Statistical methods for estimating and testing treatment effect for multiple treatment groups in observational studies.

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.

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Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge

Observational Studies ◽

10.1353/obs.2019.0000 ◽

2019 ◽

Vol 5 (2) ◽

pp. 21-35

Author(s):

Carlos Carvalho ◽

Avi Feller ◽

Jared Murray ◽

Spencer Woody ◽

David Yeager

Keyword(s):

Treatment Effect ◽

Observational Studies

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Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽

10.1093/biomet/asaa071 ◽

2020 ◽

Author(s):

Oliver Dukes ◽

Stijn Vansteelandt

Keyword(s):

Sample Size ◽

Confidence Intervals ◽

Treatment Effect ◽

Observational Studies ◽

Treatment Effects ◽

Treatment Selection ◽

High Dimensional ◽

Selection Mechanism ◽

Dimensional Models ◽

Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

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Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

Complexity ◽

10.1155/2021/6685951 ◽

2021 ◽

Vol 2021 ◽

pp. 1-29

Author(s):

Keyi Mou ◽

Zhiming Li

Keyword(s):

Binary Data ◽

Estimation Error ◽

Control Group ◽

Homogeneity Test ◽

Optimal Algorithms ◽

Score Statistic ◽

Correlated Binary Data ◽

Treatment Groups ◽

Multiple Treatment ◽

Rate Score

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.

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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

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.9.suppl_2.154 ◽

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.

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