The Experts below are selected from a list of 70533 Experts worldwide ranked by ideXlab platform

Eric A Dubois - One of the best experts on this subject based on the ideXlab platform.

Murat Arslan - One of the best experts on this subject based on the ideXlab platform.

Eric Boersma - One of the best experts on this subject based on the ideXlab platform.

Admir Dedic - One of the best experts on this subject based on the ideXlab platform.

Graham Dunn - One of the best experts on this subject based on the ideXlab platform.

  • beyond total treatment effects in randomised controlled trials Baseline Measurement of intermediate outcomes needed to reduce confounding in mediation investigations
    Clinical Trials, 2018
    Co-Authors: Sabine Landau, Richard Emsley, Graham Dunn
    Abstract:

    Background:Random allocation avoids confounding bias when estimating the average treatment effect. For continuous outcomes measured at post-treatment as well as prior to randomisation (Baseline), analyses based on (A) post-treatment outcome alone, (B) change scores over the treatment phase or (C) conditioning on Baseline values (analysis of covariance) provide unbiased estimators of the average treatment effect. The decision to include Baseline values of the clinical outcome in the analysis is based on precision arguments, with analysis of covariance known to be most precise. Investigators increasingly carry out explanatory analyses to decompose total treatment effects into components that are mediated by an intermediate continuous outcome and a non-mediated part. Traditional mediation analysis might be performed based on (A) post-treatment values of the intermediate and clinical outcomes alone, (B) respective change scores or (C) conditioning on Baseline measures of both intermediate and clinical outcome...

  • integrating biomarker information within trials to evaluate treatment mechanisms and efficacy for personalised medicine
    Clinical Trials, 2013
    Co-Authors: Graham Dunn, Richard Emsley, Hanhua Liu, Sabine Landau
    Abstract:

    Background The development of personalised (stratified) medicine is intrinsically dependent on an understanding of treatment-effect mechanisms (effects on therapeutic targets that mediate the effect of the treatment on clinical outcomes). There is a need for clinical trial data for the joint evaluation of treatment efficacy, the utility of predictive markers as indicators of treatment efficacy, and the mediational mechanisms proposed as the explanation of these effects. Purpose (1) To review the problem of confounding (common causes) for the drawing of valid inferences concerning treatment-effect mechanisms, even when the data have been generated using a randomised controlled trial, and (2) to suggest and illustrate solutions to this problem of confounding. Results We illustrate the potential of the predictive biomarker stratified design, together with Baseline Measurement of all known prognostic markers, to enable us to evaluate both the utility of the predictive biomarker in such a stratification and, perhaps more importantly, to estimate how much of the treatment’s effect is actually explained by changes in the putative mediator. The analysis strategy involves the use of instrumental variable (IV) regression, using the treatment by predictive biomarker interaction as an IV – a refined, much more powerful, and (in the present context) subtle use of Mendelian randomisation. Conclusion Personalised (stratified) medicine and treatment-effect mechanisms evaluation are inextricably linked. Stratification without corresponding mechanisms evaluation lacks credibility. In the presence of mediator-outcome confounding, mechanisms evaluation is dependent on stratification for its validity. Both stratification and treatment-effect mediation can be evaluated using a biomarker stratified trial design together with detailed Baseline Measurement of all known prognostic biomarkers and other prognostic covariates. Direct and indirect (mediated) effects should be estimated through the use of IV methods (the IV being the predictive marker by treatment interaction) together with adjustments for all known prognostic markers (confounders) – the latter adjustments contributing to increased precision (as in a conventional analysis of treatment effects) rather than bias reduction. Clinical Trials 2013; 10: 709–719. http://ctj.sagepub.com