Causal Inference with Missingness in Confounders

Bagmar, Md. Shaddam Hossain

Causal Inference with Missingness in Confounders

dc.contributor.advisor	Shen, Hua
dc.contributor.author	Bagmar, Md. Shaddam Hossain
dc.contributor.committeemember	Wu, Jingjing
dc.contributor.committeemember	Lu, Xuewen
dc.date	2019-11
dc.date.accessioned	2019-08-16T21:38:45Z
dc.date.available	2019-08-16T21:38:45Z
dc.date.issued	2019-08-15
dc.description.abstract	Causal inference is the process of uncovering causal connection between the effect variable and disease outcome in epidemiologic research. Confounders that influence both the effect variable and outcome need to be accounted for when obtaining the causal effect in observational studies. In addition, missing data often arise in the data collection procedure, working with complete cases often results in biased estimates. We consider the estimation of causal effect in the presence of missingness in the confounders under the missing at random assumption. We investigate how different estimators namely regression, G-estimation, propensity score-based estimators including matching, stratification, weighting, propensity regression and finally doubly robust estimator, perform when applying complete-case analysis or multiple imputation. Due to the uncertainty of imputation model and computational challenge for large number of imputations, we propose an expectation-maximization (EM) algorithm to estimate the expected values of the missing confounder and utilize weighting approach in the estimation of average treatment effect. Simulation studies are conducted to see whether there is any gain in estimation efficiency under the proposed method than complete case analysis and multiple imputation. The analysis identified EM method as most efficient and accurate method for dealing missingness in confounder except for propensity score matching and inverse weighting estimators. In these two estimators, multiple imputation is found as efficient, however EM is efficient for inverse weighting when the outcome is binary. Real life data application is shown for estimating the effect of adjuvant radiation treatment on patient's survival status after 10 years of breast cancer diagnosis. Under missing completely at random (MCAR) mechanism, EM is found as the most accurate method for handling missingness in confounder than multiple imputation.	en_US
dc.identifier.citation	Bagmar, M. S. H. (2019). Causal Inference with Missingness in Confounders (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/36819
dc.identifier.uri	http://hdl.handle.net/1880/110728
dc.language.iso	eng	en_US
dc.publisher.faculty	Science	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject.classification	Statistics	en_US
dc.title	Causal Inference with Missingness in Confounders	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Mathematics & Statistics	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	false	en_US