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.

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 Parental Incarceration and Child Well-being: Conceptual and Practical Concerns Regarding the Use of Propensity Scores

2018 ◽  
Vol 4 ◽  
pp. 237802311877930 ◽  
Author(s):  
Jennifer E. Copp ◽  
Peggy C. Giordano ◽  
Wendy D. Manning ◽  
Monica A. Longmore
Keyword(s):  
Propensity Score ◽  
Problem Behaviors ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Well Being ◽  
Propensity Score Methods ◽  
Score Analysis ◽  
Data Limitations ◽  
Using Data ◽  
And Control

The aim of the current investigation was to examine the appropriateness of propensity score methods for the study of incarceration effects on children by directing attention to a range of conceptual and practical concerns, including the exclusion of theoretically meaningful covariates, the comparability of treatment and control groups, and potential ambiguities resulting from researcher-driven analytic decisions. Using data from the Fragile Families and Child Wellbeing Study, we examined the effects of maternal and paternal incarceration on a range of child well-being outcomes, including internalizing and externalizing problem behaviors, Peabody Picture Vocabulary Test scores, and early juvenile delinquency. Our findings suggested that propensity scores and treatment effect estimates are highly sensitive to a number of decisions made by the researcher, including aspects where little consensus exists. In light of the conceptual underpinnings of propensity score analysis and existing data limitations, we suggest the potential utility of different identification methods and specialized data collection efforts.

scholarly journals Rural-to-urban migration, discrimination experience, and health in China: Evidence from propensity score analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244441
Author(s):  
Zihong Deng ◽  
Yik Wa Law
Keyword(s):  
Propensity Score ◽  
Urban Areas ◽  
Propensity Score Analysis ◽  
Urban Migration ◽  
Rural Residents ◽  
Mediation Analyses ◽  
Urban Migrants ◽  
Score Analysis ◽  
Rural To Urban Migration ◽  
Using Data

This research examines how rural-to-urban migration influences health through discrimination experience in China after considering migration selection bias. We conducted propensity score matching (PSM) to obtain a matched group of rural residents and rural-to-urban migrants with a similar probability of migrating from rural to urban areas using data from the 2014 China Family Panel Studies (CFPS). Regression and mediation analyses were performed after PSM. The results of regression analysis after PSM indicated that rural-to-urban migrants reported more discrimination experience than rural residents, and those of mediation analysis revealed discrimination experience to exert negative indirect effects on the associations between rural-to-urban migration and three measures of health: self-reported health, psychological distress, and physical discomfort. Sensitivity analysis using different calipers yielded similar results. Relevant policies and practices are required to respond to the unfair treatment and discrimination experienced by this migrant population.

scholarly journals Subgroup balancing propensity score

2019 ◽  
Vol 29 (3) ◽  
pp. 659-676 ◽  
Author(s):  
Jing Dong ◽  
Junni L Zhang ◽  
Shuxi Zeng ◽  
Fan Li
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Search Algorithm ◽  
Propensity Score Method ◽  
Moment Conditions ◽  
Propensity Score Model ◽  
Propensity Score Methods ◽  
Covariate Balance ◽  
Score Model

This paper concerns estimation of subgroup treatment effects with observational data. Existing propensity score methods are mostly developed for estimating overall treatment effect. Although the true propensity scores balance covariates in any subpopulations, the estimated propensity scores may result in severe imbalance in subgroup samples. Indeed, subgroup analysis amplifies a bias-variance tradeoff, whereby increasing complexity of the propensity score model may help to achieve covariate balance within subgroups, but it also increases variance. We propose a new method, the subgroup balancing propensity score, to ensure good subgroup balance as well as to control the variance inflation. For each subgroup, the subgroup balancing propensity score chooses to use either the overall sample or the subgroup (sub)sample to estimate the propensity scores for the units within that subgroup, in order to optimize a criterion accounting for a set of covariate-balancing moment conditions for both the overall sample and the subgroup samples. We develop two versions of subgroup balancing propensity score corresponding to matching and weighting, respectively. We devise a stochastic search algorithm to estimate the subgroup balancing propensity score when the number of subgroups is large. We demonstrate through simulations that the subgroup balancing propensity score improves the performance of propensity score methods in estimating subgroup treatment effects. We apply the subgroup balancing propensity score method to the Italy Survey of Household Income and Wealth (SHIW) to estimate the causal effects of having debit card on household consumption for different income groups.

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.

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