Evidence map›Paper›PMID 25890220›Full record

ArticleBMC research notes2015

Analytical strategies for the marble burying test: avoiding impossible predictions and invalid p-values.

Stanley E Lazic

Abstract read
In one paragraph

Article in BMC research notes, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
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  10. Review
  11. The Chronic Treatment With 5-HTNeurochemical research · 2020
    Article
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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

1 author.

Stanley E LazicIn Silico Lead Discovery, Novartis Institutes for Biomedical Research, Basel, Switzerland. stan.lazic@cantab.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe marble burying test is used to measure repetitive and anxiety-related behaviour in rodents. The number of marbles that animals bury are count data (non-negative integers), which are bounded below by zero and above by the number of marbles present. Count data are often analysed using normal linear models, which include the t-test and analysis of variance (ANOVA) as special cases. Linear models assume that the data are unbounded and that the variance is constant across groups. These requirements are rarely met with count data, leading to 95% confidence intervals that include impossible values (less than zero or greater than the number of marbles present), misleading p-values, and impossible predictions. Transforming the data or using nonparametric methods are common alternatives but transformations do not perform well when many zero values are present and nonparametric methods have several drawbacks.

findingsThe problems with using normal linear models to analyse marble burying data are demonstrated and generalised linear models (GLMs) are introduced as more appropriate alternatives.

conclusionsGLMs have been specifically developed to deal with count and other types of non-Gaussian data, are straightforward to use and interpret, and will lead to more sensible inferences.

Indexed as

Linear ModelsAnalysis of VarianceAnimalsAnxietyAutistic DisorderBehavior, AnimalCalcium CarbonateDisease Models, AnimalMiceObsessive-Compulsive DisorderPyridinesStatistical DistributionsValproic Acid6-methyl-2-(phenylethynyl)pyridineCalcium CarbonatePyridinesValproic Acid

Identifiers

PMID25890220
PMCPMC4395904

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.