Selected Publications
Engels, E.A., Mandal, S.†, Corley, D.A., Blosser, C.D., Hart, A., Lynch, C.F., Qiao, B., Pawlish, K.S., Haber, G., Yu, K.J., Pfeiffer, R.M. (2025) Cure probabilities and solid organ transplantation for patients with colorectal cancer. American Journal of Transplantation, 25(3), pp. 545–555. [paper] (Selected AJT Editor’s Choice Article, March 2025; featured on the AJT podcast) [† joint first author]
Cahoon, E., Mandal, S., Pfeiffer, R.M., Wheeler, D.C., Sargen, M., Alexander, B.H., Kitahara, C.M., Linet, M.S., Mai, J.Z. (2024) Ambient ultraviolet A, ultraviolet B and risk of melanoma in a nationwide United States cohort, 1984-2014. Journal of the National Cancer Institute, 116(12), pp. 1928--1933. [paper]
Mandal, S., Kim, D. H., Hua, X., Li, S., Shi, J. (2023) Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error. Biostatistics [paper] [Github link for R codes]
Mandal, S., Qin, J., Pfeiffer, R. (2022) Non-parametric estimation of the age-at-onset distribution from a cross-sectional sample. Biometrics [paper] [GitHub link for R codes]
Mandal, S., Qin, J., Pfeiffer, R. (2021) Incorporating survival data into case-control studies with incident and prevalent cases. Statistics in Medicine, 40(28), pp. 6295–6308. [paper] [GitHub link for R codes]
Hu, L., Mandal, S.*, Sinha, S. (2021) A comparative study of two-sample tests for interval-censored data. Journal of Statistical Computation and Simulation, 91(18), pp. 3894–3916. [paper] [* corresponding author]
Mandal, S., Wang, S., Sinha, S. (2019) Analysis of linear transformation models with covariate measurement error and interval censoring. Statistics In Medicine, 38(23), pp. 4642–4655 [paper]
ICEMELT package published on CRAN: This package implements the imputation method proposed in the above paper for interval-censored data. The package can also analyze such data using the regression calibration method and a naive method. All three methods can be implemented using our package for single as well as multiple-surrogate measurement error data. [R package]
Johnson, V.E., Payne, R.D., Wang, T., Asher, A., Mandal, S. (2017) On the reproducibility of psychological science. Journal of the American Statistical Association, 112(517), pp. 1–10. [paper]