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< .01) and increasing life satisfaction (B= .12, p< .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< .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.

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.

scholarly journals ASPECTS OF THE USE OF PROPENSITY SCORE MATCHING METHODS IN NEUROSURGERY

2020 ◽  
Vol 19 (2) ◽  
pp. 154-159
Author(s):  
ALEKSANDR V. KRUTKO ◽  
SHAMIL A. AKHMETYANOV ◽  
KIRILLYU ORLOV ◽  
VICTOR S. GLADKIKH ◽  
ANDREY V. MOSKALEV
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Prospective Studies ◽  
Propensity Scores ◽  
Real Life ◽  
Level Of Evidence ◽  
Randomized Controlled ◽  
Study Results ◽  
Systematic Selection ◽  
Baseline Covariates

ABSTRACT Objective Observational studies and register data provide researchers with ample opportunities to obtain answers to questions that randomized controlled trials cannot answer for organizational or ethical reasons. One of the most common tools for solving this problem is the use of propensity score matching (PSM) methods. The purposes of our study were to compare various models and algorithms for selecting PSM parameters, using retrospective clinical data, and to compare the results obtained using the PSM method with those of prospective studies. Methods The results of two studies (randomized prospective and retrospective) conducted at the Novosibirsk Research Institute of Traumatology and Orthopedics were used for comparative analysis. The trials aimed to study the effectiveness and safety of surgical treatment of degenerative dystrophic lesions in the lumbar spine. We compared the results using the recommended PSM parameters (caliper=0.2 and 0.6) the propensity score is the probability of assignment to one treatment conditional on a subject’s measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper widthand the caliper values often used in real-life studies (0.05, 0.1, 0.25, 0.5, and 0.8) with the those obtained in a similar prospective study. Results After eliminating systematic selection bias, the results of the retrospective and randomized prospective studies were qualitatively comparable. Conclusion The results of this study provide recommendations for the use of PSM: when evaluating efficacy scores in neurosurgical studies (with a sample size < 150 patients), we recommend matching on the logit of the propensity score using calipers of width equal to 0.6 of the standard deviation of the logit of the propensity score. Level of evidence V; Type of study is expert opinion.

scholarly journals Using Propensity Score Matching Technique to Estimate Utilization and Costs of General Practitioners’ Services associated with Alzheimer’s Disease

10.36469/9827 ◽  
2016 ◽  
Vol 4 (1) ◽  
pp. 67-79
Author(s):  
Rajan Sharma ◽  
Elizaveta Sopina ◽  
Jan Sørensen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
General Practitioners ◽  
Service Use ◽  
Propensity Scores ◽  
Service Utilisation ◽  
Gp Services ◽  
And Control

Objective: General practitioners (GPs) play an important role in caring for people with Alzheimer’s disease (AD). However, the cost and the extent of service utilization from GPs due to AD patients are difficult to assess. This study aimed to explore the principles of propensity score matching (PSM) technique to assess the additional GP service use and cost imposed by AD in persons aged ≥60 years in Denmark. Design: PSM was used to estimate the additional use and cost of GP services attributable to AD. Case and control baseline characteristics were compared with and without the application of PSM. Propensity scores were then estimated using the generalized boosted model, a multivariate, nonparametric and automated algorithm technique. Setting: Observational data from Statistics Denmark registry. Subjects: 3368 cases and 3368 controls; cases with AD were defined as patients with diagnoses G30 and F00 and/or those with primary care prescriptions for anti-AD drugs from the years 2004 until 2009. Main Outcome Measures: GP service utilisation and costs attributable to AD. Results: PSM brought a large improvement to the balance of observed covariates among the cases and control groups. AD patients received around 20% more GP services and utilized services that cost 15% more than non-AD controls during a calendar year. Conclusion: AD patients utilize more GP services and incur higher costs as compared to their matched controls. The PSM technique can be an effective tool to reduce imbalance of observable confounders from register based data and improve the estimations.

scholarly journals Use of Propensity Score Matching to Create Counterfactual Group to Assess Potential HIV Prevention Interventions

2020 ◽  
Author(s):  
Andrew Abaasa ◽  
Yunia Mayanja ◽  
Gershim Asiki ◽  
Matt Price ◽  
Patricia Fast ◽  
...  
Keyword(s):  
Hiv Prevention ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Sex Workers ◽  
Vaccine Efficacy ◽  
Propensity Scores ◽  
Source Population ◽  
Hiv Incidence ◽  
Efficacy Trial ◽  
Baseline Characteristics

Abstract BackgroundThe design of HIV prevention trials in the context of effective HIV preventive methods is a challenge. Alternate designs, including using non-randomised ‘observational control arms’ have been proposed. We used HIV simulated vaccine efficacy trials (SiVETs) to show pitfalls that may arise from using such observational controls and suggest how to conduct the analysis in the face of the pitfalls.MethodsTwo SiVETs were nested within previously established observational cohorts of fisherfolk and female sex workers (FSW) in Uganda. SiVET participants received a licensed Hepatitis B vaccine in a schedule (0, 1 and 6 months) similar to that for a possible HIV vaccine efficacy trial. All participants received HIV counselling and testing every quarter for one year to assess HIV incidence rate ratio (IRR) between SiVET and non-SiVET (observational data). Propensity scores, conditional on baseline characteristics were calculated for SiVET participation and matched between SiVET and non-SiVET in the period before and during the SiVET study. We compared IRR before and after propensity score matching (PSM).Results In total, 3989 participants were enrolled into observational cohorts prior to SiVET, (1575 FF prior to Jul 2012 and 2414 FSW prior to Aug 2014). SiVET enrolled 572 participants (Jul 2012 to Apr 2014 in FF & Aug 2014 to Apr 2017 in FSW), with 953 non-SiVET participants observed in the SiVET concurrent period and 2928 from the pre-SiVET period (before Jul 2012 in FF or before Apr 2014 in FSW). Imbalances in baseline characteristics were observed between SiVET and non-SiVET participants in both periods before PSM. Similarly, HIV incidence was lower in SiVET than non-SiVET; SiVET-concurrent period, IRR= 0.59, 95%CI: 0.31-0.68, p=0.033 and pre-SiVET period, IRR= 0.77, 95%CI: 0.43-1.29, p=0.161. After PSM, participants baseline characteristics were comparable and there were minimal differences in HIV incidence between SiVET and non-SiVET participants. ConclusionThe process of screening for eligibility for efficacy trial selects participants with baseline characteristics different from the source population, confounding any observed differences in HIV incidence. PSM can be a useful tool to adjust the imbalance in the measured participants’ baseline characteristics creating a counterfactual group to estimate the effect of interventions on HIV incidence.

scholarly journals Estimation of Average Treatment Effects Based on Propensity Scores

2002 ◽  
Vol 2 (4) ◽  
pp. 358-377 ◽  
Author(s):  
Sascha O. Becker ◽  
Andrea Ichino
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Labor Economics ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Short Overview ◽  
Average Treatment ◽  
Matching Estimators ◽  
Average Treatment Effects ◽  
Supported Work

In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature, and we provide a set of Stata programs, which we illustrate using the National Supported Work (NSW) demonstration widely known in labor economics.

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.

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