. New York: Wiley.
Little, R., and Yau, L. (1996). Intent-to-treat analysis for longitudinal studies with drop-outs. Biometrics, 52 (4): 1324–1333.
Little, R. J. A., and Yau, L. (1998). Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model. Psychological Methods, 3: 147–159.
Liu, G., and Gould, A. L. (2002). Comparison of alternative strategies for analysis of longitudinal trials with dropouts. Journal of Biopharmaceutical Statistics, 12 (2): 207–226.
Lu, K., and Mehrotra, D. (2009). Specification of covariance structure in longitudinal data analysis for randomized clinical trials. Statistics in Medicine, 4: 474–488.
Ma, G., Troxel, A. B., and Heitjan, D. F. (2005). An index of local sensitivity to nonignorable drop-out in longitudinal modeling. Statistics in Medicine, 24 (14): 2129–2150.
Mallinckrodt, C. H., Clark, S. W., Carroll, R. J., and Molenberghs, G. (2003). Assessing response profiles from incomplete longitudinal clinical trial data under regulatory considerations. Journal of Biopharmaceutical Statistics, 13 (2): 179–190.
Mallinckrodt, C. H., Kaiser, C. J., Watkin, J. G., Molenberghs, G., and Carroll, R. J. (2004). The effect of correlation structure on treatment contrasts estimated from incomplete clinical trial data with likelihood-based repeated measures compared with last observation carried forward ANOVA. Clinical Trials, 1 (6): 477–489.
Mallinckrodt, C. H., and Kenward, M. G. (2009). Conceptual considerations regarding choice of endpoints, hypotheses, and analyses in longitudinal clinical trials. Drug Information Journal, 43 (4): 449–458.
Mallinckrodt, C. H., Lane, P. W., Schnell, D., Peng, Y., and Mancuso, J. P. (2008). Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Information Journal, 42: 305–319.
Mallinckrodt, C. H., Lin, Q., Lipkovich, I., and Molenberghs, G. (forthcoming). A structured approach to choosing estimands and estimators in longitudinal clinical trials. Pharmaceutical Statistics, (In Press).
Mallinckrodt, C. H., Tamura, R. N., and Tanaka, Y. (2011). Improving signal detection and reducing placebo response in psychiatric clinical trials. Journal of Psychiatric Research, 45: 1202–1207.
McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman & Hall.
Meng, X.-L. (1994). Multiple-imputation inferences with uncongenial sources of input. Statistical Science, 9: 538–558.
Molenberghs, G., and Kenward, M. G. (2007). Missing Data in Clinical Studies. Chichester: John Wiley & Sons.
Molenberghs, G., Kenward, M. G., and Lesaffre, E. (1997). The analysis of longitudinal ordinal data with nonrandom dropout. Biometrika, 84 (1): 33–44.
Molenberghs, G., Thijs, H., Jansen, I., Beunckens, C., Kenward, M. G., Mallinckrodt, C., and Carroll, R. J. (2004). Analyzing incomplete longitudinal clinical trial data. Biostatistics, 5 (3): 445–464.
Molenberghs, G., and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
Molenberghs, G., Verbeke, G., Thijs, H., Lesaffre, E., and Kenward, M. (2001). Mastitis in dairy cattle: Local influence to assess sensitivity of the dropout process. Computational Statistics & Data Analysis, 37 (1): 93–113.
National Research Council (2010). The Prevention and Treatment of Missing Data in Clinical Trials. Panel on Handling Missing Data in Clinical Trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
O’Neill, R. T., and Temple, R. (2012). The prevention and treatment of missing data in clinical trials: An FDA perspective on the importance of dealing with it. Clinical Pharmacology and Therapeutics. .
Permutt, T and Pinheiro, J. (2009). Dealing with the missing data challenge in clinical trials. Drug Information Journal. 43: 403--408.
Ratitch, B, O’Kelly, M. Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures. PharmaSUG 2011. Available at (accessed October 4, 2011).
Robins, J. M., Rotnizky, A., and Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89: 846–866.
Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90: 106–121.
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63 (3): 581–592.
Rubin, D. B. (1978). Multiple imputations in sample surveys – a phenomenological Bayesian approach to nonresponse. In Imputation and Editing of Faulty or Missing Survey Data. Washington, DC: U.S. Department of Commerce, pp. 1–23.
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.
Rubin, D. B. (1994). Informative dropout in longitudinal data analysis. Applied Statistics, 43: 80–82.
SAS Institute Inc. (2003). SAS/STAT® User's Guide, Version 9.1, Cary, NC: SAS Institute, Inc.
Schafer, J. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Statistics Neerlandica, 57: 19–35.
Shen, S., Beunckens, C., Mallinckrodt, C., and Molenberghs, G. (2006). A local influence sensitivity analysis for incomplete longitudinal depression data. Journal of Biopharmacological Statistics, 16 (3): 365–384.
Shen, J., Kobak, K. A., Zhao, Y., Alexander, M., and Kane, J. (2008). Use of remote centralized raters via live 2-way video in a multi central clinical trial for schizophrenia. Journal of Clinical Psychopharmacology, 28 (6): 691–693.
Siddiqui, O., Hung, H. M., and O’Neill, R. O. (2009). MMRM vs. LOCF: A comprehensive comparison based on simulation study and 25 NDA datasets. Journal of Biopharmaceutical Statistics, 19 (2): 227–246.
Snedecor, G. W., and Cochran, W. G. (1989). Statistical Methods. 8th edition. Aimes: Iowa State University Press.
Temple, R. (2005). Enrichment designs: Efficiency in development of cancer treatments. Journal of Clinical Oncology, 23 (22): 4838–4839.
Teshome, B., Lipkovich, I., Molenberghs, G., and Mallinckrodt, C. (forthcoming). A multiple imputation based approach to sensitivity analyses and effectiveness assessments in longitudinal clinical trials. Journal of Biopharmacological Statistics.
Thijs, H., Molenberghs, G., Michiels, B., Verbeke, G., and Curran, D. (2002) Strategies to fit pattern-mixture models. Biostatistics, 3: 245–265.
Thijs, H., Molenberghs, G., and Verbeke, G. (2000). The milk protein trial: Influence analysis of the dropout process. Biomedical Journal, 42 (5): 617–646.
Troxel, A. B., Ma, G., and Heitjan, D. F. (2004). An index of local sensitivity to nonignorability. Statistica Sinica, 14: 1221–1237.
Tsiatis, A. A. (2006). Semiparametric Theory and Missing Data. New York: Springer.
Verbeke, G., and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
Verbeke, G., Molenberghs, G., Thijs, H., Lesaffre, E., and Kenward, M. G. (2001). Sensitivity analysis for nonrandom dropout: a local influence approach. Biometrics, 57 (1): 7–14.
Wonnacott, T. H., and Wonnacott, R. J. (1981). Regression: A Second Course in Statistics. New York: Wiley.
Wu, M. C., and Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics, 44: 175–188.
Wu, M. C., and Bailey, K. R. (1989). Estimation and comparison of changes in the presence of informative right censoring: conditional linear model. Biometrics, 45 (3): 939–955.
Zhu, H. T., and Lee, S. Y. (2001). Local influence for incomplete-data models. Journal of the Royal Statistical Society B, 63: 111–126.