Melanie M. Wall

Professor of Biostatistics (in Psychiatry)
Department of Biostatistics, Mailman School of Public Health
and
Director of Mental Health Data Science in the
Department of Psychiatry, Columbia University Irving Medical Center and the
New York State Psychiatric Institute

I am also the director of Mental Health Data Science in the New York State Psychiatric Institute (NYSPI) and Columbia University psychiatry department where I oversee a team of 10 biostatisticians collaborating on predominately NIH funded research projects related to psychiatry.

My biostatistical expertise includes latent variable modeling (e.g. factor analysis, item response theory, latent class models, structural equation modeling) and longitudinal data analysis including the class of longitudinal models commonly called growth curve mixture models. I also have expertise in predictive modeling using machine learning methods including random forest. I received a Ph.D. (1998) from the Department of Statistics at Iowa State University, and a B.S. (1993) in mathematics from Truman State University. Before moving to Columbia University in 2010, I was on faculty in Biostatistics in the School of Public Health at the University of Minnesota.

TEACHING P8158 Latent Variable and Structural Equation Modeling for Health Sciences (Columbia University Dept of Biostatistics) - Syllabus

My full CV is here and below are selected research papers and statistical programs...

• Info about additive interactions
  • Presentation on testing for additive versus multiplicative interactions with dichotomous outcomes, and a presentation Are you looking for the right interactions? by Sharon Schwartz developing the causal theory behind why causal interactions are best represented by additivity.
  • SUDAAN code for testing additive interaction
• Info about suppression effects
  • Friedman L, Wall MM. (2005) "Graphical views of suppression and multicollinearity in multiple linear regression", The American Statistician, vol. 59, no. 2, pp. 127 - 136. Splus program for producing graphs in this paper here prepared by Lynn Friedman.
  • Here is an Interactive Shiny App that allows you to examining suppression and multicollinearity under any scenerio developed by Nicholas Brown from Department of Psychology, Linnaeus University.
• Programs for testing sequences of drug initiation in Wall MM, Cheslack-Postava K, Hu MC, Feng T, Griesler P, Kandel DB. Nonmedical prescription opioids and pathways of drug involvement in the US: Generational differences, Drug and Alcohol Dependence, 182: 103-111. 2017.
• Programs for assessing Total Information in Item Response Theory Modeling
• Papers on latent variable models including nonlinear structural equation models
  • Wall MM, Park JY, Moustaki I. (2015). IRT modeling in the presence of zero-inflation with applicaiton to psychiatric disorder severity. Applied Psychological Measurement 39(8): 583-597.
  • Wall, M.M., Guo, J., Amemiya, Y. (2012). Mixture factor analysis for approximating a non-normally distributed continuous latent factor with continuous and dichotomous observed variables. Multivariate Behavioral Research, 47:276-313. Explanation of Mplus program for Mixture Factor Analysis, Mplus .out file for Mixture Factor Model 4class result in Table 6, Data for Numerical Example fit in Table 6
  • Wall, M.M. and Li, Ran (2009) Multiple indicator hidden Markov model with an application to medical utilization data. Statistics in Medicine, 28(2): 293-310.
  • Guo, J. Wall, M.M., and Amemiya, Y. (2006) "Latent class regression on latent factors", Biostatistis, 7, 1, pp. 145-163.
  • Wall, M.M. and Li, Ruifeng (2003) "A Comparison of Multiple Regression to Two Latent Variable Techniques for Estimation and Prediction", Statistics in Medicine, 22, 3671-3685. SAS programs for performing analysis in this paper here
  • Nonlinear SEM Papers below and Nonlinear SEM Programs HERE
    • Wall, M.M. and Amemiya, Y, (2000) "Estimation for polynomial structural equation models". JASA, 95, 929-940.
    • Wall, M.M. and Amemiya, Y, (2001) "Generalized appended product indicator procedure for nonlinear structural equation analysis". Journal of Educational and Behavioral Statistics, 26, 1-29.
    • Wall, M.M. and Amemiya, Y, (2003) "A method of moments technique for fitting interaction effects in structural equation models", British Journal of Mathematical and Statistical Psychology, 56, 47-64.
    • Wall M.M. and Amemiya, Y. (2007) "A review of nonlinear factor analysis and nonlinear structural equation modeling" In Factor Analysis at 100: Historical Developments and Future Directions, eds. Robert Cudeck and Robert C. MacCallum, Chapter 16 pp 337-362, Lawrence Erlbaum Associates.
    • Wall M.M. and Amemiya, Y. (2007) "Nonlinear structural equation modeling as a statistical method" In Handbook of Latent Variable and related Models, ed Sik-Yum Lee, Chapter 15, 321-344, Elsevier, The Netherlands.
    • Wall MM (2009) Maximum likelihood and Bayesian estimation for nonlinear structural equation models, In the Handbook of Quantitative Methods in Psychology eds Roger Millsap and Albert Maydeu-Olivares, Chapter 22, 540-567, Sage.
• Papers on spatial data modelling
  • Wang, F., and Wall, M.M. (2003) "Generalized Common Spatial Factor Model" Biostatistics 4(4), 569-582.
  • Wang, F. and Wall, M.M. "Modelling multivariate data with a common spatial factor" Research Report No. 2001-008, Division of Biostatistics, University of Minnesota, Minneapolis, MN.
  • Wang, F., and Wall, M.M. (2003) "Incorporating Parameter Uncertainty into Prediction Intervals for Spatial Data Modeled via a Parametric Variogram", JABES, 8, Vol. 3., 1-14.
  • Banerjee, S., Wall, M.M., and Carlin, B.P. (2003) "Frailty Modeling for Spatially Correlated Survival Data, with Application to Infant Mortality in Minnesota", Biostatistics, 4, 123-143.
  • Wall, M.M. (2004) "A close look at the spatial correlation structure implied by the CAR and SAR models" Journal of Statsitical Planning and Inference Vol 121, 2, 311-324.
  • Zhao, Y. and Wall, M.M. (2004) ``Investigating the use of the variogram for lattice data'' Journal of Computational and Graphical Statistics, 13(3) , 1-20.
  • Liu X, Wall MM, Hodges JS (2005) "Generalized spatial structural equation modeling" Biostatistics, 6: 539-557.
  • Wall MM and Liu X (2009) ``Spatial Latent Class Analysis Model for Spatially Distributed Multivariate Binary Data", Computational Statistics and Data Analysis, 53, 3057-3069.
  • Wall MM (2012). Spatial structural equation modeling with an application to U.S. behavioral risk factor surveillance survey data. In the Handbook of Structural Equation Modeling Chapter 39, 674-689, ed Rick Hoyle, Guilford Press.
• Other statistical methodology papers
  • Wall, M.M., Boen, J. and Tweedie, R. L. (2001) "An effective CI for the mean with samples of size 1 and 2", The American Statistician, 55, No. 2, 102-105.
  • Pan, W. and Wall, M.M. (2002) "Small-Sample Adjustments in Using the Sandwich Variance Estimator in Generalized Estimating Equations" Statistics in Medicine, 21, No. 10, 1429-1441.
  • Wall, M.M. (2004) "Adjusting SIDS rates by seasonality in births in Minnesota", Statistics in Medicine, 23(13): 2037-2048.
  • Wall, M.M., Dai, Y., Eberly, L.E. (2005) "GEE Estimation of a Mis-specified Time-varying Covariate in Poisson Regression with Many Observations" Statistics in Medicine, 24:925-939.