A General Framework for Treatment Effect Estimators Considering Patient Adherence

Qu, Yongming; Fu, Haoda; Luo, Junxiang; Ruberg, Stephen J.

doi:10.1080/19466315.2019.1700157

Cited by 23 publications

(67 citation statements)

References 39 publications

(43 reference statements)

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“…These estimators are consistent for the treatment effect in the adherent strata of interest under some assumptions, and simulations demonstrated that the ACEs provide consistent estimates of the treatment difference for sample sizes that are typical for clinical trials. As the methods are complex and have been previously described, 6 here we only provide a high‐level description of the methods. The following assumptions have been posed for the validity of the ACEs:

normalA 1 : Y = Y ((), 1) T + Y ((), 0) ((), 1 - T)

normalA 2 : Z = Z ((), 1) T + Z ((), 0) ((), 1 - T)

normalA 3 : A = A ((), 1) T + A ((), 0) ((), 1 - T)

normalA 4 : T ⊥ ({},,,,,, Y ((), 1), A ((), 1), Z ((), 1), Y ((), 0), A ((), 0), Z ((), 0)) ∣ X

normalA 5 : A ({}, i) ⊥ ({},,, Y ((), 1), Y ((), 0), Z ((), 1 - i)) ∣ ({},, X, Z ((), i)), \forall i = 0, 1

normalA 6 : Y ((), i) ⊥ Z ((), 1 - i) ∣ ({},, X, Z ((), i)), \forall i = 0, 1

normalA 7 : Z ((), 0) ⊥ Z ((), 1) ∣ X

…”

Section: Methodsmentioning

confidence: 99%

“…The

{\overset{false^}{φ}}_{t} ((), X_{j})

is the expected value of the product of this probability of adherence to the alternative treatment and

{\overset{false^}{ϕ}}_{t} ((), X_{j})

, conditional on X . Again, the details for the construction of

{\overset{false^}{ϕ}}_{t}, {\overset{false^}{φ}}_{t}

, and

{\overset{false^}{h}}_{t}

have previously been described in detail 6 …”

Section: Methodsmentioning

confidence: 99%

“…Those six post‐baseline variables were considered in intermediate covariates Z 1 for Week 12 and Z 2 for Week 26, respectively. The probability of adherence was estimated using a multiplicative probability model 6 …”

Section: Applicationmentioning

confidence: 99%

“…The causal inference framework is required to define the estimand and construct a corresponding estimator. Qu et al propose a theoretical framework for adherence causal estimators (ACEs) based on the causal‐inference framework for the estimands of Estimand 3, 6 but they do not provide details on the implementation of tripartite estimands.…”

Section: Introductionmentioning

confidence: 99%

“…The article is organized in the following way. Section 2 will discuss the statistical methods including the refinement of the tripartite estimands and review the statistical method for the estimation of Estimand 3 as presented by Qu et al 6 Section 3 will cover information relevant to the clinical case study, including the data and specific details for our implementation of the tripartite analysis, and will present the analysis results. Finally, Section 4 will discuss our learnings, additional considerations, and potential for further research.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Luo

Ruberg³

2020

Pharmaceutical Statistics

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Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data.The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes.

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normalA 1 : Y = Y ((), 1) T + Y ((), 0) ((), 1 - T)

normalA 2 : Z = Z ((), 1) T + Z ((), 0) ((), 1 - T)

normalA 3 : A = A ((), 1) T + A ((), 0) ((), 1 - T)

normalA 4 : T ⊥ ({},,,,,, Y ((), 1), A ((), 1), Z ((), 1), Y ((), 0), A ((), 0), Z ((), 0)) ∣ X

normalA 5 : A ({}, i) ⊥ ({},,, Y ((), 1), Y ((), 0), Z ((), 1 - i)) ∣ ({},, X, Z ((), i)), \forall i = 0, 1

normalA 6 : Y ((), i) ⊥ Z ((), 1 - i) ∣ ({},, X, Z ((), i)), \forall i = 0, 1

normalA 7 : Z ((), 0) ⊥ Z ((), 1) ∣ X

…”

Section: Methodsmentioning

confidence: 99%

“…The

{\overset{false^}{φ}}_{t} ((), X_{j})

is the expected value of the product of this probability of adherence to the alternative treatment and

{\overset{false^}{ϕ}}_{t} ((), X_{j})

, conditional on X . Again, the details for the construction of

{\overset{false^}{ϕ}}_{t}, {\overset{false^}{φ}}_{t}

, and

{\overset{false^}{h}}_{t}

have previously been described in detail 6 …”

Section: Methodsmentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Luo

Ruberg³

2020

Pharmaceutical Statistics

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CITIES: Clinical trials with intercurrent events simulator

Wahab,

Qu,

Michiels

et al. 2023

Biometrical J

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Although clinical trials are often designed with randomization and well‐controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real‐life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data‐generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real‐life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real‐life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real‐life trials.

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Defining estimands using a mix of strategies to handle intercurrent events in clinical trials

Shurzinske

Sethuraman

2020

Pharmaceutical Statistics

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SUMMARY Randomized controlled trials (RCTs) are the gold standard for evaluation of the efficacy and safety of investigational interventions. If every patient in an RCT were to adhere to the randomized treatment, one could simply analyze the complete data to infer the treatment effect. However, intercurrent events (ICEs) including the use of concomitant medication for unsatisfactory efficacy, treatment discontinuation due to adverse events, or lack of efficacy may lead to interventions that deviate from the original treatment assignment. Therefore, defining the appropriate estimand (the appropriate parameter to be estimated) based on the primary objective of the study is critical prior to determining the statistical analysis method and analyzing the data. The International Council for Harmonisation (ICH) E9 (R1), adopted on November 20, 2019, provided five strategies to define the estimand: treatment policy, hypothetical, composite variable, while on treatment, and principal stratum. In this article, we propose an estimand using a mix of strategies in handling ICEs. This estimand is an average of the “null” treatment difference for those with ICEs potentially related to safety and the treatment difference for the other patients if they would complete the assigned treatments. Two examples from clinical trials evaluating antidiabetes treatments are provided to illustrate the estimation of this proposed estimand and to compare it with the estimates for estimands using hypothetical and treatment policy strategies in handling ICEs.

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A General Framework for Treatment Effect Estimators Considering Patient Adherence

Cited by 23 publications

References 39 publications

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

CITIES: Clinical trials with intercurrent events simulator

Defining estimands using a mix of strategies to handle intercurrent events in clinical trials

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