Mushfiquee M and Rahman MS : Propensity score-based adjustment for covariate effects on classification accuracy of bio-marker using ROC curve, Biostatistics & Epidemiology , 2022 .

Alam TF, Rahman MS and Bari W : On estimation for accelerated failure time models with small or rare event survival data, BMC Medical Research Methodology , 2022 .

Mondol MH, Rahman MS and Bari W : A penalized likelihood approach for dealing with separation in count data regression model, Communications in Statistics - Simulation and Computation Taylor & Francis , 2022 .

MH Mondol and MS Rahman : Bias‐reduced and separation‐proof GEE with small or sparse longitudinal binary data, Statistics in Medicine , vol.38 , no.14 , pp.2544-2560 , 2019 .

MS Rahman and AS Rumana : A model-based concordance-type index for evaluating the added predictive ability of novel risk factors and markers in the logistic regression models, Journal of Applied Statistics , vol.46 , no.2 , pp.2145-2163 , 2019 .

MS Rahman, M Mushfiquee, MS Masud MS and T Howlader : Association between malnutrition and anemia in under-five children and women of reproductive age: Evidence from Bangladesh Demographic and Health Survey 2011, PLoS one , 2019 .

MN Mia, MZ Islam, MR Chowdhury, A Razzaque, B Chin and MS Rahman : Socio-demographic, health and institutional determinants of caesarean section among the poorest segment of the urban population: Evidence form selected slums in Dhaka, Bangladesh, SSM-Population Health , vol.8 , no.100415 , 2019 .

PK Roy, HRK Khan, T Akter and MS Rahman : Exploring socio-demographic-and geographical-variations in prevalence of diabetes and hypertension in Bangladesh: Bayesian spatial analysis of national health survey data, Spatial and Spatio-Temporal Epidemiology , vol.29 , pp.71-82 , 2019 .

MN Mia, Z Islam, MR Chowdhury, A Razzaque, B Chin and MS Rahman : Socio-demographic, health and institutional determinants of caesarean section among the poorest segment of the urban population: Evidence form selected slums in Dhaka, Bangladesh, SSM- Population Health , 2019 .

MJ Bhuyan, MA Islam and MS Rahman : A bivariate Bernoulli model for analyzing malnutrition data, Health Services Outcomes Research Methodology , vol.18 , no.2 , pp.109-127 , 2018 .

MN Mia, MS Rahman and PK Roy : Sociodemographic and geographical inequalities in under- and overnutrition among children and mothers in Bangladesh: a spatial modelling approach to a nationally representative, Public Health nutrition , vol.21 , no.13 , pp.2471-2481 , 2018 .

RA Collis, MS Rahman, O Watkinson, OP Guttmann, C O'Mahony and PM Elliott : Outcomes following the surgical management of left ventricular outflow tract obstruction; A systematic review and meta-analysis, International journal of cardiology , vol.265 , pp.62-70 , 2018 .

T Akter, EB Sarker and MS Rahman : A tutorial on GEE with applications to diabetes and hypertension data from a complex survey, Journal of Biomedical Analytics , vol.1 , no.37-50 , pp.1 , 2018 .

F Khatun, AE Heywood, SMA Hanifi, MS Rahman, PK Ray, ST Liaw and A Bhuiya : Gender differentials in readiness and use of mHealth services in a rural area of Bangladesh, BMC health services research , vol.17 , no.1 , 2017 .

MS Rahman and M Sultana : Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data, BMC medical research methodology , vol.17 , no.1 , pp.33 , 2017 .

MS Rahman, SM Hanifi, F Khatun, M Iqbal, S Rasheed, T Ahmed, S Haque, T Sharmin, NZ Khan, SS Mahmood and A Bhuiya : Knowledge, attitudes and intention regarding mHealth in generation Y: evidence from a population based cross sectional study in Chakaria, Bangladesh, BMJ Open , vol.7 , no.11 , 2017 .

MS Rahman, G Ambler, B Choodari-Oskooei and RZ Omar : Review and evaluation of performance measures for survival prediction models in external validation settings, BMC Medical Research Methodology , vol.17 , pp.60 , 2017 .

MH Mondol and MS Rahman : A comparison of internal validation methods for validating predictive models for binary data with rare events, Journal of Statistical Research , vol.51 , no.2 , pp.131-144 , 2017 .

MN Mia, SMA Hanifi, MS Rahman, A Sultana, S Hoque and A Bhuiya : Prevalence, pattern and sociodemographic differentials in smokeless tobacco consumption in Bangladesh: evidence from a population-based cross-sectional study in Chakaria, BMJ Open , vol.7 , no.1 , 2017 .

MS Rahman, T Howlader, MS Masud and ML Rahman : Association of low-birth weight with malnutrition in children under five years in Bangladesh: do mother’s education, socio-economic status, and birth interval matter?, PLOS One , vol.11 , no.6 , 2016 .

V Patel, C O'Mahony, D Hyghes, MS Rahman, C Coats, E Murphy, R Lachmann, A Mehta and PM Elliott : Clinical and genetic predictors of major cardiac events in patients with Anderson–Fabry Disease, Heart , vol.101 , no.12 , 2015 .

Fatema Khatun mail, SMA. Hanifi, Mohammad Iqbal, Sabrina Rasheed, M. Shafiqur Rahman, Tanvir Ahmed, Shahidul Hoque, Tamanna Sharmin, Nazib Uz Zaman Khan, Shehrin Shaila Mahmood, David H. Peters, SMA Hanifi, M Iqbal, S Rasheed, MS Rahman, T Ahmed, S Haque, T Sharmin, NZ Khan, SS Mahmood, DH Peters and A Bhuiya : Prospects of mHealth Services in Bangladesh: Recent Evidence from Chakaria, PLOS ONE , 2014 .

Oliver P Guttmann, M Shafiqur Rahman, Constantinos O'Mahony, Aris Anastasakis, Perry M Elliott, MS Rahman, C O'Mahony, A Anastasakis and PM Elliott : Atrial fibrillation and thromboembolism in patients with hypertrophic cardiomyopathy: systematic review, Heart , vol.100 , no.6 , 2014 .

LR Lopes, MS Rahman and PM Elliott : A systematic review and meta-analysis of genotype–phenotype associations in patients with hypertrophic cardiomyopathy caused by sarcomeric protein mutations, Heart , vol.99 , no.24 , 2013 .

Adhikary A. and Rahman MS : Firth's penalized method in Cox proportional hazard framework for developing predictive models for sparse or heavily censored survival data, Journal of Statistical Computation and Simulation , vol.91 , no.3 Taylor & Francis , pp.445-463 , 2021 .

Nusrat N and Rahman MS : Dealing with separation or near-to-separation in the model for multinomial response with application to childhood health seeking behavior data from a complex survey, Journal of Applied Statistics Taylor & Francis , 2021 .