Dr. M. Shafiqur Rahman
- Risk prediction models
- Causal inference
- Modeling clustered/longitudinal data
- Medical statistics and public health
Methodological Research Papers:
*indicates student supervised paper
- Mondal MH, Rahman MS, Bari W (2022). A penalized likelihood approach for dealing with separation in count data regression model. Communication in Statistics: Simulation and Computation.
- *Nusrat N, Rahman MS (2021). 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.
- *Adhikary A, Rahman MS (2020). 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 (3).
- *Mondol MH, Rahman MS (2019). Bias reduced and separation-proof GEE with small or sparse longitudinal binary data. Statistics in Medicine, Vol 38(19):2544-2560.
- Rahman MS, *Rumana AS (2019). 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(12):2145-2163.
- Roy PK, Khan MHR, Akter T and Rahman MS (2019). 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 29:71-83.
- *Bhuyan MJ, Islam MA, Rahman MS (2018). A bivariate Bernoulli model for analyzing malnutrition data. Health Services and Outcomes Research Methodology, Vol 18(2).
- Rahman MS, Ambler G, Choodari-Oskooei B, Omar R (2017). Review and evaluation of performance measures for survival prediction models in external validation settings. BMC Medical Research Methodology, 17:60.
- Rahman MS, *Sultana M (2017). Performance of Firth-and log F-type penalized methods in risk prediction for small or sparse binary data. BMC Medical Research Methodology, 17:33.
- *Mondol MH, Rahman MS (2017). A comparison of internal validation methods for validating predictive models for binary data with rare events. Journal of Statistical Research, Vol 51(2).
- Rahman MS (2013). Estimating vaccine efficacy under the heterogeneity of vaccine action in a non-randomly mixing population. Journal of Biopharmaceutical Statistics, 23(2):394-412.
- Rahman MS, Islam MA (2007). Markov structure based logistic regression for repeated measures: An application to Diabetes Mellitus Data. Statistical Methodology, 4(4):448-460.
Multidisciplinary Research Papers:
- Mia MN, Rahman MS, Roy PK (2018). Sociodemographic and geographical inequalities in under- and overnutrition among children and mothers in Bangladesh: a spatial modelling approach to a nationally representative survey. Public Health Nutrition, Vol 21(13):2471-2481.
- Collis RA, Rahman MS, Watkinson O, Guttman OP, O’Mahony, Elliott PM (2018). Outcomes following the surgical management of left ventricular outflow tract obstruction: A systematic review and meta-analysis. International Journal of Cardiology, Vol 265(15).
- Rahman MS, Howlader T, Masud MS, Rahman ML (2016). 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, 11(6).
- Lopes LR, Rahman MS, Elliott PM (2013). A systematic review and meta-analysis of genotype–phenotype associations in patients with hypertrophic cardiomyopathy caused by sarcomeric protein mutations. Heart, 99(24):1800-11.
- Guttmann OP, Rahman MS, O’Mahony C, Anastasakis A, Elliott PM (2014). Atrial fibrillation and thromboembolism in patients with hypertrophic cardiomyopathy: systematic review. Heart, 100(6):465-72.
Future Research Projects (MS thesis):
- Information criteria for penalized likelihood approach in generalized linear models.
- Penalized regression for sparse binary data from complex survey design.
- Dynamic prediction with longitudinally measured biomarker
Research Students (for MS thesis in Applied Statistics):
- Safwan Shihab (2019-2020): Estimating causal effect of time dependent exposure on survival outcome.
- Silvia Khanam (2019-2020): On the second order bias reduction in the generalized linear models.
- Argha Dhar (2019-2020): Evaluation of the methods for estimating causal effect with binary outcome : A simulation study.
- Ariful Sanim (2018-2019): On estimating average treatment effect of rare exposure in observational studies: sample size consideration and bias correction.
- Md. Rasel (2018-2019): Dynamic prediction using landmark model: A simulation study to evaluate performance of different landmark approaches.
- Tasneem Fatima Alam (2017-2018): Estimation and Inference for Accelerated Failure Time (AFT) models with small or rare event survival data.
- Nowrin Nusrat (2017-2018): Dealing with separation or near-to-separation in the model for multinomial response.
- Ema Akter (2017-2018): Using Jeffreys-and logF(1,1)-prior based penalized methods for propensity score models with rare exposure.
- Muntaha Mushfiquee (2016-2017): Propensity score based adjustment for covariate effects on classification accuracy of biomarker using ROC curve.
- Tanzila Rahman Mou (2016-2017): Using penalized methods for propensity score model with rare exposure in observation studies with binary outcome.
- Momenul Haque Mondol (2015-2016): Bias reduced and separation-proof generalized estimating equation for correlated binary data.
- Avizit Adhikary (2015-2016): Application of Firth’s type penalized method to Cox PH framework for developing predictive models with sparse or highly censored survival data.
- Md. Maidul Husain (2015-2016): Covariates adjusted area under ROC curve in the presence of measurement error.
- Mahbuba Sultana (2014-2015): An evaluation of penalized likelihood methods for developing binary risk models with rare events.
- Md. Moniruzzaman (2014-2015): Prediction in the random effects logistic model for clustered binary data.
- Afrin Sadia Rumana (2013-2014): Concordance measures for evaluating the added predictive ability of new risk factors in logistic regression models: An alternative to the existing measures.
- Mohammad Junayed Bhuyan (2013-2014): Generalized Bernoulli model for correlated binary responses with an application to child nutrition data in Bangladesh
- Tanjina Rahman (2013-2014): Concordance statistics for evaluating the predictive performance of stratified Cox proportional hazard models.
- Biplob Biswas (2012-2013): Concordance statistics and discrimination in logistic regression models.
- Ariful Bhuiyan (2012-2013): Measures of predictive accuracy and explained variation for survival risk models.
- Geographical variation in the prevalence and pattern of chronic diseases in Bangladesh: A Bayesian spatial analysis of national health survey data (2021). DU Centennial Research Grant.
- Disease Mapping in Bangladesh (2015): A case study with hypertension and diabetes. DU Research Grant.
Awards and Grants
- Dean Award (2019). Awarded by the Faculty of Science, University of Dhaka for research paper publication.
- Educational Ambassadorship (2015). Awarded by the American Statistical Association (ASA).
- Conference Scientist Award (2015). Awarded by the International Society for Clinical Biostatistics (ISCB) in the 36th Annual Conference, Utrecht, The Netherlands.
- Travel Grant (2014). Funded by the Word Bank and ISI for attending Regional Statistics Congress, 16-20 November 2014, Malayesia.
- Research Grant (2013-2014). Funded by the University Grant Commission Bangladesh for the project entitled “Disease mapping in Bangladesh”.
- Costas Goutis Prize (2010). Awarded by the Department of Statistical Science, UCL, UK for research student excellence.
- Travel Grant (2013). Funded by the Word Bank and ISI for attending 59th ISI World Statistics Congress, 25-30 August 2013, Hongkong.
Courses currently teaching
- Longitudinal Data Analysis (Postgraduate level)
- Statistical Computing: GLM and Survival Analysis (Undergraduate level)
- Lifetime Data Analysis (Undergraduate level)
- Epidemiology (Undergraduate level)
- Design of Experiments (Undergraduate level)
- Generalized Linear Models (Undergraduate level)
- Meta Analysis. (Postgraduate level)
- Statistical Computing: Survival Analysis and GLM (Undergraduate level)