Evidence map›Paper›PMID 40079652›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2025

Toward Power Analysis for Partial Least Squares-Based Methods.

Angela Andreella, Livio Finos, Bruno Scarpa, Matteo Stocchero

Abstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Angela AndreellaDepartment of Economics and Management, University of Trento, Trento, Italy.ORCID 0000-0002-1141-3041
Livio FinosDepartment of Statistical Sciences, University of Padova, Padova, Italy.
Bruno ScarpaDepartment of Statistical Sciences, University of Padova, Padova, Italy.
Matteo StoccheroDepartment of Women's and Children's Health, University of Padova, Padova, Italy.

Funding

Università Ca' Foscari Venezia PON 2014-2020/DM 1062Università degli Studi di Padova BIRD2020/SCAR ASSEGNIBIRD202001
6 · The paper itself

Abstract

In recent years, power analysis has become widely used in applied sciences, with the increasing importance of the replicability issue. When distribution-free methods, such as partial least squares (PLS)-based approaches, are considered, formulating power analysis is challenging. In this study, we introduce the methodological framework of a new procedure for performing power analysis when PLS-based methods are used. Data are simulated by the Monte Carlo method, assuming the null hypothesis of no effect is false and exploiting the latent structure estimated by PLS in the pilot data. In this way, the complex correlation data structure is explicitly considered in power analysis and sample size estimation. The paper offers insights into selecting test statistics for the power analysis procedure, comparing accuracy-based tests and those based on continuous parameters estimated by PLS. Simulated and real data sets are investigated to show how the method works in practice.

Indexed as

BiometryLeast-Squares AnalysisMonte Carlo Methodclassificationomics datapartial least squarespermutation testspower analysis

Identifiers

PMID40079652
PMCPMC11905696

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.