Propensity‐score‐based meta‐analytic predictive prior for incorporating real‐world and historical data

Liu, Meizi; Bunn, Veronica; Hupf, Bradley; Lin, Junjing; Lin, Jianchang

doi:10.1002/sim.9095

Cited by 28 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some methods, which have characteristics similar to those of nonconcurrent borrowing, have been proposed on the basis of covariate adjustment. [27][28][29][30][31] Regarding the borrowing strength parameters of each method, we used the recommended values in the literature. In fact, there are two common ways to determine these parameters: empirically specifying and objectively estimating.…”

Section: Discussionmentioning

confidence: 99%

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Chen

Zhang

et al. 2022

JCO Precision Oncology

View full text Add to dashboard Cite

PURPOSE With deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials. METHODS We divided the borrowing information scenarios into concurrent and nonconcurrent scenarios according to whether the data to be borrowed are observed at the same time as in the current trial or not. Then, we provided an overview of the methods in each scenario. Performance comparison of different methods is carried out with regard to the type I error and power. RESULTS As demonstrated by the simulation results in each borrowing scenario, the Bayesian hierarchical model and its extensions are more appropriate for concurrent borrowing. The simulation results demonstrate that the Bayesian hierarchical model shows great advantages when the arms are homogeneous. However, such a method should be adopted with caution when heterogeneity exists. We recommend the other methods, considering heterogeneity. Borrow information from informative priors is more suggested for nonconcurrent borrowing scenarios. Multisource exchangeability models are more suitable for multiple historical trials, while meta-analytic-predictive prior should be carefully applied. CONCLUSION Bayesian information borrowing is useful and can improve the efficiency of clinical trial designs. However, we should carefully choose an appropriate information borrowing method when facing a practical innovative oncology trial, as an appropriate method is essential to provide ideal design performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Chen

Zhang

et al. 2022

JCO Precision Oncology

View full text Add to dashboard Cite

show abstract

“…The degree of borrowing from the external-control cohort depends on the similarity between the external-control and the concurrent-control arm, and assessment of the similarity and adjusting accordingly differentiates hybrid-control methods 22 . For example, novel methods that use power priors 23 or meta-analytic predictive priors 24 with PS-based selection of patients can assess various degrees of between-trial heterogeneity, and adaptively adjusting the amount of borrowing of external information. Here we leveraged two methods: the first was the static borrowing method under the frequentist paradigm, and the second was the dynamic borrowing method within the Bayesian framework.…”

Section: Discussionmentioning

confidence: 99%

Hybrid-control arm construction using historical trial data for an early-phase, randomized controlled trial in metastatic colorectal cancer

Ferro

Mhatre³

et al. 2022

Commun Med

View full text Add to dashboard Cite

Background Treatment for metastatic colorectal cancer patients beyond the second line remains challenging, highlighting the need for early phase trials of combination therapies for patients who had disease progression during or following two prior lines of therapy. Leveraging hybrid control design in these trials may preserve the benefits of randomization while strengthening evidence by integrating historical trial data. Few examples have been established to assess the applicability of such design in supporting early phase metastatic colorectal cancer trials. Methods MORPHEUS-CRC is an umbrella, multicenter, open-label, phase Ib/II, randomized, controlled trial (NCT03555149), with active experimental arms ongoing. Patients enrolled were assigned to a control arm (regorafenib, 15 patients randomized and 13 analysed) or multiple experimental arms for immunotherapy-based treatment combinations. One experimental arm (atezolizumab + isatuximab, 15 patients randomized and analysed) was completed and included in the hybrid-control study, where the hybrid-control arm was constructed by integrating data from the IMblaze370 phase 3 trial (NCT02788279). To estimate treatment efficacy, Cox and logistic regression models were used in a frequentist framework with standardized mortality ratio weighting or in a Bayesian framework with commensurate priors. The primary endpoint is objective response rate, while disease control rate, progression-free survival, and overall survival were the outcomes assessed in the hybrid-control study. Results The experimental arm showed no efficacy signal, yet a well-tolerated safety profile in the MORPHEUS-CRC trial. Treatment effects estimated in hybrid control design were comparable to those in the MORPHEUS-CRC trial using either frequentist or Bayesian models. Conclusions Hybrid control provides comparable treatment-effect estimates with generally improved precision, and thus can be of value to inform early-phase clinical development in metastatic colorectal cancer.

show abstract

“…The treatment effect is then evaluated through the Bayesian approach. As shown in (9), the calculation of the treatment effect follows a usual linear model as if the supplemental data on the control is in the trial itself. However, the likelihood of these external controls is weighted as indicated in (11).…”

Section: Roadmap To Application and Discussionmentioning

confidence: 99%

“…Similar explorations were conducted in Wang et al, 6 where propensity scores were applied to stratify the supplemental data and to incorporate stratum‐specific power priors. Propensity scores were also used for matching supplemental control subjects with current experimental subjects in Lin et al 7 and Lin et al 8 Liu et al 9 integrated propensity scores with meta‐analytic‐predictive prior to account for degrees of between‐trial heterogeneity. A literature review of utilizing propensity score under Bayesian framework can be found in Lin and Lin 10 .…”

Section: Introductionmentioning

confidence: 99%

Ensuring exchangeability in data‐based priors for a Bayesian analysis of clinical trials

Lin

Gamalo

Tiwari

2021

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type‐I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid “cherry‐picking” advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity‐score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect.

show abstract

Propensity‐score‐based meta‐analytic predictive prior for incorporating real‐world and historical data

Cited by 28 publications

References 31 publications

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Hybrid-control arm construction using historical trial data for an early-phase, randomized controlled trial in metastatic colorectal cancer

Ensuring exchangeability in data‐based priors for a Bayesian analysis of clinical trials

Contact Info

Product

Resources

About