Evidence map›Paper›PMID 27110045›Full record

ArticleJournal of the American Statistical Association2016

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

Wei Liu, Zhiwei Zhang, R Jason Schroeder, Martin Ho, Bo Zhang, Cynthia Long, Hui Zhang, Telba Z Irony

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond.medRxiv : the preprint server for health sciences · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wei LiuDepartment of Mathematics, Harbin Institute of Technology, Harbin, P. R. China; Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Zhiwei ZhangDivision of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
R Jason SchroederDivision of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Martin HoDivision of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Bo ZhangDivision of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Cynthia LongDivision of Reproductive, Gastro-Renal, and Urological Devices, Office of Device Evaluation, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
Hui ZhangDepartment of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Telba Z IronyOffice of Biostatistics and Epidemiology, Center for Biologic Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.

Funding

Intramural FDA HHS FD999999
6 · The paper itself

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.

Indexed as

causal inferenceconfoundingdouble robustnessG-computationinverse probability weightingsequential regressiontargeted minimum loss based estimationtreatmentality

Identifiers

PMID27110045
PMCPMC4838817

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.