Department of Biostatistics, Mailman School of Public Health

and

Director of Mental Health Data Science in the

Department of Psychiatry, Columbia University Medical Center and the

New York State Psychiatric Institute

I am 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 13 biostatisticians collaborating on predominately NIH funded research projects related to psychiatry. I have worked extensively with modeling complex multilevel and multimodal data on a wide array of psychosocial public health and psychiatric research questions in both clinical studies and large epidemiologic studies (over 260 total journal publications). My biostatistical expertise includes latent variable modeling (e.g. factor analysis, item response theory, latent class models, structural equation modeling), spatial data modeling (e.g. disease mapping), and longitudinal data analysis including the class of longitudinal models commonly called growth curve mixture models. 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. Link to my blog about Mental Health Data Science.

**TEACHING P8158** Latent Variable and Structural Equation Modeling for Health Sciences (Columbia University Dept of Biostatistics - Spring 2018) -
Syllabus

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

- 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.
- 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

- Papers examining associations between marijuana use and state medical marijuana laws
- Wall M, Mauro C, Hasin D, Keyes K, Cerda M, Martins S, Feng T. Prevalence of marijuana use does not differentially increase among youth after states pass medical marijuana laws: Commentary on Stolzenberg et al (2015) and reanalysis of US National Survey on Drug Use in Households data 2002-2011.
*International Journal of Drug Policy*, 29, 9-13. 2016. - Hasin D, Wall M, Keyes K, Cerda M, Galea S, Schulenberg J, Feng T, OMalley P. (2015) Medical marijuana laws and adolescent marijuana use in the USA from 1991 to 2014: results from annual, repeated cross-sectional surveys.
*Lancet Psychiatry*, 2(7), 601-8. - Cerda M, Wall MM, Keyes K, Galea S, Hasin D. (2012) Medical marijuana laws in 50 states: investigating the relationship between state legalization of medical marijuana and marijuana use, abuse and dependence.
*Drug and Alcohol Dependence*, 120(1-3):22-7. - Wall MM, Poh E, Cerda M, Keyes K, Galea S, Hasin D. (2012) Commentary on Harper S, Strumpf EC, and Kaufman JS, "Do Medical Marijuana Laws Increase Marijuana Use? Replication study and extension". Annals of Epidemiology, 22(7), 537-7.
- Wall MM, Poh E, Cerda M, Keyes K, Galea S, Hasin D. (2011) Adolescent marijuana use from 2002 to 2008: higher in states with medical marijuana laws, cause still unclear.
*Annals of Epidemiology*, 21:714-716. NDSUH data and SAS programs used for analysis

- Wall M, Mauro C, Hasin D, Keyes K, Cerda M, Martins S, Feng T. Prevalence of marijuana use does not differentially increase among youth after states pass medical marijuana laws: Commentary on Stolzenberg et al (2015) and reanalysis of US National Survey on Drug Use in Households data 2002-2011.
- Programs for 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.

- Wall, M.M. and Amemiya, Y, (2000) "Estimation for polynomial
structural equation models".

- Wall MM, Park JY, Moustaki I. (2015). IRT modeling in the presence of zero-inflation with applicaiton to psychiatric disorder severity.
- 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.

- Wang, F., and Wall, M.M. (2003)
"Generalized Common Spatial Factor Model"
- 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. - 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.

- Wall, M.M., Boen, J. and Tweedie, R. L. (2001) "An effective CI
for the mean with samples of size 1 and 2",

Melanie M. Wall

email: mmw2177@cumc.columbia.edu

phone: (646)774-5458

1051 Riverside Drive

Unit 48

New York, NY 10032

Allan Rosenfield Building

722 West 168th Street - R207

New York, NY 10032