Semiparametric Transformation Models for Causal Inference in Time-to-Event Studies with All-or-Nothing Compliance

Yu, Wen; Chen, Kani; Sobel, Michael E.; Ying, Zhiliang

doi:10.1111/rssb.12072

Cited by 22 publications

(23 citation statements)

References 36 publications

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“…Of note, some of the estimands (ACE, RACE, SPCE, and SQE) in this paper are applicable without the conventional proportional hazards assumption. This is useful because alternatives to using the hazard ratio for between‐group comparison of survival have received increasing attention in the literature …”

Section: Discussionmentioning

confidence: 99%

On the propensity score weighting analysis with survival outcome: Estimands, estimation, and inference

Mao

Yang

et al. 2018

Statistics in Medicine

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Propensity score analysis is widely used in observational studies to adjust for confounding and estimate the causal effect of a treatment on the outcome. When the outcome is survival time, there are special considerations on the definition of the causal estimand, point, and variance estimation that have not been thoroughly studied in the literature. We investigate propensity score analysis of survival data with a class of weighting methods. We consider the following estimands in the two-sample context: average survival time, restricted average survival time, survival probability, survival quantile, and the marginal hazard ratio. We propose a unified analytic framework to obtain the point and variance estimators. The proposed methodology properly adjusts for the sampling variability in the estimated propensity scores. Extensive simulations show that the point and variance estimators possess desired finite sample properties and demonstrate better numerical performance than some existing weighting and matching methods commonly used in the literature. The proposed methodology is illustrated with data from a breast cancer study.

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Section: Discussionmentioning

confidence: 99%

On the propensity score weighting analysis with survival outcome: Estimands, estimation, and inference

Mao

Yang

et al. 2018

Statistics in Medicine

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“…This review includes 20 papers describing 12 methods and 8 extensions to those methods. 22,31–49 In total, the searches resulted in 4472 records (Figure 1). The included papers were published between 1992 and 2018 (inclusive); the majority were published in the Statistics in Medicine journal (30%) and Biometrics journal (25%).…”

Section: Resultsmentioning

confidence: 99%

Statistical Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Systematic Review of Methodological Papers

et al. 2019

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Introduction. Medication nonadherence can have a significant negative impact on treatment effectiveness. Standard intention-to-treat analyses conducted alongside clinical trials do not make adjustments for nonadherence. Several methods have been developed that attempt to estimate what treatment effectiveness would have been in the absence of nonadherence. However, health technology assessment (HTA) needs to consider effectiveness under real-world conditions, where nonadherence levels typically differ from those observed in trials. With this analytical requirement in mind, we conducted a review to identify methods for adjusting estimates of treatment effectiveness in the presence of patient nonadherence to assess their suitability for use in HTA. Methods. A “Comprehensive Pearl Growing” technique, with citation searching and reference checking, was applied across 7 electronic databases to identify methodological papers for adjusting time-to-event outcomes for nonadherence using individual patient data. A narrative synthesis of identified methods was conducted. Methods were assessed in terms of their ability to reestimate effectiveness based on alternative, suboptimal adherence levels. Results. Twenty relevant methodological papers covering 12 methods and 8 extensions to those methods were identified. Methods are broadly classified into 4 groups: 1) simple methods, 2) principal stratification methods, 3) generalized methods (g-methods), and 4) pharmacometrics-based methods using pharmacokinetics and pharmacodynamics (PKPD) analysis. Each method makes specific assumptions and has associated limitations. Five of the 12 methods are capable of adjusting for real-world nonadherence, with only g-methods and PKPD considered appropriate for HTA. Conclusion. A range of statistical methods is available for adjusting estimates of treatment effectiveness for nonadherence, but most are not suitable for use in HTA. G-methods and PKPD appear to be more appropriate to estimate effectiveness in the presence of real-world adherence.

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“…Li and Gray (2016) further proposed an EM algorithm for the full likelihood based estimation. Yu et al (2015) tackled the problem of estimating causal estimands including the complier average causal effect, complier survival probability, and complier quantile causal effect under the semiparametric transformation model. They adapted the nonparametric likelihood estimation technique of Zeng and Lin (2007), and provided an EM algorithm for implementing the proposed estimation as well as theoretical justifications.…”

Section: Introductionmentioning

confidence: 99%

Causal Proportional Hazards Estimation with a Binary Instrumental Variable

Kianian¹,

Kim²,

Fine³

et al. 2021

STAT SINICA

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Instrumental variables (IV) are a useful tool for estimating causal effects in the presence of unmeasured confounding. IV methods are well developed for uncensored outcomes, particularly for structural linear equation models, where simple two-stage estimation schemes are available. The extension of these methods to survival settings is challenging, partly because of the nonlinearity of the popular survival regression models and partly because of the complications associated with right censoring or other survival features. We develop a simple causal hazard ratio estimator in a proportional hazards model with right censored data. The method exploits a special characterization of IV which enables the use of an intuitive inverse weighting scheme that is generally applicable to more complex survival settings with left truncation, competing risks, or recurrent events. We rigorously establish the asymptotic properties of the estimators, and provide plug-in variance estimators. The proposed method can be implemented in standard software, and is evaluated through extensive simulation studies. We apply the proposed IV method to a data set from the Prostate, Lung, Colorectal and Ovarian cancer screening trial to delineate the causal effect of flexible sigmoidoscopy screening on colorectal cancer survival which may be confounded by informative noncompliance with the assigned screening regimen. keywords: Causal treatment effect; Cox proportional hazards model; Instrumental variable (IV); Noncompliance. + The first two authors have equal contributions to this work

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Semiparametric Transformation Models for Causal Inference in Time-to-Event Studies with All-or-Nothing Compliance

Cited by 22 publications

References 36 publications

On the propensity score weighting analysis with survival outcome: Estimands, estimation, and inference

On the propensity score weighting analysis with survival outcome: Estimands, estimation, and inference

Statistical Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Systematic Review of Methodological Papers

Causal Proportional Hazards Estimation with a Binary Instrumental Variable

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