scholarly journals When Inverse Propensity Scoring does not Work

2020 ◽  
Author(s):  
Ali Vardasbi ◽  
Harrie Oosterhuis ◽  
Maarten de Rijke
Keyword(s):  
Propensity Scoring

Survival after partial and radical nephrectomy for the treatment of stage T1bN0M0 renal cell carcinoma (RCC) in the USA: a propensity scoring approach

2011 ◽  
Vol 109 (10) ◽  
pp. 1457-1462 ◽  
Author(s):  
Gina M. Badalato ◽  
Max Kates ◽  
Juan P. Wisnivesky ◽  
Arindam Roy Choudhury ◽  
James M. McKiernan
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Radical Nephrectomy ◽  
Propensity Scoring ◽  
The Usa

EXPLORING TWO COST-ADJUSTMENT METHODS FOR SELECTION BIAS IN A SMALL SAMPLE: USING A FETAL CARDIOLOGY DATASET

2014 ◽  
Vol 30 (3) ◽  
pp. 325-332 ◽  
Author(s):  
Hema Mistry
Keyword(s):  
Selection Bias ◽  
Small Sample ◽  
Economic Evaluations ◽  
Scoring Methods ◽  
Regression Analyses ◽  
Cost Estimates ◽  
Propensity Scoring ◽  
The Cost ◽  
Cost Differences ◽  
Direct Referral

Objectives: In economic evaluations of healthcare technologies, situations arise where data are not randomized and numbers are small. For this reason, obtaining reliable cost estimates of such interventions may be difficult. This study explores two approaches in obtaining cost estimates for pregnant women screened for a fetal cardiac anomaly.Methods: Two methods to reduce selection bias in health care: regression analyses and propensity scoring methods were applied to the total mean costs of pregnancy for women who received specialist cardiac advice by means of two referral modes: telemedicine and direct referral.Results: The observed total mean costs of pregnancy were higher for the telemedicine group than the direct referral group (4,918 versus 4,311 GBP). The regression model found that referral mode was not a significant predictor of costs and the cost difference between the two groups was reduced from 607 to 94 GBP. After applying the various propensity score methods, the groups were balanced in terms of sizes and compositions; and again the cost differences between the two groups were smaller ranging from -62 (matching “by hand”) to 333 GBP (kernel matching).Conclusions: Regression analyses and propensity scoring methods applied to the dataset may have increased the homogeneity and reduced the variance in the adjusted costs; that is, these methods have allowed the observed selection bias to be reduced. I believe that propensity scoring methods worked better for this dataset, because after matching the two groups were similar in terms of background characteristics and the adjusted cost differences were smaller.

Comparison of perioperative outcomes in endovascular versus open repair for juxtarenal and pararenal aortic aneurysms: A propensity-matched analysis

Vascular ◽  
2016 ◽  
Vol 25 (4) ◽  
pp. 339-345 ◽  
Author(s):  
Nathan T Orr ◽  
Daniel L Davenport ◽  
David J Minion ◽  
Eleftherios S Xenos
Keyword(s):  
Length Of Stay ◽  
Mortality Rates ◽  
Aortic Aneurysms ◽  
Endovascular Aortic Repair ◽  
Hospital Length Of Stay ◽  
Perioperative Outcomes ◽  
Propensity Scoring ◽  
Aortic Repair ◽  
Open Aortic Repair ◽  
Significant Difference

Objective Endoluminal aortic aneurysm repair is suitable within certain anatomic specifications. This study aims to compare 30-day outcomes of endovascular versus open repairs for juxtarenal and pararenal aortic aneurysms (JAA/PAAs). Methods The ACS-NSQIP database was queried from 2012 to 2015 for JAA/PAA repairs. Procedures characterized as emergent were included in the study; however, failed prior repairs and ruptured aneurysms were excluded. The preoperative and perioperative patient characteristics, operative techniques, and outcome variables were compared between the open aortic repair and the endovascular aortic repair groups. Propensity scoring was performed to clinically match open aortic repair and endovascular aortic repair groups on preoperative risk and select perioperative factors that differed significantly in the unmatched groups. Outcome comparisons were then performed between matched groups. Results A total of 1005 (789 JAAs and 216 PAAs) aneurysm repairs were included in the study. Of these, there were 395 endovascular aortic repairs and 610 open aortic repairs. Propensity scoring created a matched group of 263 endovascular aortic repair and 263 open aortic repair patients. There was no statistically significant difference in 30-day mortality rates between matched endovascular aortic repair and open aortic repair patients (2.7% vs. 5.7%). The endovascular aortic repair group had a shorter ICU length of stay and overall hospital stay. The 30-day morbidity significantly favored endovascular aortic repair over open aortic repair (16% vs. 35%, p < 0.001). The main drivers of morbidity for endovascular aortic repair versus open aortic repair included return to the OR (6.8% vs. 15%, p < 0.001), rate of cardiac or respiratory failure (7.6% vs. 21%, p = 0.001), rate of renal insufficiency or failure (3.8% vs. 9.9%, p = 0.009), and rate of pneumonia (1.5% vs. 6.8%, p = 0.004). Conclusions There is no difference in mortality rates between endovascular aortic repair versus open aortic repair when repairing JAAs/PAAs. There is a significant difference in overall morbidity, and ICU and hospital length of stay favoring endovascular aortic repair over open aortic repair. This supports the expanded applicability and efficacy of endovascular repair for complex aneurysms.

Extracting Data from the National Inpatient Sample

2010 ◽  
pp. 69-93
Author(s):  
Patricia Cerrito ◽  
John Cerrito
Keyword(s):  
Health Care ◽  
Randomized Controlled Trial ◽  
Propensity Score ◽  
Controlled Trial ◽  
Primary Diagnosis ◽  
The Other ◽  
National Inpatient Sample ◽  
Propensity Scoring ◽  
Health Care Database ◽  
Care Database

In the other type of health care database that we discuss in this chapter, there are multiple columns for each patient observation. It is more difficult to find both the most frequently occurring codes, or to find patients with specific codes for the purpose of extraction. For this reason, many studies focus on the primary diagnosis or procedure. We will provide the programming necessary to find the most frequent codes and to find the patients who have a specific condition. Another aspect of preprocessing we will explore in this chapter using the National Inpatient Sample is that of propensity scoring. When it is not possible to perform a randomized, controlled trial, an attempt is made to emulate such a trial by comparing two observational subgroups. The two groups are matched based upon demographic factors and related patient conditions. It is possible to define a level of patient severity and then to match patients with the severity level as part of the propensity score.

Propensity Scoring Approach

2021 ◽  
pp. 161-198
Author(s):  
Jae Kwang Kim ◽  
Jun Shao
Keyword(s):  
Propensity Scoring

Efficacy of Video-assisted Thoracoscopic Esophagectomy for Stage II/III Esophageal Cancer: Analysis Using the Propensity Scoring System

2020 ◽  
Vol 40 (3) ◽  
pp. 1587-1595 ◽  
Author(s):  
KENTA IGUCHI ◽  
CHIKARA KUNISAKI ◽  
SHO SATO ◽  
YUSAKU TANAKA ◽  
HIROSHI MIYAMOTO ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Scoring System ◽  
Thoracoscopic Esophagectomy ◽  
Stage Ii ◽  
Propensity Scoring ◽  
Video Assisted

scholarly journals PDB27 ANTIPSYCHOTIC UTILIZATION AND TREATMENT-EMERGENT DIABETES—COMPARISON OF RESULTS WITH AND WITHOUT PROPENSITY SCORING

2006 ◽  
Vol 9 (3) ◽  
pp. A39
Author(s):  
M Yang ◽  
JC Barrier ◽  
KA Lawson ◽  
KL Rascati ◽  
ML Crismon ◽  
...  
Keyword(s):  
Propensity Scoring ◽  
Comparison Of Results

Cost Savings Associated with Palliative Care (DRAFT)

2018 ◽  
Author(s):  
Breffni Hannon
Keyword(s):  
Intensive Care Unit ◽  
Palliative Care ◽  
Intensive Care ◽  
Diagnostic Imaging ◽  
Hospital Costs ◽  
Cost Savings ◽  
Direct Costs ◽  
Economic Benefits ◽  
Propensity Scoring ◽  
Clinical Benefits

Although the clinical benefits associated with hospital-based palliative care (PC) consultation teams are well established, few studies address the potential economic impact of these services. This study aimed to examine the effect of hospital-based PC teams on hospital costs for patients who died in the hospital, as well as for those discharged alive. Eight diverse hospital settings with established PC teams were chosen, and administrative data relating to direct costs (including laboratory, diagnostic imaging, pharmacy, and intensive care unit [ICU] costs) were analyzed. Propensity scoring was used to match PC patients with usual care (UC) patients. Of 2,630 PC patients who were discharged alive, net savings of $2,642 per admission were calculated, compared with 18,427 UC patients. For the 2,278 PC patients who died in the hospital, savings of $4,908 per admission were seen, when compared with 2,124 UC patients, confirming the additional economic benefits associated with hospital-based PC teams.

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