Main research interest focuses on statistical methodologies and applications in drug development, including clinical trial designs and analysis methodologies, development decision making, and biomarker strategies.
Extensive Research Description
Hu, M. X. and Liu, Y. (Under Preparation). Critical Statistical Ideas and Implementation Strategies in Drug Development
Hu, M.X. (2016). Multi-Regional Clinical Trials in Oncology Drug Development. Book Chapter in Multi-Regional Clinical Trials for Simultaneous Global New Drug Development. Chapman & Hall/CRC.
Liu, Y. and Hu, M.X. (2016). Testing Multiple Primary Hypotheses in Clinical Trials with Sample Size Adaptive Design. Pharmaceutical Statistics, Vol. 15, No. 1, 37-45.
Hu, M.X., Liu, Y, and Lin, J.C. (2013). Research Topics in Applied Statistics from 2012 Symposium of International Chinese Statistical Association. Springer.
Hu, M. X. and Zhou, T. Y. (2011). Analysis of Missing Mechanism in IVUS Imaging Clinical Trials with Missing Covariates. Journal of Biopharmaceutical Statistics, 21, 282-293.
He, X. M., K. Hsu, and Hu, M. X. (2010). Statistical Tests on Covariate-Adjusted Expected Shortfalls. Annals of Applied Statistics, Vol. 4, No. 4, 2114-2125.
Gilbert, J., Lekstrom-Himes, J., Donaldson, D., Lee, Y., Hu, M. X., Xu, J., Wyant, T., Davidson, M. (2011). Effect of CC Chemokine Receptor 2 CCR2 Blockade on Serum C-Reactive Protein in Individuals at Atherosclerotic Risk and With a Single Nucleotide Polymorphism of the Monocyte Chemoattractant Protein-1 Promoter Region. American Journal of Cardiology, 107, 906-911.
Lenderking, W., Hu, M. X., Tennen, H., Cappelleri, J., Petrie, C. D., Rush, J. (2008) Daily Process Methodology for Measuring Earlier Antidepressant Response. Contemporary Clinical Trials, 29, 867-877.
Hu, M. X., Cappelleri, J., Lan, K. K. G. (2007). Applying the Law of Iterated Logarithm to Control Type I Error in Cumulative Meta-Analysis for Binary Outcomes. Clinical Trials, 4, 329-340.
Sutton, S. C., and Hu, M. X (2006). An Automated Process for Building Reliable and Optimal IVIVC Models Using Monte Carlo Simulations. American Association of Pharmaceutical Scientists Journal, 8 (2), Article 35.
Lan, K.G.G., Hu, M. X., and Cappelleri, J. (2003). Applying the Law of Iterated Logarithm to Cumulative Meta-Analysis for a Continuous Outcome. Statistica Sinica, 13, 1135-1145.
Hu, M.-X. and Lachin, J. M. (2003). Correction for Bias in Maximum Likelihood Parameter Estimates Due to Nuisance Parameters. Communications in Statistics: Simulation and Computation, 32, 619-640.
Trost, DC, Hu, M. X., Brailey, A., Hoffman, J. (2002). The Probability-Based Construction of Reference Ranges for Ratios of Log-Gaussian Analytes: An Example from Automated Leukocyte Counts. American Journal of Clinical Pathology, 117, 851-856.
Rosenberger, W. F. and Hu, M-X. (2002). On the Use of Linear Models Following a Sequential Design. Statistics and Probability Letters, 56, 155-161.
Hu, M.-X. and Lachin, J. M (2001). Application of Robust Estimating Functions to the Analysis of Quantitative Longitudinal Data. Statistics in Medicine, 20, 3411-3428.
Hu, M. X. and Salvucci, S. (2001). A Study of Imputation Algorithm. National Center for Education Statistics Publication # 200117, U. S. Department of Education: Washington, DC.
Hu, M.-X. and Lachin, J. M. (1999). Likelihood-based Approaches for Correcting Bias Caused by Nuisance Parameters. Proceedings from American Statistical Association, Bayesian Statistical Science Section, 108-113
Hu, M.-X., Salvucci, S. and Cohen, M. (1998). Evaluation of Some Popular Imputation Algorithms. Proceedings from American Statistical Association, Survey Methodology Section, 308-313.
Hu, M.-X., Zhang, F., Cohen, M. and Salvucci, S. (1997). On the Performance of Replication-based Variance Estimation Methods with Small Numbers of PSUs. Proceedings from American Statistical Association, Survey Methodology Section, 541-546.
Monaco, D., Salvucci, S., Zhang, F., Hu, M.-X. and Gruber, K. (1997). An Analysis of Total Nonresponse in the 1993-94 Schools and Staffing Survey (SASS). NCES 98-243. U. S. Department of Education, Office of Educational Research and Improvement. Washington, DC: National Center for Education Statistics.
Hu, M.-X., Salvucci, S. and Weng, S. (1996). Evaluation of Proc Impute and Schafer’s Multiple Imputation Software. Proceedings from American Statistical Association, Survey Methodology Section, 287-292.
Salvucci, S., Zhang, F., Hu, M.-X, Monaco, D., Gruber, K. (1996). Nonresponse Analysis of 1993-94 Schools and Staffing Survey. Proceedings from American Statistical Association, Survey Methodology Section, 716-721.
STATISTICAL SOFTWARE DEVELOPED
- IVIVC Modeling SAS Macro: For in vitro/in vivo correlation modeling, by Mingxiu Hu and Steven Sutton. Predictive IVIVC models enable formulation changes without in vivo bioequivalent studies
- REE SAS Macro: For analyzing longitudinal data using robust estimating equations, by Mingxiu Hu and Huaiyu Xiong.
- IRMA (Window Version): For imputing missing data, by Mingxiu Hu, James Cochran, and Michael P. Cohen.
- Imputation-Simulation SAS Macro: For simulation studies to evaluate advanced imputation method and PROC MIXED, by Mingxiu Hu and Quan Hong.