Bias in progression‐free survival analysis due to intermittent assessment of progression

Cook, Richard J.; Wen, Lan; Boruvka, Audrey

doi:10.1002/sim.6529

Cited by 20 publications

(26 citation statements)

References 31 publications

(61 reference statements)

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“…While such an assumption is commonly applied, in practice, assessments of progression are intermittent, resulting in different right-censoring times for progression and death and interval-censored progression times. 27 For the model-based approach, the model can be fitted using a likelihood that properly accounts for the intermittent observation. 28,29 Adaptation of the other approaches is less straightforward since PFS is a composite measure of progression, which may be interval censored, and of OS, which is right censored.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the association between progression‐free survival and overall survival in oncology trials using Kendall's τ

Weber

Titman

2018

Statistics in Medicine

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This paper considers methods for estimating the association between progression‐free and overall survival in oncology trials. Copula‐based, nonparametric, and illness‐death model–based methods are reviewed. In addition, the approach based on an underlying illness‐death model is generalized to allow general parametric models. The performance of these methods, in terms of bias and efficiency, is investigated through simulation and also illustrated using data from a clinical trial of treatments for colon cancer. The simulations suggest that the illness‐death model–based method provides good estimates of Kendall's τ across several scenarios. In some situations, copula‐based methods perform well but their performance is sensitive to the choice of copula. The Clayton copula is most appropriate in scenarios, which might realistically reflect an oncology trial, but the use of copula models in practice is questionable.

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Section: Discussionmentioning

confidence: 99%

Quantifying the association between progression‐free survival and overall survival in oncology trials using Kendall's τ

Weber

Titman

2018

Statistics in Medicine

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“…Treating the progression time as interval‐censored is more appropriate, which yields a composite endpoint with one component (progression) subject to interval censoring and another (death) to right censoring. The bias in the regression coefficient estimator of a semiparametric Cox model is examined in Zeng et al, and Boruvka and Cook discuss semiparametric estimation for this problem. The purpose of this article was to develop valid design criteria when analyses are appropriately based on an illness‐death model with the progression status assessed intermittently and deaths are subject to right‐censoring.…”

Section: Discussionmentioning

confidence: 99%

“…We also recognize that Cox models are routinely used for the analysis of progression‐free survival and the intermittent assessment of progression is routinely ignored through use of the surrogate progression time defined as the time of the first positive assessment. Zeng et al showed that such an approach results in biases in the estimates of treatment effect and a loss of power for the associated test. For the second approach, we propose a sample size adjustment based on misspecified Cox models by deriving the limiting behaviour of the naive estimator as in Zeng et al and accommodating the bias and robust large sample variance in the calculations to ensure the power is maintained at the nominal level despite the model misspecification.…”

Section: Design Methods For the Endpoint Of Progression‐free Survivalmentioning

confidence: 99%

“…A further complication is that even progression status is indeterminate for individuals who were progression‐free at their last assessment before their death. Zeng et al study the asymptotic bias of the Kaplan‐Meier estimator of the progression‐free survival distribution and the estimator of treatment effect from a Cox regression model when the time that progression is detected is taken as the actual progression time; the power implications of such analyses are also examined. Frydman and Szarek consider this problem and discuss nonparametric estimation of the progression‐free survival time distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Section 3 reviews the standard approach to analyze the composite progression‐free survival time and the associated sample size formula and derives the formula based on the proposed model and likelihood. A more ad hoc approach to sample size adjustment is also given based on Cox model typically used in this setting, which is misspecified in this framework . Section 4 discusses related design criteria for analyses geared simply towards progression.…”

Section: Introductionmentioning

confidence: 99%

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Design of cancer trials based on progression‐free survival with intermittent assessment

Cook

Lee

2018

Statistics in Medicine

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Therapeutic advances in cancer mean that it is now impractical to performed phase III randomized trials evaluating experimental treatments on the basis of overall survival. As a result, the composite endpoint of progression-free survival has been routinely adopted in recent years as it is viewed as enabling a more timely and cost-effective approach to assessing the clinical benefit of novel interventions. This article considers design of cancer trials directed at the evaluation of treatment effects on progression-free survival. In particular, we derive sample size criteria based on an illness-death model that considers cancer progression and death jointly while accounting for the fact that progression is assessed only intermittently. An alternative approach to design is also considered in which the sample size is derived based on a misspecified Cox model, which uses the documented time of progression as the progression time rather than dealing with the interval censoring. Simulation studies show the validity of the proposed methods.

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Differential frequency in imaging‐based outcome measurement: Bias in real‐world oncology comparative‐effectiveness studies

Adamson

Griffith

et al. 2021

Pharmacoepidemiology and Drug

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Background: Comparative-effectiveness studies using real-world data (RWD) can be susceptible to surveillance bias. In solid tumor oncology studies, analyses of endpoints such as progression-free survival (PFS) are based on progression events detected by imaging assessments. This study aimed to evaluate the potential bias introduced by differential imaging assessment frequency when using electronic health record (EHR)-derived data to investigate the comparative effectiveness of cancer therapies.Methods: Using a nationwide de-identified EHR-derived database, we first analyzed imaging assessment frequency patterns in patients diagnosed with advanced nonsmall cell lung cancer (aNSCLC). We used those RWD inputs to develop a discrete event simulation model of two treatments where disease progression was the outcome and PFS was the endpoint. Using this model, we induced bias with differential imaging assessment timing and quantified its effect on observed versus true treatment effectiveness. We assessed percent bias in the estimated hazard ratio (HR). Results:The frequency of assessments differed by cancer treatment types. In simulated comparative-effectiveness studies, PFS HRs estimated using real-world imaging assessment frequencies differed from the true HR by less than 10% in all scenarios (range: 0.4% to À9.6%). The greatest risk of biased effect estimates was found comparing treatments with widely different imaging frequencies, most exaggerated in disease settings where time to progression is very short.Conclusions: This study provided evidence that the frequency of imaging assessments to detect disease progression can differ by treatment type in real-world patients with cancer and may induce some bias in comparative-effectiveness studies in some situations.

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Bias in progression‐free survival analysis due to intermittent assessment of progression

Cited by 20 publications

References 31 publications

Quantifying the association between progression‐free survival and overall survival in oncology trials using Kendall's τ

Quantifying the association between progression‐free survival and overall survival in oncology trials using Kendall's τ

Design of cancer trials based on progression‐free survival with intermittent assessment

Differential frequency in imaging‐based outcome measurement: Bias in real‐world oncology comparative‐effectiveness studies

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