Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-Observations

Zeng, Shuxi; Li, Fan

doi:10.5705/ss.202021.0175

Cited by 10 publications

(16 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, we show that using the estimated propensity scores from a correctly specified model yields more efficient estimates than using the true propensity scores. This generalises the well‐known analogous result proved for the independent and identically distributed setting (e.g., Hirano et al, 2003) to the spatially and temporally dependent case (see Zeng et al, 2021, for a similar result in a different dependent setting). Thus, even with the estimated propensity score, we can make asymptotically conservative inference based on the variance upper bound derived above.…”

Section: Estimation and Inferencesupporting

confidence: 87%

Causal Inference with Spatio-Temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq

Papadogeorgou

Imai

Lyall

et al. 2022

Journal of the Royal Statistical Society Series B: Statistical Methodology

Self Cite

View full text Add to dashboard Cite

Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio‐temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular stochastic treatment assignment strategy. Our methodology allows for arbitrary patterns of spatial spillover and temporal carryover effects. Using martingale theory, we show that the proposed estimator is consistent and asymptotically normal as the number of time periods increases. We propose a sensitivity analysis for the possible existence of unmeasured confounders, and extend it to the Hájek estimator. Simulation studies are conducted to examine the estimators' finite sample performance. Finally, we illustrate the proposed methods by estimating the effects of American airstrikes on insurgent violence in Iraq from February 2007 to July 2008. Our analysis suggests that increasing the average number of daily airstrikes for up to 1 month may result in more insurgent attacks. We also find some evidence that airstrikes can displace attacks from Baghdad to new locations up to 400 km away.

show abstract

Section: Estimation and Inferencesupporting

confidence: 87%

Causal Inference with Spatio-Temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq

Papadogeorgou

Imai

Lyall

et al. 2022

Journal of the Royal Statistical Society Series B: Statistical Methodology

Self Cite

View full text Add to dashboard Cite

show abstract

“…As noted above, an earlier study examined the use of the bootstrap when using propensity score‐based weighting to estimate hazard ratios 20 . Zeng et al developed a method based on pseudo‐observations for the analysis of survival outcomes when using propensity score weighting 26 . Cheng et al developed estimators for survival functions when using propensity score‐based weights 27 .…”

Section: Discussionmentioning

confidence: 99%

“…20 Zeng et al developed a method based on pseudo-observations for the analysis of survival outcomes when using propensity score weighting. 26 Cheng et al developed estimators for survival functions when using propensity score-based weights. 27 In the current study we focused on estimating the effect of treatment in the overall sample.…”

Section: F I G U R E 7 Case Study-overlap and Balance Diagnosticsmentioning

confidence: 99%

Bootstrap vs asymptotic variance estimation when using propensity score weighting with continuous and binary outcomes

Austin

2022

Statistics in Medicine

View full text Add to dashboard Cite

We used Monte Carlo simulations to compare the performance of asymptotic variance estimators to that of the bootstrap when estimating standard errors of differences in means, risk differences, and relative risks using propensity score weighting. We considered four different sets of weights: conventional inverse probability of treatment weights with the average treatment effect (ATE) as the target estimand, weights for estimating the average treatment effect in the treated (ATT), matching weights, and overlap weights. We considered sample sizes ranging from 250 to 10 000 and allowed the prevalence of treatment to range from 0.1 to 0.9. We found that, when using ATE weights and sample sizes were ≤ 1000, then the use of the bootstrap resulted in estimates of SE that were more accurate than the asymptotic estimates. A similar finding was observed when using ATT weights and sample sizes were ≤ 1000 and the prevalence of treatment was moderate to high. When using matching weights and overlap weights, both the asymptotic estimator and the bootstrap resulted in accurate estimates of SE across all sample sizes and prevalences of treatment. Even when using the bootstrap with ATE weights, empirical coverage rates of confidence intervals were suboptimal when sample sizes were low to moderate and the prevalence of treatment was either very low or very high. A similar finding was observed when using the bootstrap with ATT weights when sample sizes were low to moderate and the prevalence of treatment was very high. K E Y W O R D Sbootstrap, inverse probability of treatment weighting, propensity score, variance estimation INTRODUCTIONPropensity score weighting is a popular causal inference method for estimating the effects of treatments, exposures, and interventions when using observational data. The propensity score is the probability of a subject being treated (vs receiving the control) based on the subject's observed baseline covariates. 1 Propensity score weighting entails constructing weights that are derived from the propensity score. The first set of propensity-score based weights, referred to as inverse probability of treatment weights (IPTW), weight each subject by the inverse of the probability of receiving the treatment that subjectThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…The increasing availability of observational data sources like large registries and electronic health records provides new opportunities to obtain real world comparative effectiveness evidence. Recent efforts have been made to evaluate the comparative effectiveness of multiple treatment approaches on patient survival for high‐risk localized prostate cancer using the large‐scale national cancer database (NCDB) 1‐4 . The complex data structures, however, pose three main challenges for statistical analyses that have not been fully addressed in the extant literature.…”

Section: Introductionmentioning

confidence: 99%

“…Recent efforts have been made to evaluate the comparative effectiveness of multiple treatment approaches on patient survival for high‐risk localized prostate cancer using the large‐scale national cancer database (NCDB). 1 , 2 , 3 , 4 The complex data structures, however, pose three main challenges for statistical analyses that have not been fully addressed in the extant literature.…”

Section: Introductionmentioning

confidence: 99%

A flexible approach for causal inference with multiple treatments and clustered survival outcomes

Ennis

Hogan

2022

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding.

show abstract

Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-Observations

Cited by 10 publications

References 49 publications

Causal Inference with Spatio-Temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq

Causal Inference with Spatio-Temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq

Bootstrap vs asymptotic variance estimation when using propensity score weighting with continuous and binary outcomes

A flexible approach for causal inference with multiple treatments and clustered survival outcomes

Contact Info

Product

Resources

About