Propensity score based methods for estimating the treatment effects based on observational studies.

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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Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision

BMC Medical Research Methodology ◽

10.1186/s12874-021-01383-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chinmay Belthangady ◽

Will Stedden ◽

Beau Norgeot

Keyword(s):

Propensity Score ◽

Causal Inference ◽

Real World ◽

Observational Studies ◽

Treatment Effects ◽

Infant Health ◽

Development Program ◽

Health Condition ◽

Simultaneous Intervention ◽

Inference Methods

Abstract Background Observational studies are increasingly being used to provide supplementary evidence in addition to Randomized Control Trials (RCTs) because they provide a scale and diversity of participants and outcomes that would be infeasible in an RCT. Additionally, they more closely reflect the settings in which the studied interventions will be applied in the future. Well-established propensity-score-based methods exist to overcome the challenges of working with observational data to estimate causal effects. These methods also provide quality assurance diagnostics to evaluate the degree to which bias has been removed and the estimates can be trusted. In large medical datasets it is common to find the same underlying health condition being treated with a variety of distinct drugs or drug combinations. Conventional methods require a manual iterative workflow, making them scale poorly to studies with many intervention arms. In such situations, automated causal inference methods that are compatible with traditional propensity-score-based workflows are highly desirable. Methods We introduce an automated causal inference method BCAUS, that features a deep-neural-network-based propensity model that is trained with a loss which penalizes both the incorrect prediction of the assigned treatment as well as the degree of imbalance between the inverse probability weighted covariates. The network is trained end-to-end by dynamically adjusting the loss term for each training batch such that the relative contributions from the two loss components are held fixed. Trained BCAUS models can be used in conjunction with traditional propensity-score-based methods to estimate causal treatment effects. Results We tested BCAUS on the semi-synthetic Infant Health & Development Program dataset with a single intervention arm, and a real-world observational study of diabetes interventions with over 100,000 individuals spread across more than a hundred intervention arms. When compared against other recently proposed automated causal inference methods, BCAUS had competitive accuracy for estimating synthetic treatment effects and provided highly concordant estimates on the real-world dataset but was an order-of-magnitude faster. Conclusions BCAUS is directly compatible with trusted protocols to estimate treatment effects and diagnose the quality of those estimates, while making the established approaches automatically scalable to an arbitrary number of simultaneous intervention arms without any need for manual iteration.

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Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison

Stats ◽

10.3390/stats4020032 ◽

2021 ◽

Vol 4 (2) ◽

pp. 529-549

Author(s):

Tingting Zhou ◽

Michael R. Elliott ◽

Roderick J. A. Little

Keyword(s):

Propensity Score ◽

Observational Studies ◽

Treatment Effects ◽

Single Measure ◽

Propensity Score Model ◽

Penalized Spline ◽

Treatment Comparison ◽

Control Subjects ◽

Score Model ◽

The Impact

Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects. Confounding control is commonly achieved using the propensity score, defined as the conditional probability of assignment to a treatment given the observed covariates. The propensity score collapses all the observed covariates into a single measure and serves as a balancing score such that the treated and control subjects with similar propensity scores can be directly compared. Common propensity score-based methods include regression adjustment and inverse probability of treatment weighting using the propensity score. We recently proposed a robust multiple imputation-based method, penalized spline of propensity for treatment comparisons (PENCOMP), that includes a penalized spline of the assignment propensity as a predictor. Under the Rubin causal model assumptions that there is no interference across units, that each unit has a non-zero probability of being assigned to either treatment group, and there are no unmeasured confounders, PENCOMP has a double robustness property for estimating treatment effects. In this study, we examine the impact of using variable selection techniques that restrict predictors in the propensity score model to true confounders of the treatment-outcome relationship on PENCOMP. We also propose a variant of PENCOMP and compare alternative approaches to standard error estimation for PENCOMP. Compared to the weighted estimators, PENCOMP is less affected by inclusion of non-confounding variables in the propensity score model. We illustrate the use of PENCOMP and competing methods in estimating the impact of antiretroviral treatments on CD4 counts in HIV+ patients.

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Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

Statistics in Medicine ◽

10.1002/sim.6607 ◽

2015 ◽

Vol 34 (28) ◽

pp. 3661-3679 ◽

Cited By ~ 863

Author(s):

Peter C. Austin ◽

Elizabeth A. Stuart

Keyword(s):

Propensity Score ◽

Observational Studies ◽

Best Practice ◽

Treatment Effects ◽

Inverse Probability ◽

Causal Treatment

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PROPENSITY SCORE MATCHING AS A MODERN STATISTICAL METHOD FOR BIAS CONTROL IN OBSERVATIONAL STUDIES WITH BINARY OUTCOME

Human Ecology ◽

10.33396/1728-0869-2016-5-50-64 ◽

2016 ◽

pp. 50-64 ◽

Cited By ~ 2

Author(s):

A. M. Grjibovski ◽

S. V. Ivanov ◽

М. A. Gorbatova ◽

A. A. Dyussupov

Keyword(s):

Propensity Score ◽

Statistical Method ◽

Propensity Score Matching ◽

Observational Studies ◽

Binary Outcome

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

Statistical Methods in Medical Research ◽

10.1177/09622802211037075 ◽

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.

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