Measurement error, time lag, unmeasured confounding: considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials

Measurement error, time lag, unmeasured confounding: considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work. This would allow evaluation of theoretical treatment models and refinement and improvement of treatments. Treatment processes can be studied using mediation analysis. Randomised treatment makes...

Full description

Bibliographic Details
Main Authors:	Goldsmith, K, Chalder, T, White, P, Sharpe, M
Format:	Journal article
Published:	SAGE Publications 2016

Similar Items

Improved sensitivity bounds for mediation under unmeasured mediator–outcome confounding
by: Sjölander Arvid, et al.
Published: (2024-07-01)

Adjustment for unmeasured confounding through informative priors for the confounder-outcome relation
by: Rolf H. H. Groenwold, et al.
Published: (2018-12-01)

Examining the role of unmeasured confounding in mediation analysis with genetic and genomic applications
by: Sharon M. Lutz, et al.
Published: (2017-07-01)

unmconf : an R package for Bayesian regression with unmeasured confounders
by: Ryan Hebdon, et al.
Published: (2024-09-01)

Simple yet sharp sensitivity analysis for unmeasured confounding
by: Peña Jose M.
Published: (2022-03-01)

Instrumental variables to test for unmeasured confounding: a precautionary note
by: Ali, MS, et al.
Published: (2017)

Quantifying the impact of unmeasured confounding in observational studies with the E value
by: Irene Petersen, et al.
Published: (2023-07-01)

Quantitative bias analysis in practice: review of software for regression with unmeasured confounding
by: Emily Kawabata, et al.
Published: (2023-05-01)

Tutorial: The practical application of longitudinal structural equation mediation models in clinical trials
by: Goldsmith, K, et al.
Published: (2017)

Estimation of marginal structural models under irregular visits and unmeasured confounder: calibrated inverse probability weights
by: Sumeet Kalia, et al.
Published: (2023-01-01)

Prenatal depression and infant health: The importance of inadequately measured, unmeasured, and unknown confounds
by: Chittaranjan Andrade
Published: (2018-01-01)

A note on a sensitivity analysis for unmeasured confounding, and the related E-value
by: Sjölander Arvid
Published: (2020-12-01)

Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data
by: Jason R. Guertin MSc, PhD, et al.
Published: (2017-03-01)

Methodological Tutorial Series for Epidemiological Studies: Confounder Selection and Sensitivity Analyses to Unmeasured Confounding From Epidemiological and Statistical Perspectives
by: Kosuke Inoue, et al.
Published: (2025-01-01)

Instrumental variable-based high-dimensional mediation analysis with unmeasured confounders for survival data in the observational epigenetic study
by: Fangyao Chen, et al.
Published: (2023-02-01)

How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies
by: Michael Andrew Barrowman, et al.
Published: (2019-07-01)

Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score
by: Wyss Richard, et al.
Published: (2014-09-01)

Extended sensitivity analysis for heterogeneous unmeasured confounding with an application to sibling studies of returns to education
by: Fogarty, Colin B, et al.
Published: (2021)

Comments on “Prenatal depression and infant health: The importance of inadequately measured, unmeasured and unknown confounds”
by: Tarun Verma
Published: (2018-01-01)

Socioeconomic status and survival after stroke – using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding
by: Anita Lindmark, et al.
Published: (2020-04-01)

The impact of unmeasured confounding on causal inference in observational studies: A plasmode simulation study of targeted maximum likelihood estimation
by: Lateef Amusa, et al.
Published: (2022-04-01)

How do treatments for chronic fatigue syndrome work? Exploration of instrumental variable methods for mediation analysis in PACE – a randomised controlled trial of adaptive pacing therapy, cognitive behaviour therapy, graded exercise therapy, and specialist medical care
by: Goldsmith, K, et al.
Published: (2011)

Negative control outcomes to control for unmeasured confounding in device safety research: a clinical example
by: Prieto Alhambra, D, et al.
Published: (2019)

How do treatments for chronic fatigue syndrome work? Exploration of instrumental variable methods for mediation analysis in PACE – a randomised controlled trial of adaptive pacing therapy, cognitive behaviour therapy, graded exercise therapy, and specialist medical care
by: Goldsmith Kimberly, et al.
Published: (2011-12-01)

Author's response to comments on “Prenatal depression and infant health: The importance of inadequately measured, unmeasured and unknown confounds”
by: Chittaranjan Andrade
Published: (2018-01-01)

The ACCE method: an approach for obtaining quantitative or qualitative estimates of residual confounding that includes unmeasured confounding [v2; ref status: indexed, http://f1000r.es/54j]
by: Eric G. Smith
Published: (2015-04-01)

Comparing g-computation, propensity score-based weighting, and targeted maximum likelihood estimation for analyzing externally controlled trials with both measured and unmeasured confounders: a simulation study
by: Jinma Ren, et al.
Published: (2023-01-01)

Multiply efficient trials: combining multiple trial arms and critical secondary questions increases trial efficiency
by: Goldsmith, K, et al.
Published: (2017)

PACE trial authors' reply to letter by Kindlon.
by: White, P, et al.
Published: (2013)

Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation
by: Nathaniel Stockham, et al.
Published: (2022-07-01)

Delayed Impulsive Control for Lag Synchronization of Neural Networks with Time-Varying Delays and Partial Unmeasured States
by: Jiarui Ye, et al.
Published: (2022-01-01)

Association of maternal personality traits with medication use during pregnancy to appraise unmeasured confounding in long-term pharmacoepidemiological safety studies
by: Angela Lupattelli, et al.
Published: (2023-05-01)

Investigation of Unmeasured Parameters Estimation for Distributed Control Systems
by: Hao Wang, et al.
Published: (2020-01-01)

Recovery from chronic fatigue syndrome after treatments given in the PACE trial.
by: White, P, et al.
Published: (2013)

Comparing the performance of two-stage residual inclusion methods when using physician’s prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility
by: Lisong Zhang, et al.
Published: (2024-04-01)

Letter to the editor: response to correspondence concerning 'recovery from chronic fatigue syndrome after treatments in the PACE trial'.
by: White, P, et al.
Published: (2013)

Adaptive fault estimation for T-S fuzzy systems with unmeasurable premise variables
by: Shaokun Liu, et al.
Published: (2018-03-01)

Unmeasured Confounders in Observational Studies Comparing Bilateral Versus Single Internal Thoracic Artery for Coronary Artery Bypass Grafting: A Meta‐Analysis
by: Mario Gaudino, et al.
Published: (2018-01-01)

Sensitivity analysis for unmeasured confounders using an electronic spreadsheet Análise de sensibilidade para um confundidor não mensurado utilizando planilha eletrônica
by: Maria Deolinda Borges Cabral, et al.
Published: (2007-06-01)

The PACE trial: Results of a large trial of non-pharmacological treatments
by: White, P, et al.
Published: (2011)