Selected Publications
For a full, up-to-date list of publications and funding, please see my CV.
Clustered Data Analysis
Self S, A Overby, A Zgodic†, D White, AC McLain‡, and C Dyckman (2023). A hypothesis test for detecting distance-specific clustering and dispersion in areal data. Spatial Statistics 55, 100757.
Bradshaw J, JM Eberth, A Zgodic, A Federico, K Flory, AC McLain‡ (2023). County-Level Prevalence Estimates of Autism Spectrum Disorder in Children in the United States. Journal of Autism and Developmental Disorders (available online).
Zgodic** A, AC McLain‡, JM Eberth, A Federico, J Bradshaw, K Flory (2023). County-Level Prevalence Estimates of ADHD in Children in the United States. Annals of Epidemiology 79, 56– 64.
Hong**, Y, JM Eberth, B Cai, and AC McLain⋇‡ (2022). Estimating confidence intervals for spatial hierarchical mixed-effects models with post-stratification. Spatial Statistics 51, 100670.
Self, S.C.W., R. Huang, S. Amin, J. Ewing, C. Rudisill, and A.C. McLain‡ (2022). A Bayesian Susceptible- Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System. PLOS ONE 17:12, e0260595.
Zgodic**, A., J. Eberth, C. Brennen, M.E. Wende, E.W. Stowe, A.T. Kaczynski, A.D. Liese and A.C. McLain‡ (2021). Estimates of Childhood Overweight and Obesity at the Region, State, and County Levels: A Multilevel Small Area Estimation Approach. American Journal of Epidemiology 190:12, 2618–2629.
Hong**, Y., J.M. Eberth, B. Cai, and A.C. McLain‡ (2022). Estimating confidence intervals for spatial hierarchical mixed-effects models with post-stratification. Spatial Statistics 51, 100670.
McLain A.C., E.A. Frongillo, E. Borghi, and J. Feng (2019). Prediction intervals for heterogeneous penalized longitudinal models with multi-source summary measures: an application to estimating child malnutrition rates. Statistics in Medicine 38:1 1002–1012. Link to R code.
Analytic file with code and data.
Mulatyay** C.M., A.C. McLain, B. Cai, J.W. Hardin, P.S. Albert (2016). Estimating time to event characteristics via longitudinal threshold regression models – an application to cervical dilation progression. Statistics in Medicine 35: 4368–4379.
McLain A. C., R. Sundaram, and G. M. Buck Louis (2015). Joint analysis of longitudinal and survival data measured on nested time-scales using shared parameter models: an application to fecundity data. Journal of the Royal Statistical Society: Series C 64, 339–357.
McLain A. C. and P. Albert (2014). A random-effects model for longitudinal data with random change-point and no time zero: an application to modeling and prediction of individualized labor curves. Biometrics 70 (4), 1052–1060.
Albert, P., R. Sundaram, and A. C. McLain (2013). Innovative applications of shared random parameter models for analyzing longitudinal data subject to dropout. In B. C. Sutradhar (Ed.), ISS- 2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers, Lecture Notes in Statistics, pp. 139–156. Springer New York.
McLain A. C., K. Lum, and R. Sundaram (2012). A joint mixed effects dispersion model for menstrual cycle length and time-to-pregnancy. Biometrics 68, 648–656.
Survival Analysis
McLain, A.C., S. Guo, M.E. Thoma, and J. Zhang (2021). Length-biased semicompeting risks models for cross-sectional data: An application to current duration of pregnancy attempt data. Annals of Applied Statistics 15:2, 1054–1067.
Zhou**, J., J. Zhang, A.C. McLain, W. Lu, X. Sui, and J.W. Hardin (2020). Semiparametric Regression of the Illness-Death Model with Interval Censored Disease Incidence Time: an Application to the ACLS Data. Statistical Methods in Medical Research 29:12, 3707-3720.
Zhou, J, A.C. McLain, W. Lu, X. Sui, J.W. Hardin, and J. Zhang (2019). A Varying-Coefficient Generalized Odds Rate Model with Time-Varying Exposure: An Application to Fitness and CVD Mortality. Biometrics 75:3 853–863.
McLain A. C., R. Sundaram, and G. M. Buck Louis (2016). Modeling fecundity in the presence of a sterile fraction using a semi-parametric transformation model for grouped survival data. Statistical Methods in Medical Research 25, 22–36.
Zhou J., J. Zhang, A.C. McLain and B. Cai (2016). Multiple imputation approach for semiparametric cure model with interval-censored data. Computational Statistics and Data Analysis 99, 105–114.
McLain A. C., R. Sundaram, M. E. Thoma, and G. M. Buck Louis (2014). Semi-parametric grouped backward recurrence Cox model for the analysis of current duration data with preferential reporting. Statistics in Medicine, 33 (23), 3961–3972. (Featured Article).
Analytic file with code and data. Please also see this cautionary note on using this method with censoring.
McLain A. C. and S. Ghosh (2013). Efficient sieve maximum likelihood estimation of time transformation models. Journal of Statistical Theory and Practice, 7, 285–303.
Sundaram R., A. C. McLain, and G. M. Buck Louis (2012). A survival analysis approach to modeling human fecundity. Biostatistics 13, 4–17.
McLain A. C., R. Sundaram, M. Cooney, A. Gollenberg, and G. Buck Louis (2011). Clustering of fecundability within women. Paediatric and Perinatal Epidemiology 25, 460–465.
McLain A. C. and S. Ghosh (2011). Nonparametric estimation of the conditional mean residual life function with censored data. Lifetime Data Analysis 17, 514–532.
High-Dimensional Analysis
Zheng**, S., McLain‡, A. C., Habiger, J., Rorden, C., & Fridriksson, J. (2023). False Discovery Rate Control for Lesion-Symptom Mapping with Heterogeneous data via Weighted P-values. ArXiv. /abs/2308.08364. Link to R code.
McLain, A. C., Zgodic, A., & Bondell, H. (2022). Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm. ArXiv. /abs/2209.08139. Link to R package.
Luo†, X., G. Cai, A.C. McLain, C. I. Amos, B. Cai, and F. Xiao. BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variation. Genetics 222:4, iyac147.
Cipolli† W., T. Hanson, and A.C. McLain (2016). Bayesian Nonparametric Multiple Testing. Computational Statistics and Data Analysis, 101, 64–79.
Sun W., and A. C. McLain (2012). Multiple testing of composite null hypotheses in heteroscedastic models. Journal of the American Statistical Association 107, 673–687.
Others
Kristinsson†, S, A Basilakos, DB den Ouden, C Cassarly, LA Spell, L Bonilha, C Rorden, AE Hillis, G Hickok, L Johnson, N Hetherington, N Busby, GM Walker, AC McLain, and J Fridriksson (2023). Predicting Outcomes of Language Rehabilitation: Prognostic Factors for Immediate and Long-Term Outcomes After Aphasia Therapy. Journal of Speech, Language, and Hearing Research 24, 1–7.
Saraswati**, C.M., E. Borghi, J.J. Rodrigues da Silva Breda, C.M. Flores-Urrutia, J. Williams, C. Hayashi, E.A. Frongillo, and A.C. McLain‡ (2022). Estimating childhood stunting and overweight trends in the European region from sparse longitudinal data. Journal of Nutrition 150:7, 1773–1782.
Editorial published by the Journal of Nutrition on the study, which was highlighted by the American Society for Nutrition.
Hess∗, S.Y., A.C. McLain∗, H. Lescinsky, K.H. Brown A. Afshin, R. Atkin, and S.J.M. Osendarp (2022). Basis for changes for global burden of disease estimates related to vitamin A and zinc deficiency in the GBD 2017 and 2019 Studies. Public Health Nutrition 25:8, 2225–2231.
Hess∗, S.Y., A.C. McLain∗, E.A. Frongillo, A. Afshin, N.J. Kassebaum, S.J.M. Osendarp, R. Atkin, R. Rawat, and K.H. Brown (2021). Challenges for estimating the global prevalence of micronutrient deficiencies and related disease burden: A case study of the Global Burden of Disease Study. Current Developments in Nutrition 5:12, nzab141.
McLain, A.C., E.A. Frongillo, S.Y. Hess, E. Piwoz (2019). Comparison of methods used to estimate the global burden of disease related to undernutrition and suboptimal breastfeeding. Advances in Nutrition 10:3 380–390.
Geraci, M., and A.C. McLain (2018). Multiple imputation for bounded variables. Psychometrika 83:4 919--940.
Eberth* J.M., A.C. McLain* , Y. Hongy, E. Sercy, A. Diedhiou, D. J. Kilpatrick (2018). Estimating county-level tobacco use and exposure in South Carolina: a spatial model-based small area estimation approach. Annals of Epidemiology 28:7 481–488.e4. Link.
McMahan C., A.C. McLain, C. Gallagher, and E. Schisterman (2016). Regression analysis of pooled biomarkers. Biomedical Journal 58:4, 944–961.
Cummings T. H., J. W. Hardin, A. C. McLain, J. R. Hussey, K. J. Bennett and G. M. Wingood (2015). Modeling Heaped Count Data. The Stata Journal 15, 457–479.
Applied Research with Media Coverage
Bornstein, D., G. Grieve, M. Clennin, A.C. McLain, L. Whitsel, M. Beets, K. Hauret, B. Jones, M. Sarzynski (2019). Which U.S. states pose the greatest threats to military readiness and public health? Public health policy implications for a cross-sectional investigation of cardiorespiratory fitness and injuries among U.S. Army Recruits. Journal of Public Health Management and Practice 25:1 36–44.
Polis C.B., C.M. Cox, Ö. Tunçalp, A.C. McLain, M.E. Thoma (2017). Estimating infertility prevalence in low-to-middle-income countries: An application of a current duration approach to Demographic and Health Survey data. Human Reproduction, 32(5), 1064–1074.
Thoma M. E., A. C. McLain, J. F. Louis, R. B. King, A. C. Trumble, R. Sundaram, and G. M. Buck Louis (2013). The prevalence of infertility in the United States as estimated by the current duration approach and a traditional construct approach. Fertility and Sterility, 99, 1324–1331.
Corresponding news media on Reuters or Mommyish.com discussing the results.
Recognized in November 2017 as one of the most highly cited articles in Fertility and Sterility since 2012 (link).
**Student first author, *Dual-first authors, ‡ Senior author.