Selected Publications

The list below contains mostly biostatistical methods papers and those where I was a lead author. A complete List of publications can be seen in my CV.

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\(^s\)Student Author. \(^c\)Corresponding Author. \(^\ast\)Dual first-authors. \(^\divideontimes\)Senior Author.

1. Guo\(^s\) S, J Zhang, AC McLain\(^{\divideontimes c}\) (2024+). Joint modeling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment. Journal of the Royal Statistical Society: Series C, (available online).

2. Zhang\(^s\) S, AC McLain\(^{\divideontimes c}\), J Habiger, C Rorden, and J Fridriksson (2024). False Discovery Rate Control for Lesion Symptom Mapping with Heterogeneous data via Weighted P-values. Biometrical Journal, 66:6, e202300198. Link to R code.

3. Huang\(^s\), R, AC McLain, B Herrin, M Nolan, B Cai and S Self (2024+). Bayesian Group Testing Regression Models for Spatial Data.Spatio and Spatio-Temporal Epidemiology (available online).

4. Guo\(^s\), S, Y Wu, AC McLain, JW Hardin, B Olatosi, X Li, and J Zhang (2024). Functional multi-variable logistic regression with an application to HIV viral suppression prediction. Biometrical Journal (accepted).

5. Self S, X Zhao\(^s\), A Zgodic\(^s\), A Overby, D White, AC McLain\(^{\divideontimes}\), and C Dyckman (2024). A Hypothesis Test for Detecting Spatial Patterns in Categorical Areal Data. Spatial Statistics (available online).

6. Olejua\(^s\) P, AC McLain\(^{\divideontimes c}\), N Inak, M Dowda, and RR Pate (2024). Clustering patterns of 24-hour physical activity in children 6 to 36 months old. Pediatric Exercise Science (available online).

7. Bradshaw J, JM Eberth, A Zgodic, A Federico, K Flory, AC McLain\(^{\divideontimes c}\) (2024). County-Level Prevalence Estimates of Autism Spectrum Disorder in Children in the United States. Journal of Autism and Developmental Disorders 54, 2710–2718..

8. Self S, A Overby, A Zgodic\(^s\), D White, AC McLain\(^{\divideontimes}\), and C Dyckman (2023). A hypothesis test for detecting distance-specific clustering and dispersion in areal data. Spatial Statistics 55, 100757.

9. Zgodic\(^{s c}\) A, AC McLain\(^{\divideontimes}\), 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.

10. Hong\(^s\), Y, JM Eberth, B Cai, and AC McLain\(^{\divideontimes c}\) (2022). Estimating confidence intervals for spatial hierarchical mixed-effects models with post-stratification. Spatial Statistics 51, 100670.

    • Editorial published by the Journal of Nutrition and blog post by the American Society for Nutrition, discussing the results.

11. Saraswati\(^s\), CM, E Borghi, JJ Rodrigues da Silva Breda, CM Flores-Urrutia, J Williams, C Hayashi, EA Frongillo, and AC McLain\(^{\divideontimes}\) (2022). Estimating childhood stunting and overweight trends in the European region from sparse longitudinal data. Journal of Nutrition 150:7, 1773–1782.

12. Hess\(^\ast\), SY, AC McLain\(^\ast\), H Lescinsky, KH Brown A Afshin, R Atkin, and SJM 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.

13. Luo\(^s\), X, G Cai, AC McLain, C I Amos, B Cai, and F Xiao (2022) BMI-CNV: a Bayesian framework for multiple genotyping platforms detection of copy number variation. Genetics 222:4, iyac147.

14. Self, SCW, R Huang, S Amin, J Ewing, C Rudisill, and AC McLain\(^{\divideontimes}\) (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.

15. McLain\(^ c\), AC, S Guo, ME 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. Link to code.

16. Zgodic\(^s\), A, J Eberth, C Brennen, ME Wende, EW Stowe, AT Kaczynski, AD Liese and AC McLain\(^{\divideontimes c}\) (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.

17. Hess\(^\ast\), SY, AC McLain\(^\ast\), EA Frongillo, A Afshin, NJ Kassebaum, SJM Osendarp, R Atkin, R Rawat, and KH 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.

18. Zhou\(^s\), J, J Zhang, AC McLain, W Lu, X Sui, and JW 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.

19. McLain\(^{ c}\) AC, EA 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.

20. McLain, AC, EA Frongillo, SY 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.

21. Zhang\(^s\) Y, AC McLain\(^ c\), B Davis, S McDermott (2019). Fecundity and infertility among women with disability in the United States. Journal of Women’s Health 28:7, 934–940.

22. Zhou\(^s\), J, AC McLain, W Lu, X Sui, JW 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.

23. Eberth\(^\ast\) JM, AC McLain\(^\ast\), Y Hong\(^s\), 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.

24. Geraci, M, and AC McLain (2018). Multiple imputation for bounded variables. Psychometrika 83:4 919–940.

25. Polis, CB, CM Cox, "{O} Tunalp, AC McLain, ME 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.

    • post by Global Health Now, post by University of Maryland School of Public Health and post by the University of South Carolina Arnold School of Public Health.
    • Honorable mention for the Winners of the Untold Global Health Stories of 2019.

26. McLain\(^ c\) AC, R Sundaram, and GM 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.

27. Mulatya\(^s\) CM, AC McLain\(^{\divideontimes c}\), B Cai, JW Hardin, PS Albert (2016). Estimating time to event characteristics via longitudinal threshold regression models – an application to cervical dilation progression. Statistics in Medicine 35, 4368–4379.

28. McMahan, C, AC McLain, C Gallagher, and E Schisterman (2016). Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments. Biomedical Journal 58:4, 944–961.

29. Zhou\(^s\), J, J Zhang, AC McLain and B Cai (2016) A multiple imputation approach for semiparametric cure model with interval censored data. Computational Statistics and Data Analysis 99, 105–114.

30. Cipolli\(^s\), W, T Hanson, and AC McLain (2016). Bayesian Nonparametric Multiple Testing. Computational Statistics and Data Analysis, 101, 64–79.

31. McLain AC, R Sundaram, and GM 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.

32. Cummings\(^s\), T H, J W Hardin, AC McLain, J R Hussey, K J Bennett and G M Wingood (2015). Modeling Heaped Count Data. The Stata Journal 15, 457–479.

33. Sapra\(^s\), KJ, AC McLain, JM Maisog, R Sundaram, GM Buck Louis (2015). Clustering of retrospectively reported and prospectively observed time-to-pregnancy among women in a preconception cohort. Annals of Epidemiology 25, 959–963.

34. McLain\(^ c\) AC, R Sundaram, M E Thoma, and G M Buck Louis (2014). Semiparametric modeling of grouped current duration data with preferential reporting. Statistics in Medicine, 33:23, 3961–3972.

    • Featured article of the October 2014 issue of Statistics in Medicine.

35. McLain\(^ c\) AC and Albert P (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.

36. McLain\(^ c\) AC and Ghosh S (2013). Efficient sieve maximum likelihood estimation of time-transformation models. Journal of Statistical Theory and Practice, 7, 285–303.

37. Thoma ME, AC McLain, JF Louis, RB King, AC Trumble, R Sundaram, and GM 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.

    • Link to 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).

38. Albert, P, R Sundaram, and AC McLain (2013). Innovative applications of shared random parameter models for analyzing longitudinal data subject to dropout. In ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers, pp. 139–156. Springer New York.

39. McLain\(^ c\) AC, K Lum, and R Sundaram (2012). A joint mixed effects dispersion model for menstrual cycle length and time-to-pregnancy. Biometrics 68, 648–656.

40. Sun W, and AC McLain (2012). Multiple testing of composite null hypotheses in heteroscedastic models. Journal of the American Statistical Association 107, 673–687.

41. Sundaram R, AC McLain, and G M Buck Louis (2012). A survival analysis approach to modeling human fecundity. Biostatistics 13, 4–17.

42. McLain\(^ c\) AC, R Sundaram, M Cooney, A Gollenberg, and G Buck Louis (2011). Clustering of fecundability within women. Paediatric and Perinatal Epidemiology 25, 460–465.

43. McLain\(^ c\) AC and Ghosh S (2011). Nonparametric estimation of the conditional mean residual life function with censored data. Lifetime Data Analysis 17, 514–532.

In Progress

• Guo\(^s\), S, J Zhang, and AC McLain\(^{\divideontimes c}\). Joint modeling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment. Revision submitted to the Journal of the Royal Statistical Society: Series C.

• Zgodic\(^s\), A, R Bai, J Zhang, and AC McLain\(^{\divideontimes c}\). Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm. In revision for the Journal of Computational and Graphical Statistics. Link to lmmprobe R package.

  • 2023 ENAR Distinguished Student Paper Award Winner.

• McLain\(^c\), AC, A Zgodic\(^s\), H Bondell. Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm. Submitted to the Journal of Computational and Graphical Statistics. Link to probe R package.

• Zgodic\(^s\), A, R Bai, J Zhang, Y Wang, and AC McLain\(^{\divideontimes c}\). Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression. Submitted to Statistics in Medicine.

• Chen\(^s\), S, J Zhang, J Zhou, AC McLain, and J Hardin. Semiparametric Estimations Methods for the Generalized Odds Rate Cure Model with Background Mortality with an application to Breast Cancer. Revision submitted to Statistical Methods in Medical Research.

• Huang\(^s\), R, C McMahan, B Herrin, AC McLain, B Cai and S Self. {Gradient Boosting: A Computationally Efficient Alternative to Markov Chain Monte Carlo Sampling for Fitting Large Bayesian Spatio-Temporal Binomial Regression Models}. Revision submitted to Infectious Disease Modeling.