Explore the vast range of titles available.

Assessment of study success using conditional power (CP), the predictive power of success (PPoS) and probability of success (PoS) is becoming increasingly common for resource optimization and adaption of trials in clinical investigation. Determination of these measures is often a non-trivial mathematical task. Further, the terminologies used across the literature are not consistent, and there is no consolidated presentation on this. We have made a structured presentation on these measures for both trial success and clinical success: first, we have summarized the expressions of CP, PPoS and PoS in a general setting and subsequently, expressions for these measures are obtained for continuous, binary, and time-to-event endpoints in single-arm and two-arm trial settings. Many of these expressions are previously published; however, some of the expressions are very new including the expressions for testing median of time-to-event endpoint in a single-arm trial. We have also shown that 1/(no.ofevents) consistently underestimates the variance of log(median) and alternative expression for variance was derived. Examples are given along with the comparison of CP and PPoS. Expressions presented in this paper are implemented in LongCART package in R and in R shiny app https://ppos.shinyapps.io/public/.

The hybrid frequentist-Bayesian approach to sample size determination is based on the expectation of the power function of a test with respect to a design prior for the unknown parameter value. In clinical trials this quantity is often called probability of success (PoS). Determination of the limiting value of PoS as the number of observations tends to infinity, that is crucial for well defined sample size criteria, has been considered in previous articles. Here, we focus on the asymptotic behavior of the whole distribution of the power function induced by the design prior. Under mild conditions, we provide asymptotic results for the three most common classes of hypotheses on a scalar parameter. The impact of the design parameters choice on the distribution of the power function and on its limit is discussed.

Background: The Portfolio-To-Impact version 2 (P2I v.2) financial forecasting tool estimates funding requirements for development of portfolios of candidate health products (drugs, biologics, vaccines or diagnostics). The assumptions and archetypes relating to diagnostics in P2I v.2 are based on limited data and may not accurately describe research and development costs, timelines and probability of success. This study aimed to revise the P2I v.2 tool by modifying the diagnostic assumptions to improve accuracy of predictions for diagnostic portfolios. Methods: Data from expert interviews and historical information on development of 26 existing diagnostics were used to determine approximate research and development costs, timelines and probability of success for development of diagnostics, and to revise diagnostic archetypes and development phases. To compare the revised tool with P2I v.2, data on 27 candidates from the Foundation for Innovative New Diagnostics (FIND) tuberculosis and pandemic preparedness portfolios were input into both versions. Results: The number of diagnostic archetypes increased from two in P2I v.2 to three in the revised tool. Total estimated costs to move the 27 candidates along the pipeline to launch were US$641.62 million with P2I v.2 and US$274.00 million with the revised model. The number of expected launches was 21.65 over five years with P2I v.2 and 11.48 over eight years with the revised model. Development timelines were extended and probability of success was lower with the revised model compared with P2I v.2. Conclusions: Outputs from the revised tool were in line with expert experience, suggesting that the proposed revisions improve the accuracy of the tool for estimating research and development costs, timelines and probability of success relating to diagnostic portfolios. Additional improvements to the tool could include further refinement of archetypes, incorporation of a measure of potential public health impact, and addition of a commercialization phase for diagnostics.

Background To perform virtual re-executions of a breast cancer clinical trial with a time-to-event outcome to demonstrate what would have happened if the trial had used various Bayesian adaptive designs instead. Methods We aimed to retrospectively “re-execute” a randomised controlled trial that compared two chemotherapy regimens for women with metastatic breast cancer (ANZ 9311) using Bayesian adaptive designs. We used computer simulations to estimate the power and sample sizes of a large number of different candidate designs and shortlisted designs with the either highest power or the lowest average sample size. Using the real-world data, we explored what would have happened had ANZ 9311 been conducted using these shortlisted designs. Results We shortlisted ten adaptive designs that had higher power, lower average sample size, and a lower false positive rate, compared to the original trial design. Adaptive designs that prioritised small sample size reduced the average sample size by up to 37% when there was no clinical effect and by up to 17% at the target clinical effect. Adaptive designs that prioritised high power increased power by up to 5.9 percentage points without a corresponding increase in type I error. The performance of the adaptive designs when applied to the real-world ANZ 9311 data was consistent with the simulations. Conclusion The shortlisted Bayesian adaptive designs improved power or lowered the average sample size substantially. When designing new oncology trials, researchers should consider whether a Bayesian adaptive design may be beneficial.

Universities aim to enhance students' acquisition of useful knowledge and skills, crucial for their future success. This correlational study investigates the relationships between family socioeconomic status, perceived probability of success, self-efficacy, and academic performance among Indonesian students in Islamic Higher Education (IHE) and General Higher Education (GHE). It further explores the mediating role of resilience in these relationships. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA), data from 424 students (311 from IHE and 113 from GHE) were analyzed. Findings reveal that parental education and family economic status do not significantly predict resilience or academic performance in either group. However, self-efficacy significantly predicts resilience in both groups. Additionally, perceived probability of success positively predicts academic performance in both educational settings. Resilience significantly mediates and enhances academic performance, particularly among GHE students. The study emphasizes the importance of psychological factors like self-efficacy and resilience in supporting students' academic outcomes. Practical implications include developing specific programs or workshops to enhance university students' self-efficacy and resilience, such as stress management training, goal-setting sessions, and peer mentoring activities. Future studies should incorporate broader psychological and social variables across diverse student populations.

Conservation funds are grossly inadequate to address the plight of threatened species. Government and conservation organizations faced with the task of conserving threatened species desperately need simple strategies for allocating limited resources. The academic literature dedicated to systematic priority setting usually recommends ranking species on several criteria, including level of endangerment and metrics of species value such as evolutionary distinctiveness, ecological importance, and social significance. These approaches ignore 2 crucial factors: the cost of management and the likelihood that the management will succeed. These oversights will result in misallocation of scarce conservation resources and possibly unnecessary losses. We devised a project prioritization protocol (PPP) to optimize resource allocation among New Zealand's threatened-species projects, where costs, benefits (including species values), and the likelihood of management success were considered simultaneously. We compared the number of species managed and the expected benefits gained with 5 prioritization criteria: PPP with weightings based on species value; PPP with species weighted equally; management costs; species value; and threat status. We found that the rational use of cost and success information substantially increased the number of species managed, and prioritizing management projects according to species value or threat status in isolation was inefficient and resulted in fewer species managed. In addition, we found a clear trade-off between funding management of a greater number of the most cost-efficient and least risky projects and funding fewer projects to manage the species of higher value. Specifically, 11 of 32 species projects could be funded if projects were weighted by species value compared with 16 projects if projects were not weighted. This highlights the value of a transparent decision-making process, which enables a careful consideration of trade-offs. The use of PPP can substantially improve conservation outcomes for threatened species by increasing efficiency and ensuring transparency of management decisions.

Background Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%. Methods We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the “right amount of adjustment”, the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies. Results In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the “naïve” program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design. Conclusion Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

In oncology anti—PD1 / PDL1 therapy development for solid tumors, objective response rate (ORR) is commonly used clinical endpoint for early phase study decision making, while progression free survival (PFS) and overall survival (OS) are widely used for late phase study decision making. Developing predictive models to late phase outcomes such as median PFS (mPFS) and median OS (mOS) based on early phase clinical outcome ORR could inform late phase study design optimization and probability of success (POS) evaluation. In existing literature, there are ORR / mPFS / mOS association and surrogacy investigations with limited number of included clinical trials. In this paper, without establishing surrogacy, we attempt to predict late phase survival (mPFS and mOS) based on early efficacy ORR and optimize late phase trial design for anti—PD1 / PDL1 therapy development. In order to include adequate number of eligible clinical trials, we built a comprehensive quantitative clinical trial landscape database (QLD) by combining information from different sources such as clinicaltrial.gov, publications, company press releases for relevant indications and therapies. We developed a generalizable algorithm to systematically extract structured data for scientific accuracy and completeness. Finally, more than 150 late phase clinical trials were identified for ORR / mPFS (ORR / mOS) predictive model development while existing literature included at most 50 trials. A tree-based machine learning regression model has been derived to account for ORR / mPFS (ORR / mOS) relationship heterogeneity across tumor type, stage, line of therapy, treatment class and borrow strength simultaneously when homogeneity persists. The proposed method ensures that the predictive model is robust and have explicit structure for clinical interpretation. Through cross validation, the average predictive mean square error of the proposed model is competitive to random forest and extreme gradient boosting methods and outperforms commonly used additive or interaction linear regression models. An example application of the proposed ORR / mPFS (ORR / mOS) predictive model on late phase trial POS evaluation for anti—PD1 / PDL1 combination therapy was illustrated.

Decision-makers usually have an aspiration level, a target, or a benchmark they aim to achieve. This behavior can be rationalized within the expected utility framework, which incorporates the probability of success (achieving the aspiration level) as an important aspect of decision-making. Motivated by these theories, this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month. This study uses portfolio-level and firm-level analyses, revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns, even after controlling for common risk factors and various characteristics. Additional analyses support the behavioral theory of the firm, which posits that firms act to achieve short-term aspiration levels.

Under what conditions do individuals support armed humanitarian intervention (AHI) in situations where mass atrocities are ongoing? This article tests several hypotheses about support for AHIs to isolate and interact two potential drivers: international law and the probability of success. It leverages an original, pre-registered experiment from a (quota) representative sample of over 1500 Dutch citizens. Consistent with our hypotheses, we find that support for AHI increases when an action is authorized by the UN Security Council (UNSC) and has a high (80%) chance of success. But the Dutch remain supportive of AHI in situations of mass atrocities even when AHI has a low chance of success (20%). Importantly, we find that the chance of success does not affect the support for AHIs as much as the international law does. This suggests that in similar situations legal and procedural reasons may influence public opinion more than a logic of consequences.