Causal Inference Methodology for Comparisons of Hospital Quality of Care

Causal Inference Methodology for Comparisons of Hospital Quality of Care Katherine Daignault Doctor of Philosophy Graduate Department of Biostatistics, Dalla Lana School of Public Health University of Toronto 2019 In a national or provincial health care system, where limited financial resources are available to improve patient care, it is necessary to be able to evaluate the current care practices of hospitals to determine where resources are best spent. Assessment of hospital care quality is achieved by comparing each hospital’s performance to some reference level of care, often the average care level in the system, termed standardization. Standardization allows adjustment for differences in patient characteristics between hospitals which would unduly penalize hospitals that treat sicker patients. The quality and quantity of information available to make such adjustments, or lack thereof, can bias estimates of a hospital’s performance, resulting in misleading assessments of quality. Further, the goal of profiling care is not just to identify areas in which care disparities exist, but ultimately to intervene on care to improve patient outcomes. In this thesis, I take advantage of the causal nature of such comparisons (i.e. poor care leads to poor outcomes) and propose new statistical methods under a causal inference framework. First, I illustrate the current limitations of a standard hospital comparison analysis using U.S. prostate cancer data. Second, I develop a doubly robust estimator for the standardized mortality ratio (SMR) when the reference is to the system average level of care. I show that this estimator will provide unbiased estimates of the SMR as long as one of the component models is correctly specified. Third, I show that one assumption needed for the above estimator can be relaxed only for this reference comparison. Fourth, I adapt causal mediation analysis methods to derive a decomposition of the hospital effect on patient outcomes that may act through a mediating process, and develop two estimators for this decomposition. This allows quantification of the effect an intervention to improve care may have on patient outcomes so that hospitals can be prioritized in terms of those who would benefit most from government resources. Finally, I illustrate the proposed mediation methods on Ontario kidney cancer data. This thesis provides valuable tools to effectively identify and target hospitals in which care improvement is most needed. ii Acknowledgements I want to extend a huge thank you to my supervisor, Olli Saarela, for all the time and patience he has devoted to me throughout this degree. He has been a wonderful mentor and has always left me in awe of the extent of his expertise. He is a constant source of inspiration for me and I would not be where I am today without him. I also want to extend my gratitude to my committee members, Wendy Lou and Eleanor Pullenayegum. This thesis would not have been possible without their continued support and encouragement. I was also fortunate enough to have had the opportunity to work with a wonderful group of collaborators in the Urology Department at Princess Margaret Cancer Centre, under the lead of Drs. Antonio Finelli and Keith Lawson. Their projects allowed me to gain insight into the field of health services research and served as important motivation of the methods developed for my thesis. My appreciation goes out to the Biostatistics department for their continued support and advocation for their students. Their support led to the creation and renovation of the Biostatistics PhD offices, which has been one of the most important factors in my successful completion of this dissertation. Whether through academic or emotional support, or just goofing off, the room and the people in it have been an integral part of this journey. In particular, I want to thank Osvaldo, Kaviul, Thai-Son, Michela, Myrtha, Sudip, Tim, Jen, and Konstantin for all the laughs and support throughout. You’ve all made my time at Dalla Lana an unforgettable one. My experience in this program was not always an easy one, and I feel extremely lucky to have some of the most wonderful and unwaveringly supportive friends anyone could ask for. To Kuan, thank you for always being the life of any room you enter, for always making me laugh, for putting all of life’s problems into perspective for me, and for being just a great friend. To Thea, thank you for always making time for me, for helping me get accustomed to a new city and life, for our wonderfully unproductive work lunches, and for being one of my oldest and most cherished friends. To Marie, you have been my rock for so many years, there are just no words capable of expressing my gratitude. I quite literally could not have done this without you and your unending emotional support. It goes without saying that none of this would have been possible without the love and support of my family and particularly my parents. Thank you for a lifetime of encouragement to pursue my passions and for continually believing in me, even when I couldn’t believe in myself. Finally, to my love and best friend, Ryan, you are the best thing that has ever happened to me. You are endlessly supportive, caring, funny, and intelligent. You make me a better person and I can not imagine having done this without you by my side. iii