Ruby Barnard-Mayers1, Hiba Kouser2, Jamie A Cohen3, Katherine Tassiopoulos4, Ellen C Caniglia5, Anna-Barbara Moscicki6, Nicole G Campos7, Michelle R Caunca8, George R Seage Seage4, Eleanor J Murray9. 1. Department of Epidemiology, Boston University, Boston, MA, USA. Electronic address: rbarmay@bu.edu. 2. Department of Epidemiology, Boston University, Boston, MA, USA; Breast Oncology Center, Dana Farber Cancer Institute, Boston, MA, USA. 3. Health Policy PhD Program, Harvard University, Cambridge, MA, USA. 4. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. 5. Department of Population Health, NYU School of Medicine, New York, NY, USA. 6. Department of Pediatrics, Division of Adolescent Medicine, University of California, Los Angeles, CA, USA. 7. Center for Health Decision Science, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 8. Medical Scientist Training Program, Miller School of Medicine, University of Miami, Miami, FL, USA. 9. Department of Epidemiology, Boston University, Boston, MA, USA.
Abstract
BACKGROUND: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV. OBJECTIVES: To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. METHODS: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions before data analysis and exemplify the process for designing causal graphs to inform an etiologic study. CONCLUSION: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research before undergoing data collection and/or analysis.
BACKGROUND: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV. OBJECTIVES: To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. METHODS: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions before data analysis and exemplify the process for designing causal graphs to inform an etiologic study. CONCLUSION: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research before undergoing data collection and/or analysis.
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