scholarly journals Comparing Propensity Score Methods Versus Traditional Regression Analysis for the Evaluation of Observational Data: A Case Study Evaluating the Treatment of Gram-Negative Bloodstream Infections

2020 ◽  
Author(s):  
Joe Amoah ◽  
Elizabeth A Stuart ◽  
Sara E Cosgrove ◽  
Anthony D Harris ◽  
Jennifer H Han ◽  
...  
Keyword(s):  
Propensity Score ◽  
Observational Data ◽  
Oral Therapy ◽  
Propensity Score Analysis ◽  
Bloodstream Infections ◽  
Causal Effects ◽  
Gram Negative ◽  
Propensity Score Methods ◽  
Score Analysis

Abstract Background Propensity score methods are increasingly being used in the infectious diseases literature to estimate causal effects from observational data. However, there remains a general gap in understanding among clinicians on how to critically review observational studies that have incorporated these analytic techniques. Methods Using a cohort of 4967 unique patients with Enterobacterales bloodstream infections, we sought to answer the question “Does transitioning patients with gram-negative bloodstream infections from intravenous to oral therapy impact 30-day mortality?” We conducted separate analyses using traditional multivariable logistic regression, propensity score matching, propensity score inverse probability of treatment weighting, and propensity score stratification using this clinical question as a case study to guide the reader through (1) the pros and cons of each approach, (2) the general steps of each approach, and (3) the interpretation of the results of each approach. Results 2161 patients met eligibility criteria with 876 (41%) transitioned to oral therapy while 1285 (59%) remained on intravenous therapy. After repeating the analysis using the 4 aforementioned methods, we found that the odds ratios were broadly similar, ranging from 0.84–0.95. However, there were some relevant differences between the interpretations of the findings of each approach. Conclusions Propensity score analysis is overall a more favorable approach than traditional regression analysis when estimating causal effects using observational data. However, as with all analytic methods using observational data, residual confounding will remain; only variables that are measured can be accounted for. Moreover, propensity score analysis does not compensate for poor study design or questionable data accuracy.

Using Propensity Score Analysis of Survey Data to Estimate Population Average Treatment Effects: A Case Study Comparing Different Methods

2020 ◽  
Vol 44 (1) ◽  
pp. 84-108
Author(s):  
Nianbo Dong ◽  
Elizabeth A. Stuart ◽  
David Lenis ◽  
Trang Quynh Nguyen
Keyword(s):  
Propensity Score ◽  
Propensity Score Analysis ◽  
Least Squares Regression ◽  
Complex Survey ◽  
Propensity Score Methods ◽  
Analysis Methods ◽  
Population Average ◽  
Score Analysis ◽  
Average Treatment Effects

Background: Many studies in psychological and educational research aim to estimate population average treatment effects (PATE) using data from large complex survey samples, and many of these studies use propensity score methods. Recent advances have investigated how to incorporate survey weights with propensity score methods. However, to this point, that work had not been well summarized, and it was not clear how much difference the different PATE estimation methods would make empirically. Purpose: The purpose of this study is to systematically summarize the appropriate use of survey weights in propensity score analysis of complex survey data and use a case study to empirically compare the PATE estimates using multiple analysis methods that include ordinary least squares regression, weighted least squares regression, and various propensity score applications. Methods: We first summarize various propensity score methods that handle survey weights. We then demonstrate the performance of various analysis methods using a nationally representative data set, the Early Childhood Longitudinal Study–Kindergarten to estimate the effects of preschool on children’s academic achievement. The correspondence of the results was evaluated using multiple criteria. Results and Conclusions: It is important for researchers to think carefully about their estimand of interest and use methods appropriate for that estimand. If interest is in drawing inferences to the survey target population, it is important to take the survey weights into account, particularly in the outcome analysis stage for estimating the PATE. The case study shows, however, not much difference among various analysis methods in one applied example.

Bayesian propensity score analysis for clustered observational data

2019 ◽  
Vol 29 (2) ◽  
pp. 335-355
Author(s):  
Qi Zhou ◽  
Catherine McNeal ◽  
Laurel A. Copeland ◽  
Justin P. Zachariah ◽  
Joon Jin Song
Keyword(s):  
Propensity Score ◽  
Observational Data ◽  
Propensity Score Analysis ◽  
Score Analysis

Propensity score analysis: principle and concept

2018 ◽  
Vol 1 (1) ◽  
pp. 25-27
Author(s):  
Kriangsak Charoensuk
Keyword(s):  
Propensity Score ◽  
Observational Study ◽  
Randomized Control Trial ◽  
Citation Index ◽  
Propensity Score Analysis ◽  
Propensity Score Method ◽  
Propensity Score Methods ◽  
Score Analysis ◽  
Score Method ◽  
Randomized Control

การวิเคราะห์ข้อมูลทางสถิติโดยใช้คะแนนโพรเพนซิตี้ (propensity score analysis) เป็นหนึ่งในการวิจัยทางสถิติแบบใหม่ที่เพิ่งถือกำเนิดขึ้น ภายหลังสถิติพื้นฐานอื่นๆ และถูกนำมาใช้กันอย่างแพร่หลายเพิ่มมากขึ้น เพื่อช่วยควบคุมปัจจัยหรือตัวแปรกวน (confounding) ที่เกิดขึ้นในการศึกษาวิจัยแบบเชิงสังเกตการณ์ (observational study) ด้านการศึกษา จิตวิทยา รวมถึงด้ายวิทยาศาสตร์สุขภาพ ทดแทนการทดลองแบบสุ่มและมีกลุ่มเปรียบเทียบ (randomized control trial, RCT) ซึ่งบางครั้งผู้วิจัยไม่สามารถทำได้ อย่างไรก็ดี ยังคงมีผู้วิจัยหลายคนที่อาจยังสับสนและไม่เข้าใจหลักการ ความสำคัญ รวมถึงขั้นตอนพื้นฐานและเทคนิคของการวิเคราะห์วิธีดังกล่าว วัตถุประสงค์ของการทบทวนบทความในครั้งนี้จึงมุ่งหวังเพื่อ 1. ให้ผู้อ่านเข้าใจหลักการพื้นฐานของการวิเคราะห์ข้อมูลทางสถิติโดยใช้ propensity score 2. ทราบเทคนิคพื้นฐานและการเลือก propensity score methods 3. เข้าใจการใช้ propensity score matching จากการยกตัวย่างการศึกษาวิจัยที่มีมาในอดีต เพื่อให้ผู้อ่านเข้าใจหลักการการวิเคราะห์ข้อมูลดังกล่าวมากยิ่งขึ้น Figure 1 จำนวนการศึกษาวิจัยทางการแพทย์ในแต่ละปี ที่มีรายชื่ออ้างอิงใน PubMed และ Science Citation Index และใช้การวิเคราะห์ข้อมูลโดยใช้คะแนนโพรเพนซิตี้ (propensity score method) ดัดแปลงมาจากงานวิจัยของ Hill J และคณะ (8)

scholarly journals Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures

2018 ◽  
Vol 28 (5) ◽  
pp. 1365-1377 ◽  
Author(s):  
Peter C Austin
Keyword(s):  
Propensity Score ◽  
Propensity Score Analysis ◽  
Balance Assessment ◽  
Propensity Score Method ◽  
Propensity Score Methods ◽  
Covariate Balance ◽  
Crucial Component ◽  
Score Analysis ◽  
Generalized Propensity Score ◽  
Using Data

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures (e.g., active treatment vs. control). The generalized propensity score is an extension of the propensity score for use with quantitative exposures (e.g., dose or quantity of medication, income, years of education). A crucial component of any propensity score analysis is that of balance assessment. This entails assessing the degree to which conditioning on the propensity score (via matching, weighting, or stratification) has balanced measured baseline covariates between exposure groups. Methods for balance assessment have been well described and are frequently implemented when using the propensity score with binary exposures. However, there is a paucity of information on how to assess baseline covariate balance when using the generalized propensity score. We describe how methods based on the standardized difference can be adapted for use with quantitative exposures when using the generalized propensity score. We also describe a method based on assessing the correlation between the quantitative exposure and each covariate in the sample when weighted using generalized propensity score -based weights. We conducted a series of Monte Carlo simulations to evaluate the performance of these methods. We also compared two different methods of estimating the generalized propensity score: ordinary least squared regression and the covariate balancing propensity score method. We illustrate the application of these methods using data on patients hospitalized with a heart attack with the quantitative exposure being creatinine level.

scholarly journals A Case Study About Why It Can Be Difficult To Test Whether Propensity Score Analysis Works in Field Experiments

2013 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
William R. Shadish ◽  
Peter M. Steiner ◽  
Thomas D. Cook
Keyword(s):  
Experimental Data ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Field Experiments ◽  
Propensity Score Analysis ◽  
Randomized Experiment ◽  
Score Analysis ◽  
Quasi Experimental ◽  
Do So

Peikes, Moreno and Orzol (2008) sensibly caution researchers that propensity score analysis may not lead to valid causal inference in field applications. But at the same time, they made the far stronger claim to have performed an ideal test of whether propensity score matching in quasi-experimental data is capable of approximating the results of a randomized experiment in their dataset, and that this ideal test showed that such matching could not do so. In this article we show that their study does not support that conclusion because it failed to meet a number of basic criteria for an ideal test. By implication, many other purported tests of the effectiveness of propensity score analysis probably also fail to meet these criteria, and are therefore questionable contributions to the literature on the effects of propensity score analysis. DOI:10.2458/azu_jmmss_v3i2_shadish

scholarly journals Bayesian Propensity Score Analysis for Observational Data

2006 ◽  
Vol 163 (suppl_11) ◽  
pp. S222-S222
Author(s):  
L C McCandless ◽  
P Gustafson ◽  
P C Austin
Keyword(s):  
Propensity Score ◽  
Observational Data ◽  
Propensity Score Analysis ◽  
Score Analysis
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