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Kimon Bekelis, Daniel J. Gottlieb, Yin Su, A. James O'Malley, Nicos Labropoulos, Philip Goodney, Michael T. Lawton and Todd A. MacKenzie

effects methods to control for clustering at the hospital referral region (HRR) level. To control for unmeasured confounding, we used an instrumental variable (IV) approach, creating pseudo-randomization on the treatment method. Methods Data and Cohort Creation The Dartmouth Committee for the Protection of Human Subjects approved this study. Data were anonymized and de-identified prior to use; therefore, no informed consent was required. We used 100% of the Medicare Denominator File and corresponding Medicare inpatient and outpatient claims, Parts A and B, for

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Kimon Bekelis, Daniel J. Gottlieb, Yin Su, Giuseppe Lanzino, Michael T. Lawton and Todd A. MacKenzie

treatment method and Medicare expenditures for elderly patients in the 1st year post-SAH. To control for unmeasured confounding (mainly the different patient characteristics and the nonrandom selection of treatments), we used an instrumental variable (IV) approach, simulating pseudo-randomization on the treatment method. Methods Data and Cohort Creation The Dartmouth Committee for the Protection of Human Subjects approved this study. The data were anonymized and de-identified prior to use; therefore, no informed consent was required. We used 100% of the Medicare

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John H. Sampson, James E. Herndon II, Allan H. Friedman and Amy P. Abernethy

biases inherent in observational studies is a method frequently used in economic analysis: instrumental variable analysis. This analytic technique involves two regression models. The first model predicts treatment assignment as a function of instrumental variables, where an instrumental variable is defined as a variable that affects outcome only through its effect on treatment assignment and does not have an effect on outcome. The second model examines the effect of treatment on outcome. Theoretically the effect of selection bias derived from an instrument variable is

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Keita Shibahashi, Kazuhiro Sugiyama, Jun Tomio, Hidenori Hoda and Akio Morita

calculated after propensity score matching. For each group, event-time distributions were estimated using the Kaplan-Meier method. The log-rank test was used to test for differences in cumulative 30-day survival rate. To address residual confounding due to unmeasured factors, we performed an additional instrumental variable analysis using hospital treatment preference as the instrumental variable. We defined the treatment preference of hospital i in year j as the total number of ASDH patients who received decompressive craniectomy in hospital i in all years between

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Nicole A. Silva, Belinda Shao, Michael J. Sylvester, Jean Anderson Eloy and Chirag D. Gandhi

these authors’ use of instrumental variable analysis adequately adjusts for the inherent selection bias that otherwise renders the 2 groups incomparable. 32 Our study avoids comparison across differential cohorts by comparing elderly patients to nonelderly patients within each intervention cohort, extracting valuable data on health care outcomes and hospital charges. Limitations of the Study As an NIS database analysis, this study has several important limitations. Coding inaccuracies are inevitable. The NIS does not include useful variables important to the natural

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Sherman C. Stein, Patrick Georgoff, Sudha Meghan, Kasim L. Mirza and Omar M. El Falaky

-to-treat or instrumental variables analyses perfect solutions? . Stat Med 26 : 954 – 964 , 2007 9 Baxt WG , Moody P : The impact of advanced prehospital emergency care on the mortality of severely brain-injured patients . J Trauma 27 : 365 – 369 , 1987 10 Becker DP , Miller JD , Ward JD , Greenberg RP , Young HF , Sakalas R : The outcome from severe head injury with early diagnosis and intensive management . J Neurosurg 47 : 491 – 502 , 1977 11 Benzer A , Traweger C , Ofner D , Marosi M , Luef G , Schmutzhard E

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Sandi K. Lam, Visish M. Srinivasan, Thomas G. Luerssen and I-Wen Pan

, many potential determinants could impact both inpatient costs and LOS, such as hydrocephalus etiology, number of chronic conditions, and age. The estimation of conventional multivariate-regression modeled cost with LOS and all other possible covariates will be biased due to collinearity between LOS and other covariates. Alternatively, a simultaneous equations model system for both cost and LOS is an option. However, we could not find a strong instrumental variable that correlated with LOS and did not correlate with total costs. Therefore, we used CoPID to identify

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Miami, Florida • March 14–17, 2019

significantly for smoking, diabetes, gender, duration of surgery, or pre-operative symptomatic urinary retention (Tables 1, 2). Our POUR rate decreased significantly from 7.6% to 3.3% and compliance measures improved significantly following the QI project implementation (table 3). Conclusion The implementation of our POUR prevention protocol significantly lowered our POUR rate following lumbar surgery. 286 Implications of Risk Adjustment for Physician Incentives in the Medicare Access and CHIP Reauthorization Act: Evidence from an Instrumental Variables Analysis of Patients