scholarly journals Evaluating Glaucoma Treatment Effect on Intraocular Pressure Reduction Using Propensity Score Weighted Regression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mengfei Wu ◽  
Mengling Liu ◽  
Joel S. Schuman ◽  
Yuyan Wang ◽  
Katie A. Lucy ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
Propensity Score ◽  
Selection Bias ◽  
Treatment Effect ◽  
Laser Surgery ◽  
Observational Studies ◽  
Combination Therapies ◽  
Treatment Groups ◽  
Valid Assessment ◽  
Important Evidence

Abstract Observational studies in glaucoma patients can provide important evidence on treatment effects, especially for combination therapies which are often used in reality. But the success relies on the reduction of selection bias through methods such as propensity score (PS) weighting. The objective of this study was to assess the effects of five glaucoma treatments (medication, laser, non-laser surgery (NLS), laser + medication, and NLS + medication) on 1-year intraocular pressure (IOP) change. Data were collected from 90 glaucoma subjects who underwent a single laser, or NLS intervention, and/or took the same medication for at least 6 months, and had IOP measures before the treatment and 12-months after. Baseline IOP was significantly different across groups (p = 0.007) and this unbalance was successfully corrected by the PS weighting (p = 0.81). All groups showed statistically significant PS-weighted IOP reductions, with the largest reduction in NLS group (−6.78 mmHg). Baseline IOP significantly interacted with treatments (p = 0.03), and at high baseline IOP medication was less effective than other treatments. Our findings showed that the 1-year IOP reduction differed across treatment groups and was dependent on baseline IOP. The use of PS-weighted methods reduced treatment selection bias at baseline and allowed valid assessment of the treatment effect in an observational study.

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 Using Dr. Scratch as a Formative Feedback Tool to Assess Computational Thinking

2022 ◽  
pp. 550-572
Author(s):  
Peter Rich ◽  
Samuel Frank Browning
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Propensity Score ◽  
Multiple Linear Regression ◽  
Treatment Effect ◽  
Computational Thinking ◽  
Formative Feedback ◽  
Treatment Groups ◽  
Automatic Feedback ◽  
Maximum Likelihood Technique

This study investigated if using Dr. Scratch as a formative feedback tool would accelerate students' Computational Thinking (CT). Forty-one 4th-6th grade students participated in a 1-hour/week Scratch workshop for nine weeks. We measured pre- and posttest results of the computational thinking test (CTt) between control (n = 18) and treatment groups (n = 23) using three methods: propensity score matching (treatment = .575; control = .607; p = .696), information maximum likelihood technique (treatment effect = -.09; p = .006), and multiple linear regression. Both groups demonstrated significantly increased posttest scores over their pretest (treatment = +8.31%; control = +5.43%), showing that learning to code can increase computational thinking over a 2-month period. In this chapter, we discuss the implications of using Dr. Scratch as a formative feedback tool the possibilities of further research on the use of automatic feedback tools in teaching elementary computational thinking.

scholarly journals Using Dr. Scratch as a Formative Feedback Tool to Assess Computational Thinking

2020 ◽  
pp. 220-242
Author(s):  
Peter Rich ◽  
Samuel Frank Browning
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Propensity Score ◽  
Multiple Linear Regression ◽  
Treatment Effect ◽  
Computational Thinking ◽  
Formative Feedback ◽  
Treatment Groups ◽  
Automatic Feedback ◽  
Maximum Likelihood Technique

This study investigated if using Dr. Scratch as a formative feedback tool would accelerate students' Computational Thinking (CT). Forty-one 4th-6th grade students participated in a 1-hour/week Scratch workshop for nine weeks. We measured pre- and posttest results of the computational thinking test (CTt) between control (n = 18) and treatment groups (n = 23) using three methods: propensity score matching (treatment = .575; control = .607; p = .696), information maximum likelihood technique (treatment effect = -.09; p = .006), and multiple linear regression. Both groups demonstrated significantly increased posttest scores over their pretest (treatment = +8.31%; control = +5.43%), showing that learning to code can increase computational thinking over a 2-month period. In this chapter, we discuss the implications of using Dr. Scratch as a formative feedback tool the possibilities of further research on the use of automatic feedback tools in teaching elementary computational thinking.

Propensity Score Methods for Bias Reduction in Observational Studies of Treatment Effect

2018 ◽  
Vol 44 (2) ◽  
pp. 203-213 ◽  
Author(s):  
Sindhu R. Johnson ◽  
George A. Tomlinson ◽  
Gillian A. Hawker ◽  
John T. Granton ◽  
Brian M. Feldman
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Observational Studies ◽  
Bias Reduction ◽  
Propensity Score Methods

scholarly journals Effect of Caesarean Section on Neonatal Health in Bangladesh after Controlling Selection Bias: Propensity Score Based Analysis

2021 ◽  
Vol 69 (1) ◽  
pp. 23-29
Author(s):  
Md Mahmudur Rahman ◽  
Sabina Sharmin ◽  
Taslim Sazzad Mallick
Keyword(s):  
Caesarean Section ◽  
Propensity Score ◽  
Neonatal Mortality ◽  
Treatment Effect ◽  
Early Initiation ◽  
Treatment Groups ◽  
Early Neonatal Mortality ◽  
Early Initiation Of Breastfeeding ◽  
Weighting Methods ◽  
Initiation Of Breastfeeding

The paper examines the effect of caesarean section (C-section) on early neonatal mortality, neonatal mortality, and early initiation of breastfeeding using Bangladesh Demographic and Health Survey (BDHS), 2014 data. Propensity score matching and weighting methods were used to estimate unbiased estimate of treatment effect. The study demonstrates how conclusion about treatment effect varies with and without having balance in the treatment groups. Standard analysis, without caring about balance, reveals that C-section has no significant impact on early neonatal mortality and neonatal mortality. After applying propensity score adjusted methods, balance was achieved in the treatment groups and it was found that C-section has significant effect on early neonatal mortality and neonatal mortality. However, there was no difference between standard and PS adjusted methods in estimating the effect of C-section on early initiation of breastfeeding. It is concluded that children who were delivered by C-section have significantly lower odds of early neonatal mortality, neonatal mortality, and early initiation of breastfeeding as compared to the children who were not delivered by C-section. Dhaka Univ. J. Sci. 69(1): 23-29, 2021 (January)

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