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Register a new userBiased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical Outcomes
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Added by
Colin Fogarty
Publication date
2019
fields
Mathematical Statistics
and research's language is
English
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We investigate the efficacy of surgical versus non-surgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instrumental variable design with surgeons tendencies to operate as an instrument. Assuming instrument validity, we find that non-surgical alternatives can reduce both hospital length of stay and the risk of complications, with estimated effects larger for septic patients than for non-septic patients. The validity of our instrument is plausible but not ironclad, necessitating a sensitivity analysis. Existing sensitivity analyses for IV designs assume effect homogeneity, unlikely to hold here because of patient-specific physiology. We develop a new sensitivity analysis that accommodates arbitrary effect heterogeneity and exploits components explainable by observed features. We find that the results for non-septic patients prove more robust to hidden bias despite having smaller estimated effects. For non-septic patients, two individuals with identical observed characteristics would have to differ in their odds of assignment to a high tendency to operate surgeon by a factor of 2.34 to overturn our finding of a benefit for non-surgical management in reducing length of stay. For septic patients, this value is only 1.64. Simulations illustrate that this phenomenon may be explained by differences in within-group heterogeneity.
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The ill-posedness of the inverse problem of recovering a regression function in a nonparametric instrumental variable model leads to estimators that may suffer from a very slow, logarithmic rate of convergence. In this paper, we show that restricting the problem to models with monotone regression functions and monotone instruments significantly weakens the ill-posedness of the problem. In stark contrast to the existing literature, the presence of a monotone instrument implies boundedness of our measure of ill-posedness when restricted to the space of monotone functions. Based on this result we derive a novel non-asymptotic error bound for the constrained estimator that imposes monotonicity of the regression function. For a given sample size, the bound is independent of the degree of ill-posedness as long as the regression function is not too steep. As an implication, the bound allows us to show that the constrained estimator converges at a fast, polynomial rate, independently of the degree of ill-posedness, in a large, but slowly shrinking neighborhood of constant functions. Our simulation study demonstrates significant finite-sample performance gains from imposing monotonicity even when the regression function is rather far from being a constant. We apply the constrained estimator to the problem of estimating gasoline demand functions from U.S. data.
In 2020, California required San Francisco to consider equity in access to resources such as housing, transportation, and emergency services as it re-opened its economy post-pandemic. Using a public dataset maintained by the San Francisco Fire Department of every call received related to emergency response from January 2003 to April 2021, we calculated the response times and distances to the closest of 48 fire stations and 14 local emergency rooms. We used logistic regression to determine the probability of meeting the averages of response time, distance from a fire station, and distance to an emergency room based on the median income bracket of a ZIP code based on IRS statement of income data. ZIP codes in the lowest bracket ($25,000-$50,000 annually) consistently had the lowest probability of meeting average response metrics. This was most notable for distances to emergency rooms, where calls from ZIP codes in the lowest income bracket had an 11.5% chance of being within the citys average distance (1 mile) of an emergency room, while the next lowest probability (for the income bracket of $100,000-$200,000 annually) was 75.9%. As San Francisco considers equity as a part of Californias Blueprint for a Safer Economy, it should evaluate the distribution of access to emergency services. Keywords: fire department, emergency medical services, emergency rooms, equity, logistic regression
We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Our feature-rich modelling allows for dynamic updating and refinement of waiting time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. Aspects relating to communicating forecast uncertainty to patients, and implementing this methodology in practice, are also discussed.
Emergency department (ED) crowding has been an increasing problem worldwide. Prior research has identified factors that contribute to ED crowding. However, the relationships between these remain incompletely understood. This studys objective was to analyse the effects of initiating a local protocol to alleviate crowding situations at the expense of increasing returning patients through the development of a system dynamics (SD) simulation model. The SD study is from an academic care hospital in Boston, MA. Data sources include direct observations, semi-structured interviews, archival data from October 2013, and peer-reviewed literature from the domains of emergency medicine and management science. The SD model shows interrelations between inpatient capacity restraints and return visits due to potential premature discharges. The model reflects the vulnerability of the ED system when exposed to unpredicted increases in demand. Default trigger values for the protocol are tested to determine a balance between increased patient flows and the number of returning patients. Baseline simulation runs for generic variables assessment showed high leverage potential in bed assignment- and transfer times. A thorough understanding of the complex non-linear behaviour of causes and effects of ED crowding is enabled through the use of SD. The vulnerability of the system lies in the crucial interaction between the physical constraints and the expedited patient flows through protocol activation. This study is an example of how hospital managers can benefit from virtual scenario testing within a safe simulation environment to immediately visualise the impacts of policy adjustments.
Instrumental variable is an essential tool for addressing unmeasured confounding in observational studies. Two stage predictor substitution (2SPS) estimator and two stage residual inclusion(2SRI) are two commonly used approaches in applying instrumental variables. Recently 2SPS was studied under the additive hazards model in the presence of competing risks of time-to-events data, where linearity was assumed for the relationship between the treatment and the instrument variable. This assumption may not be the most appropriate when we have binary treatments. In this paper, we consider the 2SRI estimator under the additive hazards model for general survival data and in the presence of competing risks, which allows generalized linear models for the relation between the treatment and the instrumental variable. We derive the asymptotic properties including a closed-form asymptotic variance estimate for the 2SRI estimator. We carry out numerical studies in finite samples, and apply our methodology to the linked Surveillance, Epidemiology and End Results (SEER) - Medicare database comparing radical prostatectomy versus conservative treatment in early-stage prostate cancer patients.
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