ArticleOpen research Europe2026
Applying advanced psychometrics to antipsychotic treatment trials: A protocol comparing effect estimates.
Article in Open research Europe, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Antipsychotic trials in schizophrenia quantify treatment efficacy using clinician-rated scales such as the Positive and Negative Syndrome Scale (PANSS) and Brief Psychiatric Rating Scale (BPRS). If these scales include redundant or weakly informative items, measurement noise can blur drug-comparator differences that may dilute real effect sizes. Modern psychometric methods can refine and re-weight symptom scores that may improve precision and potentially increase detectable treatment effects, although it is unclear which approaches might be best. Aims: To 1) determine best psychometric models for the PANSS and BPRS, and 2) test whether psychometrically-informed scoring of PANSS/BPRS alters estimated antipsychotic effect sizes compared with conventional total-score analyses, and 3) to explore whether any changes differ across symptom domains and by sex. Method: We will conduct secondary analyses of anonymised individual participant data from randomized Phase II-IV schizophrenia trials accessed via secure data-sharing platforms (YODA Project and Vivli). Item-level PANSS/BPRS data will be harmonised at baseline and outcome. Psychometric models will be derived using confirmatory factor analysis, item response theory, and network analysis. Stability and invariance analyses will be conducted, examining covariates such as age and sex. Antipsychotic efficacy will be estimated using mixed-effects participant-level models, expressed as Cohen's d standardised mean differences (SMDs) for both conventional and psychometrically-informed outcomes. The primary outcome will be the difference in SMDs between conventional and psychometrically-informed effect sizes, summarised across trials. Conclusions: This study will determine whether best practice psychometric refinement of existing measures meaningfully changes antipsychotic effect size estimates, informing outcome measurement choices and interpretation of trial results.
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Registered trials
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