6,761 research outputs found
Comparison of Bayesian and frequentist group-sequential clinical trial designs
Background: There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials. This
includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as
in frequentist group-sequential designs.
Methods: This paper presents a practical comparison of Bayesian and frequentist group-sequential tests. Focussing
on the setting in which data can be summarised by normally distributed test statistics, we evaluate and compare
boundary values and operating characteristics.
Results: Although Bayesian and frequentist group-sequential approaches are based on fundamentally different
paradigms, in a single arm trial or two-arm comparative trial with a prior distribution specified for the treatment
difference, Bayesian and frequentist group-sequential tests can have identical stopping rules if particular critical values
with which the posterior probability is compared or particular spending function values are chosen. If the Bayesian
critical values at different looks are restricted to be equal, O’Brien and Fleming’s design corresponds to a Bayesian
design with an exceptionally informative negative prior, Pocock’s design to a Bayesian design with a non-informative
prior and frequentist designs with a linear alpha spending function are very similar to Bayesian designs with slightly
informative priors.
This contrasts with the setting of a comparative trial with independent prior distributions specified for treatment
effects in different groups. In this case Bayesian and frequentist group-sequential tests cannot have the same
stopping rule as the Bayesian stopping rule depends on the observed means in the two groups and not just on their
difference. In this setting the Bayesian test can only be guaranteed to control the type I error for a specified range of
values of the control group treatment effect.
Conclusions: Comparison of frequentist and Bayesian designs can encourage careful thought about design
parameters and help to ensure appropriate design choices are made
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On optimal designs for clinical trials: An updated review
Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development
Generating health technology assessment evidence for rare diseases
Objectives: Rare diseases are often heterogeneous in their progression and response to treatment, with only a small population for study. This provides challenges for evidence generation to support HTA, so novel research methods are required.
Methods: Discussion with an expert panel was augmented with references and case studies to explore robust approaches for HTA evidence generation for rare disease treatments.
Results: Traditional RCTs can be modified using sequential, three-stage or adaptive designs to gain more power from a small patient population or to focus trial design. However, such designs need to maintain important design aspects such as randomization and blinding and be analyzed to take account of the multiple analyses performed. N-of-1 trials use within-patient randomization to test repeat periods of treatment and control until a response is clear. Such trials could be particularly valuable for rare diseases and when prospectively planned across several patients and analyzed using Bayesian techniques, a population effect can be estimated that might be of value to HTA. When the optimal outcome is unclear in a rare disease, disease specific patient reported outcomes can elucidate impacts on patients’ functioning and wellbeing. Likewise, qualitative research can be used to elicit patients’ perspectives, with just a small number of patients.
Conclusions: International consensus is needed on ways to improve evidence collection and assessment of technologies for rare diseases, which recognize the value of novel study designs and analyses in a setting where the outcomes and effects of importance are yet to be agreed.</p
An efficient multiple imputation algorithm for control-based and delta-adjusted pattern mixture models using SAS
In clinical trials, mixed effects models for repeated measures (MMRM) and
pattern mixture models (PMM) are often used to analyze longitudinal continuous
outcomes. We describe a simple missing data imputation algorithm for the MMRM
that can be easily implemented in standard statistical software packages such
as SAS PROC MI. We explore the relationship of the missing data distribution in
the control-based and delta-adjusted PMMs with that in the MMRM, and suggest an
efficient imputation algorithm for these PMMs. The unobserved values in PMMs
can be imputed by subtracting the mean difference in the posterior predictive
distributions of missing data from the imputed values in MMRM. We also suggest
a modification of the copy reference imputation procedure to avoid the
possibility that after dropout, subjects from the active treatment arm will
have better mean response trajectory than subjects who stay on the active
treatment. The proposed methods are illustrated by the analysis of an
antidepressant trial.Comment: 27 page
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