cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4445777.v21
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v22
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v23
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v24
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v25
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v26
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v27
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v28
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v29
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v30
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v31
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v32
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v33
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v34
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4445777.v35
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision making process by explicitly identifying the necessary, albeit elaborate, information and assumptions.
DOI: 10.6084/m9.figshare.4445777.v36
title: A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

description: We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.