Literature DB >> 27110045

Joint Estimation of Treatment and Placebo Effects in Clinical Trials with Longitudinal Blinding Assessments.

Wei Liu1, Zhiwei Zhang2, R Jason Schroeder2, Martin Ho2, Bo Zhang2, Cynthia Long3, Hui Zhang4, Telba Z Irony5.   

Abstract

In some therapeutic areas, treatment evaluation is frequently complicated by a possible placebo effect (i.e., the psychobiological effect of a patient's knowledge or belief of being treated). When a substantial placebo effect is likely to exist, it is important to distinguish the treatment and placebo effects in quantifying the clinical benefit of a new treatment. These causal effects can be formally defined in a joint causal model that includes treatment (e.g., new versus placebo) and treatmentality (i.e., a patient's belief or mentality about which treatment she or he has received) as separate exposures. Information about the treatmentality exposure can be obtained from blinding assessments, which are increasingly common in clinical trials where blinding success is in question. Assuming that treatmentality has a lagged effect and is measured at multiple time points, this article is concerned with joint evaluation of treatment and placebo effects in clinical trials with longitudinal follow-up, possibly with monotone missing data. We describe and discuss several methods adapted from the longitudinal causal inference literature, apply them to a weight loss study, and compare them in simulation experiments that mimic the weight loss study.

Entities:  

Keywords:  G-computation; causal inference; confounding; double robustness; inverse probability weighting; sequential regression; targeted minimum loss based estimation; treatmentality

Year:  2016        PMID: 27110045      PMCID: PMC4838817          DOI: 10.1080/01621459.2015.1130633

Source DB:  PubMed          Journal:  J Am Stat Assoc        ISSN: 0162-1459            Impact factor:   5.033


  19 in total

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5.  A causal model for joint evaluation of placebo and treatment-specific effects in clinical trials.

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6.  Causal inference in epidemiological studies with strong confounding.

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8.  Accounting for perception, placebo and unmasking effects in estimating treatment effects in randomised clinical trials.

Authors:  Farid Jamshidian; Alan E Hubbard; Nicholas P Jewell
Journal:  Stat Methods Med Res       Date:  2011-09-08       Impact factor: 2.494

9.  Gabapentin for the symptomatic treatment of painful neuropathy in patients with diabetes mellitus: a randomized controlled trial.

Authors:  M Backonja; A Beydoun; K R Edwards; S L Schwartz; V Fonseca; M Hes; L LaMoreaux; E Garofalo
Journal:  JAMA       Date:  1998-12-02       Impact factor: 56.272

10.  Adjusting for perception and unmasking effects in longitudinal clinical trials.

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Journal:  Int J Biostat       Date:  2012-12-31       Impact factor: 1.829

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  2 in total

1.  Using instrumental variables to disentangle treatment and placebo effects in blinded and unblinded randomized clinical trials influenced by unmeasured confounders.

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Journal:  Sci Rep       Date:  2016-11-21       Impact factor: 4.379

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