Are We Estimating the Mean and Variance Correctly in the Presence of Observations Outside of Measurable Range?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F25%3A00140457" target="_blank" >RIV/00216224:14310/25:00140457 - isvavai.cz</a>

Result on the web

<a href="https://bpspubs.onlinelibrary.wiley.com/doi/10.1002/prp2.70048" target="_blank" >https://bpspubs.onlinelibrary.wiley.com/doi/10.1002/prp2.70048</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/prp2.70048" target="_blank" >10.1002/prp2.70048</a>

Result language

angličtina

Original language name

Are We Estimating the Mean and Variance Correctly in the Presence of Observations Outside of Measurable Range?

Original language description

Laboratory measurements used for safety assessments in clinical trials are subject to the limits of the used laboratory equipment. These limits determine the range of values which the equipment can accurately measure. When observations fall outside the measurable range, this creates a problem in estimating parameters of the normal distribution. It may be tempting to use methods of estimation that are easy to implement, however selecting an incorrect method may lead to biased estimates (under- or overestimation) and change the research outcomes, for example, incorrect result of two-sample test about means when comparing two populations or biased estimation of regression line. In this article, we consider the use of four methods: ignoring unmeasured observations, replacing unmeasured observations with a multiple of the limit, using a truncated normal distribution, and using a normal distribution with censored observations. To compare these methods we designed a simulation study and measured their accuracy in several different situations using relative error $$ frac{hat{mu}-mu }{mu } $$, ratio $$ frac{hat{sigma}}{sigma } $$, and mean square errors of both parameters. Based on the results of this simulation study, if the amount of observations outside of measurable range is below 40%, we recommend using a normal distribution with censored observations in practice. These recommendations should be incorporated into guidelines for good statistical practice. If the amount of observations outside of measurable range exceeds 40%, we advise not to use the data for any statistical analysis. To illustrate how the choice of method can affect the estimates, we applied the methods to real-life laboratory data.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Pharmacology Research and Perspectives

ISSN

2052-1707

e-ISSN

—

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

„e70048“

UT code for WoS article

001380654200001

EID of the result in the Scopus database

2-s2.0-85212712388