Confounders and Using the Method of Propensity Scores

2018 ◽  
pp. 362-376
Author(s):  
Chin Lin ◽  
Rui Imamura ◽  
Felipe Fregni
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Propensity Scores ◽  
Robust Method ◽  
Case Discussion ◽  
Potential Relationship

This chapter explores the important issue of confounding in observational studies. The potential imbalances that result for not controlling assignment of treatment or exposure may lead to imbalance of variables that are associated with both treatment and intervention (or exposure) thus confounding results. Therefore, in this context, a potential relationship between an intervention and an outcome could be invalid. This chapter therefore explains basic definitions of confounding and presents some methods to control for confounders, highlighting the use of the propensity score, which is considered a robust method for this purpose. Different techniques of adjustment using propensity score (matching, stratification, regression, and weighting) are also discussed. This chapter concludes with a case discussion about confounding and how to address it.

scholarly journals Propensity score matching in otolaryngologic literature: A systematic review and critical appraisal

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244423
Author(s):  
Aman Prasad ◽  
Max Shin ◽  
Ryan M. Carey ◽  
Kevin Chorath ◽  
Harman Parhar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Critical Appraisal ◽  
Propensity Scores ◽  
Final Analysis ◽  
Publication History ◽  
Covariate Balance

Background Propensity score techniques can reduce confounding and bias in observational studies. Such analyses are able to measure and balance pre-determined covariates between treated and untreated groups, leading to results that can approximate those generated by randomized prospective studies when such trials are not feasible. The most commonly used propensity score -based analytic technique is propensity score matching (PSM). Although PSM popularity has continued to increase in medical literature, improper methodology or methodological reporting may lead to biased interpretation of treatment effects or limited scientific reproducibility and generalizability. In this study, we aim to characterize and assess the quality of PSM methodology reporting in high-impact otolaryngologic literature. Methods PubMed and Embase based systematic review of the top 20 journals in otolaryngology, as measured by impact factor from the Journal Citations Reports from 2012 to 2018, for articles using PSM analysis throughout their publication history. Eligible articles were reviewed and assessed for quality and reporting of PSM methodology. Results Our search yielded 101 studies, of which 92 were eligible for final analysis and review. The proportion of studies utilizing PSM increased significantly over time (p < 0.001). Nearly all studies (96.7%, n = 89) specified the covariates used to calculate propensity scores. Covariate balance was illustrated in 67.4% (n = 62) of studies, most frequently through p-values. A minority (17.4%, n = 16) of studies were found to be fully reproducible according to previously established criteria. Conclusions While PSM analysis is becoming increasingly prevalent in otolaryngologic literature, the quality of PSM methodology reporting can be improved. We provide potential recommendations for authors regarding optimal reporting for analyses using PSM.

Validating Four Standard Scales in Spiritually Practicing and Nonpracticing Samples Using Propensity Score Matching

2008 ◽  
Vol 24 (3) ◽  
pp. 165-173 ◽  
Author(s):  
Niko Kohls ◽  
Harald Walach
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Repeated Measures ◽  
Propensity Scores ◽  
Total Sample ◽  
Experimental Manipulation ◽  
Brief Symptom Inventory ◽  
Time Interaction ◽  
Social Support Scale ◽  
Lower Test

Validation studies of standard scales in the particular sample that one is studying are essential for accurate conclusions. We investigated the differences in answering patterns of the Brief-Symptom-Inventory (BSI), Transpersonal Trust Scale (TPV), Sense of Coherence Questionnaire (SOC), and a Social Support Scale (F-SoZu) for a matched sample of spiritually practicing (SP) and nonpracticing (NSP) individuals at two measurement points (t1, t2). Applying a sample matching procedure based on propensity scores, we selected two sociodemographically balanced subsamples of N = 120 out of a total sample of N = 431. Employing repeated measures ANOVAs, we found an intersample difference in means only for TPV and an intrasample difference for F-SoZu. Additionally, a group × time interaction effect was found for TPV. While Cronbach’s α was acceptable and comparable for both samples, a significantly lower test-rest-reliability for the BSI was found in the SP sample (rSP = .62; rNSP = .78). Thus, when researching the effects of spiritual practice, one should not only look at differences in means but also consider time stability. We recommend propensity score matching as an alternative for randomization in variables that defy experimental manipulation such as spirituality.

scholarly journals The Effects of Employment on Depression and Life Satisfaction Among Old-Aged Using the DD Method Combined With PSM

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 408-408
Author(s):  
Si Young Song ◽  
Hey Jung Jun ◽  
Sun Ah Lee
Keyword(s):  
Life Satisfaction ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Multiple Regression ◽  
Propensity Scores ◽  
Panel Study ◽  
Double Difference ◽  
Combined Model ◽  
Comparative Group ◽  
Experimental Group

Abstract The purpose of this study is to explore the effect of employment on depression and life satisfaction among old-aged. Using 12th (2017) wave and 13th (2018) wave of Korean Welfare Panel Study (KoWePS), three stages of analyses were conducted. First, through propensity score matching (PSM) method, sample with similar propensity scores was matched between the group that did not work in 12th wave but worked in 13th wave (experimental group, N=180), and the group that did not work in 12th and 13th wave (comparative group, N=180). Second, the matched sample was used to conduct multiple regression analysis with the group dummy variable (experimental group, comparative group) as an independent variable, and depression and life satisfaction as the dependent variables. Third, combined model of propensity score matching (PSM) and double difference (DD) method was conducted to more appropriately derive the net effect of employment. The results of multiple regression after propensity matching showed that employment had a positive effect on reducing depression (B= -1.70, p&lt; .01) and increasing life satisfaction (B= .12, p&lt; .01) in old-aged. Furthermore, in combined model of PSM and DD, life satisfaction was improved when employed compared to non-employed (B= .15, p&lt; .05). The results of this study are meaningful in that the meaning of employment in old-aged is more clearly derived by solving selection bias and endogenous problems. Also, this study may provide reference for establishing welfare policies related to employment among old-aged.

Multiple imputation analysis for propensity score matching with missing causes of failure: An application to hepatocellular carcinoma data

2021 ◽  
pp. 096228022110370
Author(s):  
Seungbong Han ◽  
Kam-Wah Tsui ◽  
Hui Zhang ◽  
Gi-Ae Kim ◽  
Young-Suk Lim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Observational Studies ◽  
Imputation Method ◽  
Competing Risk ◽  
Data Imputation ◽  
Causes Of Failure

Propensity score matching is widely used to determine the effects of treatments in observational studies. Competing risk survival data are common to medical research. However, there is a paucity of propensity score matching studies related to competing risk survival data with missing causes of failure. In this study, we provide guidelines for estimating the treatment effect on the cumulative incidence function when using propensity score matching on competing risk survival data with missing causes of failure. We examined the performances of different methods for imputing the data with missing causes. We then evaluated the gain from the missing cause imputation in an extensive simulation study and applied the proposed data imputation method to the data from a study on the risk of hepatocellular carcinoma in patients with chronic hepatitis B and chronic hepatitis C.

Abstract 154: Estimating Treatment Effects in More Than Two Treatment Groups via Propensity Score Weighting: Practical Guidance and Application from Anticoagulant Therapy in Atrial Fibrillation Study

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Gboyega Adeboyeje ◽  
Gosia Sylwestrzak ◽  
John Barron
Keyword(s):  
Atrial Fibrillation ◽  
Propensity Score ◽  
Anticoagulant Therapy ◽  
Observational Studies ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Multinomial Logistic Regression ◽  
Baseline Risk ◽  
Risk Scores ◽  
Treatment Groups

Background: The methods for estimating and assessing propensity scores in the analysis of treatment effects between two treatment arms in observational studies have been well described in the outcomes research methodology literature. However, in practice, the decision makers may need information on the comparative effectiveness of more than two treatment strategies. There’s little guidance on the estimation of treatment effects using inverse probability of treatment weights (IPTW) in studies where more than two treatment arms are to be compared. Methods: Data from an observational cohort study on anticoagulant therapy in atrial fibrillation is used to illustrate the practical steps involved in estimating the IPTW from multiple propensity scores and assessing the balance achieved under certain assumptions. For all patients in the study, we estimated the propensity score for the treatment each patient received using a multinomial logistic regression. We used the inverse of the propensity scores as weights in Cox proportional hazards to compare study outcomes for each treatment group Results: Before IPTW adjustment, there were large and statistically significant baseline differences between treatment groups in terms of demographic, plan type, and clinical characteristics including validated stroke and bleeding risk scores. After IPTW, there were no significant differences in all measured baseline risk factors. In unadjusted estimates of stroke outcome, there were large differences between dabigatran [Hazard ratio, HR, 0.59 (95% CI: 0.53 - 0.66)], apixaban [HR, 0.69 (CI: 0.57, 0.83)], rivaroxaban [HR, 0.60 (CI: 0.53 0.68)] and warfarin users. After IPTW, estimated stroke risk differences were significantly reduced or eliminated between dabigatran [HR, 0.89 (CI: 0.80, 0.98)], apixaban [HR, 0.92 (0.76, 1.10)], rivaroxaban [HR, 0.84 (CI: 0.75, 0.95)] and warfarin users. Conclusions: Our results showed IPTW methods, correctly employed under certain assumptions, are practical and relatively simple tools to control for selection bias and other baseline differences in observational studies evaluating the comparative treatment effects of more than two treatment arms. When preserving sample size is important and in the presence of time-varying confounders, IPTW methods have distinct advantages over propensity matching or adjustment.

scholarly journals Why Propensity Scores Should Not Be Used for Matching

2019 ◽  
Vol 27 (4) ◽  
pp. 435-454 ◽  
Author(s):  
Gary King ◽  
Richard Nielsen
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Propensity Score Matching ◽  
Propensity Scores ◽  
Original Data ◽  
The Other ◽  
Randomized Experiment ◽  
Random Matching ◽  
Popular Method ◽  
Matching Methods

We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM comes from its attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.

scholarly journals Is central obesity associated with diabetic retinopathy in Chinese individuals? An exploratory study

2019 ◽  
Vol 47 (11) ◽  
pp. 5601-5612
Author(s):  
Jian-Bo Zhou ◽  
Jing Yuan ◽  
Xing-Yao Tang ◽  
Wei Zhao ◽  
Fu-Qiang Luo ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Central Obesity ◽  
Propensity Scores ◽  
Meta Analysis ◽  
Before And After ◽  
Independent Association ◽  
Two Stages ◽  
The Relationship

Objective To our knowledge, the independent association between central obesity, defined by waist circumference (WC) or waist-to-hip ratio (WHR), and diabetic retinopathy (DR) remains unknown in Chinese individuals. Method The study was conducted in two stages. First, the relationship between WC or WHR and DR was estimated in a case-control set (DR vs. non-DR) for the whole population before and after propensity score matching. Subsequently, a systematic review and meta-analysis was performed on evidence from the literature to validate the relationship. Results Of 511 eligible patients, DR (N = 156) and non-DR (N = 156) patients with similar propensity scores were included in the propensity score matching analyses. Central obesity (defined by WC) was associated with risk of DR (odds ratio [OR] 1.07, 95% confidence interval [95% CI] (1.03–1.10). The meta-analysis showed that central obesity significantly increased the risk of DR by 12% (OR 1.12, 95% CI 1.02–1.22). Analysis of data from 18 studies showed a significant association between continuous body mass index and risk of proliferative DR (OR 0.95, 95% CI 0.93–0.98; I2 = 50%). Conclusion Central obesity, particularly as defined by WC, is associated with the risk of DR in the Chinese population.

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