Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients

Kondo, Yumi; Zhao, Yinshan; Petkau, John

doi:10.1002/sim.7230

Cited by 3 publications

(2 citation statements)

References 37 publications

(67 reference statements)

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“…Some researchers propose modeling placebo response using a single continuous distribution while modeling treatment response as a mixture distribution. 62,63 This approach is based on the assumption that the distribution of potential outcomes for participants assigned to treatment who will not respond to the active treatment is equivalent to the distribution of potential outcomes for participants assigned to placebo. Applying these model assumptions in studies where large responses to placebo are observed may lead to complications in distinguishing between participants who respond to treatment and those who did not respond to treatment.…”

Section: Placebo Response As a Continuous Characteristicmentioning

confidence: 99%

Methods for assessing and controlling placebo effects

Kessels

Mozer

Bloemers

2017

Stat Methods Med Res

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The placebo serves as an indispensable control in many randomized trials. When analyzing the benefit of a new treatment, researchers are often confronted with large placebo effects that diminish the treatment effect. Various alternative methods have been proposed for analyzing placebo and treatment effects in studies where large placebo effects are expected or have already occurred. This paper presents an overview of methodological work that has been proposed for assessing and/or controlling for placebo effects in randomized trials. Throughout this paper, two main approaches are discussed. The first approach considers designs that represent alternatives to the classical placebo-controlled randomized trial design. Separately, the second approach considers adopting new methods for the statistical analysis of placebo and treatment effects to be implemented after the data have been collected using a classical randomized trial design.

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Section: Placebo Response As a Continuous Characteristicmentioning

confidence: 99%

Methods for assessing and controlling placebo effects

Kessels

Mozer

Bloemers

2017

Stat Methods Med Res

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“…[4][5][6][7] In epidemiology, increased interest in placebo partly arises from concerns that large placebo effects may mask true clinical effects and bias results. 2 Such concerns have led to a wave of research, alternative study designs [8][9][10][11][12][13] and statistical methods, [14][15][16][17][18][19][20][21] focused on assessing and controlling placebo effects.…”

Section: Introductionmentioning

confidence: 99%

When does the placebo effect have an impact on network meta-analysis results?

Nikolakopoulou

Chaimani

Furukawa

et al. 2023

BMJ EBM

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The placebo effect is the ‘effect of the simulation of treatment that occurs due to a participant’s belief or expectation that a treatment is effective’. Although the effect might be of little importance for some conditions, it can have a great role in others, mostly when the evaluated symptoms are subjective. Several characteristics that include informed consent, number of arms in a study, the occurrence of adverse events and quality of blinding may influence response to placebo and possibly bias the results of randomised controlled trials. Such a bias is inherited in systematic reviews of evidence and their quantitative components, pairwise meta-analysis (when two treatments are compared) and network meta-analysis (when more than two treatments are compared). In this paper, we aim to provide red flags as to when a placebo effect is likely to bias pairwise and network meta-analysis treatment effects. The classic paradigm has been that placebo-controlled randomised trials are focused on estimating the treatment effect. However, the magnitude of placebo effect itself may also in some instances be of interest and has also lately received attention. We use component network meta-analysis to estimate placebo effects. We apply these methods to a published network meta-analysis, examining the relative effectiveness of four psychotherapies and four control treatments for depression in 123 studies.

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A bivariate longitudinal cluster model with application to the {C}ognitive {R}eflection {T}est

Berkowitz¹,

Altman²

2022

TQMP

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The Cognitive Reflection Test (CRT) is a test designed to assess subjects' ability to override intuitively appealing but incorrect responses. Psychologists are concerned with whether subjects improve their scores on the test with repeated exposure, in which case, the test's predictive validity may be threatened. In this paper, we take a novel approach to modelling data recorded on subjects who took the CRT multiple times. We develop bivariate, longitudinal models to describe the responses, CRT score and time taken to complete the CRT. These responses serve as a proxy for the underlying latent variables "numeracy" and "reflectiveness", respectively-two components of "rationality". Our models allow for subpopulations of individuals whose responses exhibit similar patterns. We assess the reasonableness of our models via new visualizations of the data. We estimate their parameters by modifying the method of adaptive Gaussian quadrature. We then use our fitted models to address a range of subject-specific questions in a formal way. We find evidence of at least three subpopulations, which we interpret as representing individuals with differing combinations of numeracy and reflectiveness, and determine that, in some subpopulations, test exposure has a greater estimated effect on test scores than previously reported.

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Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients

Cited by 3 publications

References 37 publications

Methods for assessing and controlling placebo effects

Methods for assessing and controlling placebo effects

When does the placebo effect have an impact on network meta-analysis results?

A bivariate longitudinal cluster model with application to the {C}ognitive {R}eflection {T}est

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