32 research outputs found
On fixed and uncertain mixture prior weights
This paper focuses on the specification of the weights for the components of
mixture priors
Identifying Treatment Effects using Trimmed Means when Data are Missing Not at Random
Patients often discontinue treatment in a clinical trial because their health
condition is not improving. Consequently, the patients still in the study at
the end of the trial have better health outcomes on average than the initial
patient population would have had if every patient had completed the trial. If
we only analyze the patients who complete the trial, then this missing data
problem biases the estimator of a medication's efficacy because study outcomes
are missing not at random (MNAR). One way to overcome this problem - the
trimmed means approach for missing data - sets missing values as slightly worse
than the worst observed outcome and then trims away a fraction of the
distribution from each treatment arm before calculating differences in
treatment efficacy (Permutt 2017, Pharmaceutical statistics 16.1:20-28). In
this paper we derive sufficient and necessary conditions for when this approach
can identify the average population treatment effect in the presence of MNAR
data. Numerical studies show the trimmed means approach's ability to
effectively estimate treatment efficacy when data are MNAR and missingness is
strongly associated with an unfavorable outcome, but trimmed means fail when
data are missing at random (MAR) when the better approach would be to multiply
impute the missing values. If the reasons for discontinuation in a clinical
trial are known analysts can improve estimates with a combination of multiple
imputation (MI) and the trimmed means approach when the assumptions of each
missing data mechanism hold. When the assumptions are justifiable, using
trimmed means can help identify treatment effects notwithstanding MNAR data
Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools
Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical data. In this paper, we introduce the package RBesT (R Bayesian evidence synthesis tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by Markov chain Monte Carlo (MCMC). The MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations
Principal Stratum Strategy: Potential Role in Drug Development
A randomized trial allows estimation of the causal effect of an intervention
compared to a control in the overall population and in subpopulations defined
by baseline characteristics. Often, however, clinical questions also arise
regarding the treatment effect in subpopulations of patients, which would
experience clinical or disease related events post-randomization. Events that
occur after treatment initiation and potentially affect the interpretation or
the existence of the measurements are called {\it intercurrent events} in the
ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment,
randomization alone is no longer sufficient to meaningfully estimate the
treatment effect. Analyses comparing the subgroups of patients without the
intercurrent events for intervention and control will not estimate a causal
effect. This is well known, but post-hoc analyses of this kind are commonly
performed in drug development. An alternative approach is the principal stratum
strategy, which classifies subjects according to their potential occurrence of
an intercurrent event on both study arms. We illustrate with examples that
questions formulated through principal strata occur naturally in drug
development and argue that approaching these questions with the ICH E9(R1)
estimand framework has the potential to lead to more transparent assumptions as
well as more adequate analyses and conclusions. In addition, we provide an
overview of assumptions required for estimation of effects in principal strata.
Most of these assumptions are unverifiable and should hence be based on solid
scientific understanding. Sensitivity analyses are needed to assess robustness
of conclusions
On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis
The normal-normal hierarchical model (NNHM) constitutes a simple and widely
used framework for meta-analysis. In the common case of only few studies
contributing to the meta-analysis, standard approaches to inference tend to
perform poorly, and Bayesian meta-analysis has been suggested as a potential
solution. The Bayesian approach, however, requires the sensible specification
of prior distributions. While non-informative priors are commonly used for the
overall mean effect, the use of weakly informative priors has been suggested
for the heterogeneity parameter, in particular in the setting of (very) few
studies. To date, however, a consensus on how to generally specify a weakly
informative heterogeneity prior is lacking. Here we investigate the problem
more closely and provide some guidance on prior specification.Comment: 42 pages, 10 figures, 20 table