London School of Hygiene and Tropical Medicine logo
Research Developer Initiative logo Economic and Social Research Council logo
Home Bibliography

Bibliography

Welcome to the Bibliography. The Filter box can be used to search for publications according to their Title. Publications can also be restricted by Year and Author using the pull-down menus.

Filter:
 

Year: 2009

  • Fridley, B. L., McDonnell, S. K., Rabe, K. G., Tang, R., Biernacka, J. M., Sinnwell, J. P. et al. (2009). Single versus multiple imputation for genotypic data.. BMC Proc, 3 Suppl 7, S7. [More] [Bibtex]
  • Gallo, P. & Chuang-Stein, C. (2009). A Note on Missing Data in Noninferiority Trials. Drug Information Journal, 43(4), 469-474. [More] [Bibtex]
  • Glance, L. G., Osler, T. M., Mukamel, D. B., Meredith, W. & Dick, A. W. (2009). Impact of Statistical Approaches for Handling Missing Data on Trauma Center Quality. Annals of Surgery, 249(1), 143-148. [More] [Bibtex]
  • Graham, J. W. (2009). Missing Data Analysis: Making It Work in the Real World. Annual Review of Psychology, 60, 549-576. [More] [Bibtex]
  • Greil, R., Miles, D. & Siebert, U. (2009). Quality of Life (qol) In Patients With Metastatic Breast Cancer (mbc) From the Avado Study: the Importance of Imputation Methods For Handling Missing Data. Value In Health, 12(7), A284-A284. [More] [Bibtex]
  • Gunnes, N., Seierstad, T. G., Aamdal, S., Brunsvig, P. F., Jacobsen, A. B., Sundstrom, S. et al. (2009). Assessing quality of life in a randomized clinical trial: Correcting for missing data. Bmc Medical Research Methodology, 9. [More] [Bibtex]
  • Hamer, R. M. & Simpson, P. M. (2009). Dropouts and Missing Data in Psychiatric Clinical Trials Reply. American Journal of Psychiatry, 166(11), 1295-1296. [More] [Bibtex]
  • Hardy, S. E., Allore, H. & Studenski, S. A. (2009). Missing Data: A Special Challenge in Aging Research. Journal of the American Geriatrics Society, 57(4), 722-729. [More] [Bibtex]
  • Harel, O. (2009). The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. Journal of Applied Statistics, 36(10), 1109-1118. [More] [Bibtex]
  • Harel, O. & Schafer, J. L. (2009). Partial and latent ignorability in missing-data problems. Biometrika, 96(1), 37-50. [More] [Bibtex]
  • He, Y. L. & Raghunathan, T. E. (2009). On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions. Communications In Statistics-simulation and Computation, 38(4), 856-883. [More] [Bibtex]
  • Herman, A., Botser, I. B., Tenenbaum, S. & Chechick, A. (2009). Intention-to-Treat Analysis and Accounting for Missing Data in Orthopedic Randomized Clinical Trials. Journal of Bone and Joint Surgery-american Volume, 91A(9), 2137-2143. [More] [Bibtex]
  • Hsieh, S. H., Lee, S. M. & Shen, P. S. (2009). Semiparametric analysis of randomized response data with missing covariates in logistic regression. Computational Statistics & Data Analysis, 53(7), 2673-2692. [More] [Bibtex]
  • Hsu, C. H. & Taylor, J. M. (2009). Nonparametric comparison of two survival functions with dependent censoring via nonparametric multiple imputation. Statistics In Medicine, 28(3), 462-475. [More] [Bibtex]
  • Ibrahim, J. G. & Molenberghs, G. (2009). Missing data methods in longitudinal studies: a review. Test, 18(1), 1-43. [More] [Bibtex]
  • Jelicic, H., Phelps, E. & Lerner, R. A. (2009). Use of Missing Data Methods in Longitudinal Studies: The Persistence of Bad Practices in Developmental Psychology. Developmental Psychology, 45(4), 1195-1199. [More] [Bibtex]
  • Junger, W. & de Leon, A. P. (2009). Missing Data Imputation in Time Series of Air Pollution. Epidemiology, 20(6), S87-S87. [More] [Bibtex]
  • Kim, S., Das, S., Chen, M. H. & Warren, N. (2009). Bayesian Structural Equations Modeling for Ordinal Response Data with Missing Responses and Missing Covariates. Communications In Statistics-theory and Methods, 38(16-17), 2748-2768. [More] [Bibtex]
  • Kohnen, C. N. & Reiter, J. P. (2009). Multiple imputation for combining confidential data owned by two agencies. Journal of the Royal Statistical Society Series A-statistics In Society, 172, 511-528. [More] [Bibtex]
  • Kyureghian, G., Capps, O. & Nayga, R. M. (2009). A Missing Variable Imputation Methodology: Prototype Food Prices Database for Use with the National Health and Nutrition Examination Survey (NHANES) Dietary Intake Data. Journal of Agricultural and Resource Economics, 34(3), 542-542. [More] [Bibtex]
  • Lipsitz, S. R., Fitzmaurice, G. M., Ibrahim, J. G., Sinha, D., Parzen, M. & Lipshultz, S. (2009). Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data. Journal of the Royal Statistical Society Series A-statistics In Society, 172, 3-20. [More] [Bibtex]
  • Liu, R. & Ramakrishnan, V. (2009). Application of Multiple Imputation in Analysis of Data from Clinical Trials with Treatment Related Dropouts. Communications In Statistics-theory and Methods, 38(20), 3666-3677. [More] [Bibtex]
  • Marshall, A., Altman, D. G., Holder, R. L. & Royston, P. (2009). Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. Bmc Medical Research Methodology, 9. [More] [Bibtex]
  • Marshall, A., Billingham, L. J. & Bryan, S. (2009). Can we afford to ignore missing data in cost-effectiveness analyses?. Eur J Health Econ, 10(1), 1-3. [More] [Bibtex]
  • Marshall, G., De la Cruz-Mesia, R., Quintana, F. A. & Baron, A. E. (2009). Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing Data. Biometrics, 65(1), 69-80. [More] [Bibtex]
Results 201 - 225 of 701