biostudies-literatureUnknown17(4)Lennie JLRare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example. Early go/no-go decisions were based on projections of long-term functional outcomes from a Bayesian model-based analysis of short-term trial data informed by prior knowledge based on 6MWT natural history literature data in DMD patients. Frequentist hypothesis tests were also applied as a reference analysis method. A number of combinations of hypothetical trial designs, drug effects and cohort comparison methods were assessed. The proposed Bayesian model-based framework was superPloS onee0247286https://www.ebi.ac.uk/biostudies/studies/S-EPMC9049549biostudies-literatureBayesian modeling and simulation to inform rare disease drug development early decision-making: Application to Duchenne muscular dystrophy.PMC9049549Mondick JTGastonguay MRLennie JLfalseBayesian modeling and simulation to inform rare disease drug development early decision-making: Application to Duchenne muscular dystrophy.Rare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example. Early go/no-go decisions were based on projections of long-term functional outcomes from a Bayesian model-based analysis of short-term trial data informed by prior knowledge based on 6MWT natural history literature data in DMD patients. Frequentist hypothesis tests were also applied as a reference analysis method. A number of combinations of hypothetical trial designs, drug effects and cohort comparison methods were assessed. The proposed Bayesian model-based framework was super2022-01-01T00:00:00Z20222025-04-25T22:54:39.481Z2025-04-06T09:14:32.132ZS-EPMC90495493548263310.1371/journal.pone.0247286