A modified two-stage approach for joint modelling of longitudinal and time-to-event data

Hương, Phạm Thị Thu; Nur, Darfiana; Pham, Hoa; Branford, Alan J.

doi:10.1080/00949655.2018.1518449

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…Moreover, modeling the transitions between different health states instead of the time to a single event can make the issue more complicated. e primary joint models have been based on the twostage approaches in which the likelihood calculation is implemented in two steps [1,8,9]. ese initial approaches are not faced with the problem of computational difficulties on the full joint likelihood calculation [1].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Alafchi

Mahjub

Tapak

et al. 2021

Journal of Probability and Statistics

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In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their association with the survival process is of interest. In the present study, we applied a joint model of various longitudinal biomarkers and transitions between different health statuses in patients who underwent renal transplantation. The full joint likelihood approaches are faced with the complexities in computation of the likelihood. So, here, we have proposed two-stage modeling of multivariate longitudinal outcomes and multistate conditions to avoid these complexities. The proposed model showed reliable results compared to the joint model in case of joint modeling of univariate longitudinal biomarker and the multistate process.

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Section: Introductionmentioning

confidence: 99%

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Alafchi

Mahjub

Tapak

et al. 2021

Journal of Probability and Statistics

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“…A two‐stage design is proposed to assign new patients to desirable treatment through adaptive randomization. Joint modeling of longitudinal measurements and survival outcome has been considered in the literature 28‐37 . In this article, we adapt these methods to accommodate the unique features of immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

“…Joint modeling of longitudinal measurements and survival outcome has been considered in the literature. [28][29][30][31][32][33][34][35][36][37] In this article, we adapt these methods to accommodate the unique features of immunotherapy. To the best of our knowledge, this article provides the first phase II design for immunotherapy that jointly accounts for longitudinal immune response and time-to-event efficacy.…”

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confidence: 99%

BILITE: A Bayesian randomized phase II design for immunotherapy by jointly modeling the longitudinal immune response and time‐to‐event efficacy

Guo

Zang

2020

Statistics in Medicine

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Immunotherapy-treatments that target a patient's immune system-has attracted considerable attention in cancer research. Its recent success has led to generation of novel immunotherapeutic agents that need to be evaluated in clinical trials. Two unique features of immunotherapy are the immune response and the fact that some patients may achieve long-term durable response. In this article, we propose a two-arm Bayesian adaptive randomized phase II clinical trial design for immunotherapy that jointly models the longitudinal immune response and time-to-event efficacy (BILITE), with a fraction of patients assumed to be cured by the treatment. The longitudinal immune response is modeled using hierarchical nonlinear mixed-effects models with possibly different trajectory patterns for the cured and susceptible groups. Conditional on the immune response trajectory, the time-to-event efficacy data for patients in the susceptible group is modeled via a time-dependent Cox-type regression model. We quantify the desirability of the treatment using a utility function and propose a two-stage design to adaptively randomize patients to treatments and make treatment recommendations at the end of the trial. Simulation studies show that compared with a conventional design that ignores the immune response, BILITE yields superior operating characteristics in terms of the ability to identify promising agents and terminate the trial early for futility.

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“…This strategy is usually simple to implement and allows us to use flexible models available in standard longitudinal and survival analyses packages (separately). In the current literature of joint models, there are different proposals for two-stage methods in both frequentist [ 20 , 21 , 22 , 23 , 24 ] and Bayesian [ 25 , 26 , 27 ] approaches. These two-stage procedures speed up processing time by estimating two less complex submodels than the joint model.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

Leiva-Yamaguchi

Álvares

2020

Entropy

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Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.

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A modified two-stage approach for joint modelling of longitudinal and time-to-event data

Cited by 8 publications

References 21 publications

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data

BILITE: A Bayesian randomized phase II design for immunotherapy by jointly modeling the longitudinal immune response and time‐to‐event efficacy

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

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