Bootstrapping complex time‐to‐event data without individual patient data, with a view toward time‐dependent exposures

Bluhmki, Tobias; Putter, Hein; Allignol, Arthur; Beyersmann, Jan

doi:10.1002/sim.8177

Cited by 11 publications

(12 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Categorical time‐dependent covariates such as a CH can be included in a multistate model through transitions from one transient state to another . Then, the multistate model covers both, a time‐dependent covariate process and time‐to‐event endpoints (through the time until the multistate process enters an absorbing state).…”

Section: Survival Multistate Modelsmentioning

confidence: 99%

“…In particular, only real life event times such as PFS are modeled, and the algorithm does not require latent failure times such as “time to progression” and “time to death without prior progression”. Advantages of the multistate model approach are discussed in more detail in Meller et al for the purpose of jointly modeling PFS and OS and in Bluhmki et al with a view toward modeling of time‐dependent covariates.…”

Section: Survival Multistate Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Multistate modeling of clinical hold in randomized clinical trials

Nießl¹,

Beyersmann²,

Loos

2019

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

A clinical hold order by the Food and Drug Administration (FDA) to the sponsor of a clinical trial is a measure to delay a proposed or to suspend an ongoing clinical investigation. The phase III clinical trial START serves as motivating data example to explore implications and potential statistical approaches for a trial continuing after a clinical hold is lifted. In spite of a modified intention-to-treat (ITT) analysis introduced to account for the clinical hold by excluding patients potentially affected most by the clinical hold, results of the trial did not show a significant improvement of overall survival duration, and the question remains whether the negative result was an effect of the clinical hold. In this paper, we propose a multistate model incorporating the clinical hold as well as disease progression as intermediate events to investigate the impact of the clinical hold on the treatment effect. Moreover, we consider a simple counterfactual censoring approach as alternative strategy to the modified ITT analysis to deal with a clinical hold. Using a realistic simulation study informed by the START data and with a design based on our multistate model, we show that the modified ITT analysis used in the START trial was reasonable. However, the censoring approach will be shown to have some benefits in terms of power and flexibility.

show abstract

Section: Survival Multistate Modelsmentioning

confidence: 99%

Section: Survival Multistate Modelsmentioning

confidence: 99%

Multistate modeling of clinical hold in randomized clinical trials

Nießl¹,

Beyersmann²,

Loos

2019

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…We employed a latent time approach for our simulation method of prediction, also used in Crowther and Lambert; 17 however, Bluhmki et al 32 have highlighted the disconnect between simulating multiple unobservable event times and the real‐world setting of only observing one outcome. We agree, but would argue that such a hypothetical construct does not preclude the use of the algorithm, and as pointed out by Allignol et al, 33 quantities obtained from either method are computationally valid.…”

Section: Discussionmentioning

confidence: 99%

A multistate model incorporating estimation of excess hazards and multiple time scales

Weibull

Lambert

Eloranta

et al. 2021

Statistics in Medicine

View full text Add to dashboard Cite

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code. K E Y W O R D Scompeting risks, excess rate, multistate models, multiple timescales, relative survival, survival analysis INTRODUCTIONMarked improvement in cancer patient survival in recent decades has increased the importance of studying treatment side-effects, 1 that is, late effects. For example, chemotherapy and radiotherapy increase the risk of both secondary malignancies and cardiovascular disease (CVD). 2,3 As therapies are developed and adapted to maximize the chances of cure while reducing risk of treatment toxicity, it is of interest to simultaneously estimate real-world morbidity and mortality.This 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

“…Instead of a joint frailty parametrization for the data‐generating process simulations could also be performed by bootstrapping data of historical trials. This has recently been shown to be a promising approach to generate realistic data of complex time‐to‐event processes 46 …”

Section: Discussionmentioning

confidence: 99%

A comparison of semiparametric approaches to evaluate composite endpoints in heart failure trials

Toenges

Mütze

Jahn‐Eimermacher

2021

Statistics in Medicine

View full text Add to dashboard Cite

In heart failure (HF) trials efficacy is usually assessed by a composite endpoint including cardiovascular death (CVD) and heart failure hospitalizations (HFHs), which has traditionally been evaluated with a time‐to‐first‐event analysis based on a Cox model. As a considerable fraction of events is ignored that way, methods for recurrent events were suggested, among others the semiparametric proportional rates models by Lin, Wei, Yang, and Ying (LWYY model) and Mao and Lin (Mao‐Lin model). In our work we apply least false parameter theory to explain the behavior of the composite treatment effect estimates resulting from the Cox model, the LWYY model, and the Mao‐Lin model in clinically relevant scenarios parameterized through joint frailty models. These account for both different treatment effects on the two outcomes (CVD, HFHs) and the positive correlation between their risk rates. For the important setting of beneficial outcome‐specific treatment effects we show that the correlation results in composite treatment effect estimates, which are decreasing with trial duration. The estimate from the Cox model is affected more by the attenuation than the estimates from the recurrent event models, which both demonstrate very similar behavior. Since the Mao‐Lin model turns out to be less sensitive to harmful effects on mortality, we conclude that, among the three investigated approaches, the LWYY model is the most appropriate one for the composite endpoint in HF trials. Our investigations are motivated and compared with empirical results from the PARADIGM‐HF trial (ClinicalTrials.gov identifier: NCT01035255), a large multicenter trial including 8399 chronic HF patients.

show abstract

Bootstrapping complex time‐to‐event data without individual patient data, with a view toward time‐dependent exposures

Cited by 11 publications

References 75 publications

Multistate modeling of clinical hold in randomized clinical trials

Multistate modeling of clinical hold in randomized clinical trials

A multistate model incorporating estimation of excess hazards and multiple time scales

A comparison of semiparametric approaches to evaluate composite endpoints in heart failure trials

Contact Info

Product

Resources

About