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A Bayesian prediction model between a biomarker and the clinical endpoint for dichotomous variables.

Jiang Z, Song Y, Shou Q, Xia J, Wang W - Trials (2014)

Bottom Line: PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome.It could be applied in drug development.But the practical problems in applications have to be studied in further research.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Statistics, School of Preventive Medicine, Fourth Military Medical University, No, 169 Changle West Road, Xi'an, Shaanxi, China. jielaixia@yahoo.com.

ABSTRACT

Background: Early biomarkers are helpful for predicting clinical endpoints and for evaluating efficacy in clinical trials even if the biomarker cannot replace clinical outcome as a surrogate. The building and evaluation of an association model between biomarkers and clinical outcomes are two equally important concerns regarding the prediction of clinical outcome. This paper is to address both issues in a Bayesian framework.

Methods: A Bayesian meta-analytic approach is proposed to build a prediction model between the biomarker and clinical endpoint for dichotomous variables. Compared with other Bayesian methods, the proposed model only requires trial-level summary data of historical trials in model building. By using extensive simulations, we evaluate the link function and the application condition of the proposed Bayesian model under scenario (i) equal positive predictive value (PPV) and negative predictive value (NPV) and (ii) higher NPV and lower PPV. In the simulations, the patient-level data is generated to evaluate the meta-analytic model. PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome. The minimum number of historical trials to be included in building the model is also considered.

Results: It is seen from the simulations that the logit link function performs better than the odds and cloglog functions under both scenarios. PPV/NPV ≥0.5 for equal PPV and NPV, and PPV + NPV ≥1 for higher NPV and lower PPV are proposed in order to predict clinical outcome accurately and precisely when the proposed model is considered. Twenty historical trials are required to be included in model building when PPV and NPV are equal. For unequal PPV and NPV, the minimum number of historical trials for model building is proposed to be five. A hypothetical example shows an application of the proposed model in global drug development.

Conclusions: The proposed Bayesian model is able to predict well the clinical endpoint from the observed biomarker data for dichotomous variables as long as the conditions are satisfied. It could be applied in drug development. But the practical problems in applications have to be studied in further research.

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The diagram of Bayesian model building and prediction.
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Related In: Results  -  Collection

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Fig1: The diagram of Bayesian model building and prediction.

Mentions: is employed to evaluate the efficacy of the intervention. An equal association between the biomarker and the clinical endpoint across the new trial and historical trials is assumed here. It means that the biomarker in the new trial captures the same treatment effect as the one in the historical trials. Based on the Bayesian model built from N historical trials, the MCMC estimation of predictive distribution for φXj is obtained when φBj is given. Correspondingly, the predictive distribution of RRj is derived from formula (7). We take the median of the predictive distribution as the point estimate of RRj prediction and construct the 95% credible interval (CI) with 2.5% and 97.5% percentiles. The flow chart of Bayesian model building and prediction is depicted in Figure 1.Figure 1


A Bayesian prediction model between a biomarker and the clinical endpoint for dichotomous variables.

Jiang Z, Song Y, Shou Q, Xia J, Wang W - Trials (2014)

The diagram of Bayesian model building and prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4307375&req=5

Fig1: The diagram of Bayesian model building and prediction.
Mentions: is employed to evaluate the efficacy of the intervention. An equal association between the biomarker and the clinical endpoint across the new trial and historical trials is assumed here. It means that the biomarker in the new trial captures the same treatment effect as the one in the historical trials. Based on the Bayesian model built from N historical trials, the MCMC estimation of predictive distribution for φXj is obtained when φBj is given. Correspondingly, the predictive distribution of RRj is derived from formula (7). We take the median of the predictive distribution as the point estimate of RRj prediction and construct the 95% credible interval (CI) with 2.5% and 97.5% percentiles. The flow chart of Bayesian model building and prediction is depicted in Figure 1.Figure 1

Bottom Line: PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome.It could be applied in drug development.But the practical problems in applications have to be studied in further research.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Statistics, School of Preventive Medicine, Fourth Military Medical University, No, 169 Changle West Road, Xi'an, Shaanxi, China. jielaixia@yahoo.com.

ABSTRACT

Background: Early biomarkers are helpful for predicting clinical endpoints and for evaluating efficacy in clinical trials even if the biomarker cannot replace clinical outcome as a surrogate. The building and evaluation of an association model between biomarkers and clinical outcomes are two equally important concerns regarding the prediction of clinical outcome. This paper is to address both issues in a Bayesian framework.

Methods: A Bayesian meta-analytic approach is proposed to build a prediction model between the biomarker and clinical endpoint for dichotomous variables. Compared with other Bayesian methods, the proposed model only requires trial-level summary data of historical trials in model building. By using extensive simulations, we evaluate the link function and the application condition of the proposed Bayesian model under scenario (i) equal positive predictive value (PPV) and negative predictive value (NPV) and (ii) higher NPV and lower PPV. In the simulations, the patient-level data is generated to evaluate the meta-analytic model. PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome. The minimum number of historical trials to be included in building the model is also considered.

Results: It is seen from the simulations that the logit link function performs better than the odds and cloglog functions under both scenarios. PPV/NPV ≥0.5 for equal PPV and NPV, and PPV + NPV ≥1 for higher NPV and lower PPV are proposed in order to predict clinical outcome accurately and precisely when the proposed model is considered. Twenty historical trials are required to be included in model building when PPV and NPV are equal. For unequal PPV and NPV, the minimum number of historical trials for model building is proposed to be five. A hypothetical example shows an application of the proposed model in global drug development.

Conclusions: The proposed Bayesian model is able to predict well the clinical endpoint from the observed biomarker data for dichotomous variables as long as the conditions are satisfied. It could be applied in drug development. But the practical problems in applications have to be studied in further research.

Show MeSH