Beth Ann Griffin1, Marika Suttorp Booth2, Monica Busse3, Edward J Wild4, Claude Setodji5, John H Warner6, Cristina Sampaio6, Amrita Mohan6. 1. RAND Center for Causal Inference, RAND Corporation, 1200, South Hayes Street, Arlington, VA, USA. Electronic address: bethg@rand.org. 2. RAND Corporation, 1776, Main Street, Santa Monica, CA, USA. 3. Centre for Trials Research, Cardiff University, Neuadd Merionydd, Heath Park, CF14 4XN, Cardiff, UK. 4. Huntington's Disease Centre, UCL Institute of Neurology, London, WC1N 3BG, UK. 5. RAND Center for Causal Inference, RAND Corporation, 4570, Fifth Ave #600, Pittsburgh, PA, USA. 6. CHDI Management/CHDI Foundation, 155 Village Boulevard, Suite 200, Princeton, NJ, USA.
Abstract
INTRODUCTION: Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD). METHODS: We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models. RESULTS: Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users. CONCLUSIONS: Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.
INTRODUCTION: Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD). METHODS: We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models. RESULTS: Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users. CONCLUSIONS: Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.
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