Sample-size calculations for trials that inform individual treatment decisions: a ‘true-choice’ approach

Abstract

The approach is a pragmatic aid to trial design in settings where patient preference drives the choice between alternative treatments.

Assessing methods to specify the target difference for a randomised controlled trial: DELTA (Difference ELicitation in TriAls) review

“…462 This process has been carried out explicitly for determining the target difference for a RCT in both a Bayesian and a conventional framework and can be extended to incorporate treatment decision-making. 26,45,47,242,243,443 Key points for using the opinion-seeking method to specify the target difference The perspective is critical -whose opinions are being sought. A target difference that takes into account other outcomes and/or consequences (e.g.…”

“…Other statistical approaches for defining the required sample size are Fisherian, Bayesian and decision-theoretic Bayesian approaches, along with a hybrid of both the Bayesian and Neyman-Pearson approaches. 24,443,444 Although the sample size calculation process is different under a Bayesian statistical framework with regards to specification of the statistical aim, there is some similarity. A range in the posterior distribution of an outcome in a Bayesian statistical framework is often used in a similar manner to the equivalence limit in an equivalence trial under the Neyman-Pearson method.…”

Evaluating policy and service interventions: framework to guide selection and interpretation of study end points

“…32 Likewise, the relation between interventions and effect size would be relevant when considering cost effective sample sizes in a bayesian model. 24 Here changes in credible limits and the centre of updated probability distributions would be the relevant considerations, but it would still be necessary to think carefully about sample size, cost effectiveness, and the distinction between interventions with diffuse and narrow effects. Bayesian methods can also be used to integrate multiple observations (including qualitative data).…”

“…However, in some cases, particularly in developing country contexts or when outcomes other than mortality are salient, the effect sizes may allow cost effective measurement. In these cases we advocate the use of bayesian value of information modelling, which has been used in health technology assessment, [22][23][24] to investigate the cost effectiveness of proposed studies and to select the sample size that offers best value for money.…”

Hypothesis testing by simulation of a medical study model using the expected net benefits criteria

“…These models do not take into account the future costs and benefits of the decisions that for example might follow study findings; instead they rely on arbitrary decision criteria. Other methods have been used with similar objectives [4][5][6][7][8], but the authors applied those assuming deterministic relationships and so only point results could be assessed, which do not take into account variability and uncertainty involved in the decisions.…”