Clinical Trial Adaptation by Matching Evidence in Complementary Patient Sub-groups of Auxiliary Blinding Questionnaire Responses

Abstract

Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. Such adjustment may take on various forms, including the change in the dose of administered medicines, the frequency of administering an intervention, the number of trial participants, or the duration of the trial, to name just some possibilities. The main goal is to make the process of introducing new medical interventions to patients more efficient, either by reducing the cost or the time associated with evaluating their safety and efficacy. The principal challenge, which is an outstanding research problem, is to be found in the question of how adaptation should be performed so as to minimize the chance of distorting the outcome of the trial. In this paper we propose a novel method for achieving this. Unlike most of the previously published work, our approach focuses on trial adaptation by sample size adjustment i.e. by reducing the number of trial participants in a statistically informed manner. We adopt a stratification framework recently proposed for the analysis of trial outcomes in the presence of imperfect blinding and based on the administration of a generic auxiliary questionnaire that allows the participants to express their belief concerning the assigned intervention (treatment or control). We show that this data, together with the primary measured variables, can be used to make the probabilistically optimal choice of the particular sub-group a participant should be removed from if trial size reduction is desired. Extensive experiments on a series of simulated trials are used to illustrate the effectiveness of our method.

Strategies for informed sample size reduction in adaptive controlled clinical trials

“…Like most previous work in this area, we adopt the evaluation protocol standard in the domain of adaptive trials research and obtain data using a simulated experiment [14,[18][19][20][21][22]. The first experiment investigates the effectiveness of the proposed methods on the proximal aims of information loss minimization.…”

Random guess and wishful thinking are the best blinding scenarios

“…Particularly, we focused on the allocation ratio of 1:1 as the interpretation of blinding success under other ratios might not be straightforward, say, is the null value 50% or 33% under 1:2? More advanced designs and models have been proposed and may be considered in future [16], [22], [23], [24]. Second, statistically speaking, this problem is a special case with measurement error or misclassification in outcomes [25].…”

Towards objective and reproducible study of patient-doctor interaction: Automatic text analysis based VR-CoDES annotation of consultation transcripts

“…In particular, we address the problem of labelling a patient's utterance (verbal communication, uninterrupted by the HCP, which comprises a single or multiple sentences) as containing (i) an explicit concern, (ii) an emotional cue, or (iii) neither. Thus, in addition to addressing the specific issues of patient-HCP communication, the present work contributes to the overall effort in the use of modern computer science in assisting health care provision [4], [7], [24], [25].…”