Evidence map›Paper›PMID 30665989›Full record

ArticleEvidence-based mental health2019

Development and validation of a prediction model for the probability of responding to placebo in antidepressant trials: a pooled analysis of individual patient data.

Kiyomi Shinohara, Shiro Tanaka, Hissei Imai, Hisashi Noma, Kazushi Maruo, Andrea Cipriani, Shigeto Yamawaki, Toshi A Furukawa

Abstract readValidation Study
In one paragraph

Article in Evidence-based mental health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 4 pooled it
–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

6 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Optimizing ExpectationsFrontiers in psychiatry · 2019
    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.

Kiyomi ShinoharaDepartment of Health Promotion and Human Behavior and of Clinical Epidemiology, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.ORCID -0003-3527-4004
Shiro TanakaDepartment of Clinical Biostatistics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Hissei ImaiDepartment of Health Promotion and Human Behavior and of Clinical Epidemiology, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.
Hisashi NomaDepartment of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.
Kazushi MaruoDepartment of Biostatistics, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.
Andrea CiprianiDepartment of Psychiatry, University of Oxford, Oxford, UK.ORCID 0000-0001-5179-8321
Shigeto YamawakiAcademic-Industrial Cooperation Office, Hiroshima University, Hiroshima, Japan.
Toshi A FurukawaDepartment of Health Promotion and Human Behavior and of Clinical Epidemiology, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan.

Funding

Department of Health RP-2017-08-ST2-006Medical Research Council MC_PC_17215
6 · The paper itself

Abstract

backgroundIdentifying potential placebo responders among apparent drug responders is critical to dissect drug-specific and nonspecific effects in depression.

objectiveThis project aimed to develop and test a prediction model for the probability of responding to placebo in antidepressant trials. Such a model will allow us to estimate the probability of placebo response among drug responders in antidepressants trials.

methodsWe identified all placebo-controlled, double-blind randomised controlled trials (RCTs) of second generation antidepressants for major depressive disorder conducted in Japan and requested their individual patient data (IPD) to pharmaceutical companies. We obtained IPD (n=1493) from four phase II/III RCTs comparing mirtazapine, escitalopram, duloxetine, paroxetine and placebo. Out of 1493 participants in the four clinical trials, 440 participants allocated to placebo were included in the analyses. Our primary outcome was response, defined as 50% or greater reduction on Hamilton Rating Scale for Depression at study endpoint. We used multivariable logistic regression to develop a prediction model. All available candidate of predictor variables were tested through a backward variable selection and covariates were selected for the prediction model. The performance of the model was assessed by using Hosmer-Lemeshow test for calibration and the area under the ROC curve for discrimination.

findingsPlacebo response rates differed between 31% and 59% (grand average: 43%) among four trials. Four variables were selected from all candidate variables and included in the final model: age at onset, age at baseline, bodily symptoms, and study-level difference. The final model performed satisfactorily in terms of calibration (Hosmer-Lemeshow p=0.92) and discrimination (the area under the ROC curve (AUC): 0.70).

conclusionsOur model is expected to help researchers discriminate individuals who are more likely to respond to placebo from those who are less likely so. CLINICAL IMPLICATIONS: A larger sample and more precise individual participant information should be collected for better performance. Examination of external validity in independent datasets is warranted. TRIAL REGISTRATION NUMBER: CRD42017055912.

Indexed as

Models, StatisticalOutcome Assessment, Health CarePlacebo EffectAdultAntidepressive Agents, Second-GenerationClinical Trials, Phase II as TopicClinical Trials, Phase III as TopicFemaleHumansJapanMajor Depressive DisorderMaleMiddle AgedPlacebosPrognosisRandomized Controlled Trials as TopicAntidepressive Agents, Second-GenerationPlacebos

Identifiers

PMID30665989
PMCPMC10270413

What OpenQuestion holds

Textmetadata
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.