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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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References

Amemiya, T. (1984). Tobit models: A survey. Journal of Econometrics, 24, 3-61.

Anderson, T.W. (1957). Maximum likelihood estimation for the multivariate normal distribution when some observations are missing. Journal of the American Statistical Association, 52, 200-203.

Angrist, J.D., Imbens, G.W., and Rubin, D.B. (1996). Identification of causal effects using instrumental variables (with discussion and rejoinder). Journal of the American Statistical Association, 91, 444-472.

Baker, S.G. (1997). Compliance, all-or-none. In The Encyclopedia of Statistical Science, Volume I, S. Kotz, C.R. Read, and D.L. Banks (eds.), pp. 134-138. New York: John Wiley and Sons.

Baker, S.G., and Lindeman, K.S. (1994). The paired availability design: A proposal for evaluating epidural analgesia during labor. Statistics in Medicine, 13(21), 2,269-2,278.

Bang, H., and Robins, J.M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61, 962-972.

Barnard, J., and Rubin, D.B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86, 949-955.

Birmingham, J., Rotnitzky, A., and Fitzmaurice, G. (2003). Pattern mixture and selection models for analyzing monotone missing data. Journal of the Royal Statistical Society, Series B, 65, 275-297.

Carlin, B.P., and Louis, T.A. (2000). Bayes and Empirical Bayes Methods for Data Analysis, 2nd edition. Boca Raton, FL: Chapman and Hall, CRC Press.

Carpenter, J. (2009). Statistical Methods for Clinical Studies with Missing Data: What’s Hot, What’s Cool, and What’s Useful. Unpublished paper. Available: http://www.iscb2009.info/RSystem/Soubory/Prez%20Tuesday/S18.1%20Carpenter.pdf.

Daniels, M.J., and Hogan, J.W. (2008). Missing Data in Longitudinal Studies. Boca Raton, FL: Chapman and Hall, CRC Press.

DeGruttola, V., and Tu, X.M. (1994). Modeling progression of CD4-lymphocyte count and Modeling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics, 50, 1,003-1,014.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1-38.

Diggle, P., and Kenward, M.G. (1994). Informative dropout in longitudinal data analysis (with discussion). Applied Statistics, 43, 49-94.

Diggle, P.J., Heagerty, P., Liang K.Y., and Zeger, S.L. (2002). The Analysis of Longitudinal Data, 2nd Edition. Oxford, England: Oxford University Press.

Drennan, K. (2003). Pharma Wants You: Clinical Trials Are Agencies’ New Proving Ground. Pharmaceutical Executive. Available:http://www.corbettaccel.com/press/pdf/200304_Pharma_Exec.pdf.

Emmanuel, E.J. (2005). Undue inducement—Nonsense on stilts. American Journal of Bioethics, 5(5), 9-13.

European Medicines Evaluation Agency. (1998). Statistical Principles for Clinical Trials; Step 5: Note for Guidance on Statistical Principles for Clinical Trials. International Conference on Harmonisation (ICH) Topic E9. Available: http://www.ich.org/LOB/media/MEDIA485.pdf.

European Medicines Evaluation Agency. (2009). Guideline on Missing Data in Confirmatory Clinical Trials. Committee for Medical Products for Human Use (April). Available: http://www.ema.europa.eu/pdfs/human/ewp/177699endraft.pdf.

Finkelstein, D.M., and Wolfe, R.A. (1985). A semiparametric model for regression analysis of interval-censored failure time data. Biometrics, 41, 933-945.

Fitzmaurice, G.M., Laird, N.M., and Rotnitzky, A.G. (1993). Regression models for discrete longitudinal responses. Statistical Science, 8, 284-309.

Fitzmaurice, G.M., Laird, N.M., and Ware, J.H. (2004). Applied Longitudinal Analysis. Hoboken, NJ: Wiley Interscience.

Follmann, D. (2006). Augmented designs to assess immune response in vaccine trials. Biometrics, 62, 1,162-1,169.

Frangakis, C.E., and Rubin, D.B. (2002). Principal stratification in causal inference. Biometrics, 58, 21-29.

Gelfand, A.E., and Smith, A.F.M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409.

Gelfand, A.E., Hills, S.E., Racine-Poon, A., and Smith, A.F.M. (1990). Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association, 85, 972-985.

Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1(3), 515-533.

Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2003). Bayesian Data Analysis, 2nd edition. London, England: CRC Press.

Geman, S., and Geman, D. (1984). Stochastic relaxation, Gibbs’ distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.

Gilbert, P.B., Bosch, R.J., and Hudgens, M.G. (2003). Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials. Biometrics, 59, 531-541.

Gilks, W.R., Wang, C.C., Yvonnet, B., and Coursaget, P. (1993). Random effects models for longitudinal data using Gibbs’ sampling. Biometrics, 49, 441-453.

Glynn, R., Laird, N.M., and Rubin, D.B. (1986). Selection modeling versus mixture modeling with nonignorable nonresponse. In Drawing Inferences from Self-Selected Samples, H. Wainer (ed.), pp. 119-146. New York: Springer-Verlag.

Glynn, R.J., Laird, N.M., and Rubin, D.B. (1993). Multiple imputation in mixture models for nonignorable nonresponse with follow-ups. Journal of the American Statistical Association, 88, 984-993.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Greenlees, W.S., Reece, J.S., and Zieschang, K.D. (1982). Imputation of missing values when the probability of nonresponse depends on the variable being imputed. Journal of the American Statistical Association, 77, 251-261.

Harville, D.A. (1977). Maximum likelihood approaches to variance component estimation and to related problems (with discussion). Journal of the American Statistical Association, 72, 320-340.

Heagerty, P.J. (1999). Marginally specified logistic-normal models for longitudinal binary data. Biometrics, 55(3), 688-698.

Heagerty, P.J. (2002). Marginalized transition models and likelihood inference for categorical longitudinal data. Biometrics, 58, 342-351.

Heckman, J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables, and a simple estimator for such models. Annals of Economic and Social Measurement, 5, 475-492.

Heitjan, D.F. (1993). Ignorability and coarse data: Some biomedical examples. Biometrics, 49, 1,099-1,109.

Heitjan, D.F. (1994). Ignorability in general complete-data models. Biometrika, 81, 701-708.

Heitjan, D.F. (1997). Annotation: What can be done about missing data? Approaches to imputation. American Journal of Public Health, 87, 548-550.

Heitjan, D., and Rubin, D.B. (1991). Ignorability and coarse data. Annals of Statistics, 19, 2,244-2,253.

Hirano, K., Imbens, G., Rubin, D.B., and Zhao, X.H. (2000). Estimating the effect of an influenza vaccine in an encouragement design. Biostatistics, 1, 69-88.

Hogan, J.W., Roy, J., and Korkontzelou, C. (2004). Biostatistics tutorial: Handling dropout in longitudinal data. Statistics in Medicine, 23, 1,455-1,497.

Imbens, G.W., and Rubin, D.B. (1997a). Bayesian inference for causal effects in randomized experiments with noncompliance. Annals of Statistics, 25, 305-327.

Imbens, G.W., and Rubin, D.B. (1997b). Estimating outcome distributions for compliers in instrumental variables models. Review of Economic Studies, 64, 555-574.

International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. (1994). ICH Harmonised Tripartite Guideline: Dose-Response Information to Support Drug Registration: E4. Available: http://www.ema.europa.eu/pdfs/human/ich/037895en.pdf.

International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. (2001). ICH Harmonised Tripartite Guideline: Choice of Control Group and Related Issues in Clinical Trials: E10. Available: http://www.ema.europa.eu/pdfs/human/ich/036496en.pdf.

Jansen, I., Beunckens, C., Molenberghs, G., Verbeke, G., and Mallinckrodt, C. (2006). Analyzing incomplete discrete longitudinal clinical trial data. Statistical Science, 21, 52-69.

Jennrich, R.I., and Schluchter, M.D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42, 805-820.

Joffe, M.M., Small, D., and Hsu, C.Y. (2007). Defining and estimating intervention effects for groups that will develop an auxiliary outcome. Statistical Science, 22, 74-97.

Kang, J.D.Y., and Schaffer, J.L. (2007). A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22(4), 523-539.

Kenward, M.G. (1998) Selection models for repeated measurements with non-random dropout: An illustration of sensitivity. Statistics in Medicine, 17(23), 2,723-2,732.

Kenward, M.G., and Carpenter, J.R. (2008). Multiple imputation. In Longitudinal Data Analysis, G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs (eds.). New York: CRC Press.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Kenward, M.G., and Molenberghs, G. (2009). Last observation carried forward: A crystal ball? Journal of Biopharmaceutical Statistics, 9(5), 872-888.

Kenward, M.G., Lesaffre, E., and Molenberghs, G. (1994). An application of maximum likelihood and estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random. Biometrics, 50, 945-953.

Kenward, M.G., Molenberghs, G., and Thijs, H. (2003). Pattern mixture models with proper time dependence. Biometrika, 90(1), 53-71.

Laird, N.M., and Ware, J.H. (1982). Random effects models for longitudinal data. Biometrics, 38, 963-974.

Lakatos, E. (1988). Sample sizes based on the logrank statistic in complex clinical trials. Biometrics, 44, 229-241.

Lavori, P.W., Brown, C.H., Duan, N., Gibbons, R.D., and Greenhouse, J. (2008). Missing data in longitudinal clinical trials part A: Design and conceptual issues. Psychiatric Annals, 38(12), 784-792.

Leon, A.C., Hakan, D., and Hedeken, D. (2007). Bias reduction with an adjustment for participants’ intent to dropout of a randomized controlled clinical trial. Clinical Trials, 4, 540-547.

Liang, K-Y., and Zeger, S.L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22.

Liang, K-Y., Zeger, S.L., and Qaqish, B. (1992). Multivariate regression analyses for categorical data (with discussion). Journal of the Royal Statistical Society, Series B, 54, 3-40.

Little, R.J.A. (1985). A note about models for selectivity bias. Econometrica, 53, 1,469-1,474.

Little, R.J.A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88, 125-134.

Little, R.J.A. (1994). A class of pattern-mixture models for normal missing data. Biometrika, 81, 471-483.

Little, R.J.A. (1995). Modeling the drop-out mechanism in longitudinal studies. Journal of the American Statistical Association, 90, 1,112-1,121.

Little, R.J.A. (2008). Selection and pattern mixture models. In Advances in Longitudinal Data Analysis, G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs (eds.), pp. 409-431. London, England: CRC Press.

Little, R.J.A., and Rubin, D.B. (2000). Causal effects in clinical and epidemiological. studies via potential outcomes: Concepts and analytical approaches. Annual Review of Public Health, 21, 121-145.

Little, R.J.A., and Rubin, D.B. (2002). Statistical Analysis with Missing Data, 2nd edition. New York: Wiley.

Little, R.J.A., and Wang, Y.-X. (1996). Pattern mixture models for multivariate incomplete data with covariates. Biometrics, 52, 98-111.

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.

Little, R.J.A., Long, Q., and Lin, X. (2009). A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance. Biometrics, 65(2), 640-649.

Liu, M., Taylor, J.M.G., and Belin, T.R. (2000). Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies. Biometrics, 56, 1,157-1,163.

Long, Q., Little, R.J.A., and Lin, X. (in press). Estimating the CACE in trials involving multitreatment arms subject to noncompliance: A Bayesian framework. To appear in the Journal of the Royal Statistical Society, Series C.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Marcus, B.H., Lewis, B.A., Hogan, J., King, T.K., Albrecht, A.E., Bock, B., Parisi, A.F., Niaura, R., and Abrams, D.B. (2005). The efficacy of moderate-intensity exercise as an aid for smoking cessation in women: A randomized controlled trial. Nicotine and Tobacco Research, 7(6), 871-880.

Mehrotra, D., Li, X., and Gilbert, P.B. (2006). A comparison of eight methods for the dual endpoint evaluation of efficacy in a proof of concept HIV vaccine. Biometrics, 62, 893-900.

Meng, X.L., and van Dyk, D. (1997). The EM algorithm—An old folk song sung to a fast new tune. Journal of the Royal Statistical Society, Series B, 59, 511-567.

Molenberghs, G., and Kenward, M.G. (2007). Missing Data in Clinical Studies. Chichester, UK: Wiley.

Molenberghs, G., Kenward, M.G., and Lesaffre, E. (1997). The analysis of longitudinal ordinal data with informative dropout. Biometrika, 84(1), 33-44.

Molenberghs, G., Michiels, B., Kenward, M.G., and Diggle, P.J. (1998). Monotone missing data and pattern mixture models. Statistica Neerlandica, 52(2), 153-161.

Molenberghs, G., Verbeke, G., Thijs, H., Lesaffre, E., and Kenward, M.G. (2001). Influence analysis to assess sensitivity of the dropout process. Computational Statistics and Data Analysis, 37(1), 93-113.

Mori, M., Woolson, R.F., and Woodsworth, G.G. (1994). Slope estimation in the presence of informative censoring: Modeling the number of observations as a geometric random variable. Biometrics, 50, 39-50.

Murray, G.D., and Findlay, J.G. (1988). Correcting for the bias caused by dropouts in hypertension trials. Statistics in Medicine, 7, 941-946.

Nathan, D.G., and Wilson, J.D. (2003). Clinical research and the NIH: A report card. New England Journal of Medicine, 349, 1,860-1,865.

Normand, S.L., Rector, T.S., Neaton, J.D., Pina, I.L., Lazar, R.M., Proestel, S.E., Fleischer, D.J., Cohn, J.N., and Spertus, J.A. (2005). Clinical and analytical considerations in the study of health status in device trials for heart failure. Journal of Cardiac Failure, 11, 396-403.

Office for Human Research Protections. (2008). Draft Guidance on Important Considerations for When Participation of Human Subjects in Research Is Discontinued. Department of Health and Human Services. Available: http://www.primr.org/uploadedFiles/PRIMR_Site_Home/Public_Policy/Recently_Files_Comments/Draft_Guidance_on_Discontinued_Participation.pdf.

Oleske, D.M., Kwasny, M.M., Lavender, S.A., and Andersson, G.B. (2007). Participation in occupational health longitudinal studies: Predictors of missed visits and dropouts. Annals of Epidemiology, 17, 9-18.

O’Neill, R.T. (2009). Missing Data in Clinical Trials Intended to Support Efficacy and Safety of Medical Products: The Need for Consensus. 30th Annual Conference of the International Society for Clinical Biostatistics, Prague, Czech Republic. Available: http://www.iscb2009.info/RSystem/Soubory/Prez%20Tuesday/S18.3%20O’Neill.pdf.

Park, T. (1993). A comparison of the generalized estimating equation approach with the maximum likelihood approach for repeated measurements. Statistics in Medicine, 12, 1,723-1,732.

Pocock, S.J. (1983). Clinical Trials: A Practical Approach. New York: Wiley.

Qin, L., Gilbert, P.B., Follmann, D, and Li, D. (2008). Assessing surrogate endpoints in vaccine trials with case-cohort sampling and the Cox model. Annals of Applied Statistics, 2,386-2,407.

Raghunathan, T., Lepkowski, J. VanHoewyk, M., and Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology, 27(1), 85-95.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Robins, J.M. (1989). The analysis of randomized and non-randomized treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: A Focus on AIDS, L. Sechrest, H. Freeman, and A. Mulley (eds.), pp. 113-159. Washington, DC: U.S. Public Health Service, National Center for Health Services Research.

Robins, J.M. (1993). Analytic methods for estimating HIV treatment and cofactor effects. In Methodological Issues of AIDS Mental Health Research, D.G. Ostrow and R. Kessler (eds.), pp. 213-290. New York: Plenum.

Robins, J.M., and Finkelstein, D (2000). Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics, 56(3), 779-788.

Robins, J.M., and Greenland, S. (1996). Discussion of identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 456-458.

Robins, J.M., and Rotnitsky, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. In AIDS Epidemiology-Methodological Issues, N. Jewell and D.K. Farewell (eds.), pp. 297-331. Boston, MA: Birkhäuser.

Robins, J.M., and Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association, 90, 122-129.

Robins J.M., and Rotnitzky, A. (2001). Comment on Inference for semiparametric models: Some questions and an answer by Bickel and Kwon. Statistica Sinica, 11, 920-936.

Robins, J.M., Rotnitzky, 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.

Robinson, K.A., Dennison, C.R., Wayman, D.M., Pronovost, P.J., and Needham, D.M. (2007). Systematic review identifies a number of strategies important for retaining study participants. Journal of Clinical Epidemiology, 60(8), 757-765.

Rose, E.A., Gelijns, A.C., Moskowitz, A.J., Heitjan, D.F., Stevenson, L.W., Debistky, W., Long, J.W., Ascheim, D., Tierney, A.R., Levitan, R.G., Watson, J.T., and Meier, P. (2001). Long-term use of a left ventricular assist device for end-stage heart failure. New England Journal of Medicine, 345, 1,435-1,443.

Rosenbaum, P.R., and Rubin, D.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.

Rotnitzky, A., Holcroft, C.A., and Robins, J.M. (1997). Efficiency comparisons in multivariate multiple regression with missing outcomes. Journal of Multivariate Analysis, 61(1), 102-128.

Rotnitzky, A., Robins, J.M., and Scharfstein, D.O. (1998). Semiparametric regression for repeated measures outcomes with nonignorable nonresponse. Journal of the American Statistical Association, 93, 1,321-1,339.

Rotnitzky, A., Farall, A., Bergesion, A., and Scharfstein, D. (2007). Analysis of failure time data under competing censoring mechanisms. Journal of the Royal Statistical Society, Series B, 69, 307-327.

Rotnitzky, A., Bergesio, A., and Farall, A. (2009). Analysis of quality of life adjusted failure time data in the presence of competing possibly informative censoring mechanisms. Lifetime Data Analysis, 15, 1-23.

Rubin, D.B. (1976). Inference and missing data. Biometrika, 63, 581-592.

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Rubin, D.B. (1996). Multiple imputation after 18+ years (with discussion). Journal of the American Statistical Association, 91, 473-489.

SAS Institute, Inc. (2008a). SAS/STAT®9.2, User’s Guide, The MIXED Procedure. Cary, NC: Author.

SAS Institute, Inc. (2008b). SAS/STAT®9.2, User’s Guide, The NLMIXED Procedure. Cary, NC: Author.

Schafer, J.L. (1996). Analysis of Incomplete Multivariate Data. London, England: Chapman and Hall.

Scharfstein, D.O., and Robins, J.M. (2002). Estimation of the failure time distribution in the presence of informative censoring. Biometrika, 89, 617-634.

Scharfstein, D.O., Rotnitzky, A., and Robins, J.M. (1999). Adjusting for nonignorable dropout using semiparametric nonresponse models (with discussion). Journal of the American Statistical Association, 94, 1,096-1,146.

Schluchter, M.D. (1992). Methods for the analysis of informatively censored longitudinal data. Statistics in Medicine, 11, 1,861-1,870.

Schneider, M.M., Hoepelman, A., Schattenkerk, D.K., Eeftinck, T.L., Nielsen, van def Graaf, Y., Frissen, J.P., van def Ende, I.M., Kolsters, A.F., and Borleffs, J.C. (1992). A controlled trial of aerosolized pentamidine or trimethoprim-sulfamethoxazole as primary prophylaxis against Pneumocystis carinii pneumonia in patients with human immunodeficiency virus infection. New England Journal of Medicine, 327(26), 1,836-1,841.

Shepherd, B.E., Redman, M.W., and Ankerst, D.P. (2008). Does Finasteride affect the severity of prostate cancer? A causal sensitivity analysis. Journal of the American Statistical Association, 103, 1,392-1,404.

Shih, J.H. (1995). Sample size calculation for complex clinical trials with survival endpoints. Controlled Clinical Trials, 16(6), 395-407

Slaughter M.S., Rogers, J.G., and Milano, C.A., (2009). Advanced heart failure treated with continuous-flow left ventricular assist device. New England Journal of Medicine, 361, 2,241-2,251.

Snow, W.M., Connett, J.E., Sharma, S., and Murray, R.P. (2007), Predictors of attendance and dropout at the Lung Health Study 11-year follow-up. Contemporary Clinical Trials, 28(1), 25-32.

Sprague, S., Leece, P., Bhandari, M., Tornetta, P., 3rd, Schemitsch, E., and Swiontkowksi, M.F. (2003). Limiting loss to follow-up in a multicenter randomized trial in orthopedic surgery. Controlled Clinical Trials, 24(6), 719-725.

Stolzenberg, R.M., and Relles, D.A. (1990). Theory testing in a world of constrained research design—The significance of Heckman’s censored sampling bias correction for nonexperimental research. Sociological Methods and Research, 18, 395-415.

Tanner, M.A. (1991). Tools for Statistical Inference: Observed Data and Data Augmentation Methods. New York: Springer-Verlag.

Tanner, M.A., and Wong, W.H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82, 528-550.

Thijs, H., Molenberghs, G., and Verbeke, G. (2000). The milk protein trial: Influence analysis of the dropout process. Biometrical Journal, 42(5), 617-646.

U.S. Food and Drug Administration. (2002). Guidance for Industry: Antiretroviral Drugs Using Plasma HIV; RNA Measurements—Clinical Considerations for Accelerated and Traditional Approval. Available: http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM070968.pdf.

U.S. Food and Drug Administration. (2008). Guidance for Sponsors, Clinical Investigators, and IRBs: Data Retention When Subjects Withdraw from FDA-Regulated Clinical Trials. Office of the Commissioner, Good Clinical Practice Program. Available: http://www.fda.gov/downloads/RegulatoryInformation/Guidances/UCM126489.pdf.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
×

Van Buuren, S., and Oudshoorn, C.G.M. (1999). Flexible Multivariate Imputation by MICE. (TNO/VGZ/PG99.054.) Leiden, The Netherlands: TNO Preventieen Gezondheid. Available: http://www.multiple-imputation.com.

Van Steen, K., Molenberghs, G., Verbeke, G., and Thijs, H. (2001). A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data. Statistical Modeling, 1, 125-142.

Verbeke, G., and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag.

Verbeke, G., and Molenberghs, G. (in press). Arbitrariness of models for augmented and coarse data, with emphasis on incomplete-data and random-effects models. Statistical Modeling, 9.

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.

Wang-Clow, F., Lange, M., Laird, N.M., and Ware, J.H. (1995). Simulation study of estimators for rate of change in longitudinal studies with attrition. Statistics in Medicine, 14, 283-297.

Warden, D., Trivedi, M.H., Wisniewski, S.R., Davis, L., Nierenberg, A.A., Gaynes, B.N., Zisook, S., Hollon, S.D., Balasubramani, G.K., Howland, R., Fava, M., Steward, J.W., and Rush, A.J. (2007). Predictors of attrition during initial (citalopram) treatment for depression: A STAR*D report. American Journal of Psychiatry, 164, 1,189-1,197.

White, I.R. (2005). Uses and limitations of randomization-based efficacy estimators. Statistical Methods in Medical Research, 14, 327-347.

Williams, P.L., Van Dyke, R., Eagle, M., Smith, D., Vincent, C., Cuipak, G, Oleske, J., and Seage, G.R. III. (2008). Association of site-specific and participant-specific factors with retention of children in a long-term pediatric HIV cohort. American Journal of Epidemiology, 167, 1,375-1,386.

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, 939-955.

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.

Zeger, S.L., and Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121-130.

Zhang, G., and Little, R.J.A. (2009). Extensions of the penalized spline of propensity prediction method of imputation. Biometrics, 65, 911-918.

Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Suggested Citation:"References." National Research Council. 2010. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press. doi: 10.17226/12955.
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Next: Appendix A: Clinical Trials: Overview and Terminology »
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Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups.

Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable.

The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.

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