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type: Ontology
creation date: 2026-08-15T14:36:58+02:00
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Imputation of missing data in clinical trials
type: Subject
type: Category:Theme
page: https://embassy.science:443/wiki/Theme-3A09d02462-2D6fbb-2D45eb-2Db83b-2Dc7cd1da6d224
wiki namespace: 7600
wiki page content language: en
display title: Imputation of missing data in clinical trials
has best practice: '''Planning stage of the clinical trial''' To reduce the risks of missing data in the panning of the clinical trial, statistical analyses should be specified, key data items should be identified, and the procedures to prevent missing data '"`UNIQ--ref-0000000D-QINU`"'. '''Analysis stage''' There are different strategies to deal with missing data that will depend on the specific clinical trial and type of missing data: 1. Complete cases analysis could be used when the proportions of missing data are below 5%and the potential impact of the missing data is negligible '"`UNIQ--ref-0000000E-QINU`"'. 2. Single imputation replaces missing values by a value defined by a certain rule. However, this method ignores the data variation and can potentially introduce bias and should be used with great caution '"`UNIQ--ref-0000000F-QINU`"'. 3. When the missing data accomplish certain characteristics, multiple imputation may be used to minimize bias . Missing values are replaced by a random sample of plausible values imputations. There are several multiple imputation methodologies that must be chosen according to the variable with missing values '"`UNIQ--ref-00000010-QINU`"'. To conclude, handling missing data validly is an important, yet difficult and complex, task. This theme showed different strategies to handle missing data but always statistical expertise’s advice is needed. '"`UNIQ--references-00000011-QINU`"'
has good practice and misconduct: Bias
has good practice and misconduct: Clinical trials
has timepoint: 30/05/2022
has virtue and value: Accountability
has virtue and value: Care
has virtue and value: Excellence
important because: Missing data are unavoidable in clinical trials. Frequently, complete cases analysis is used only including individuals with no missing data '"`UNIQ--ref-00000009-QINU`"'. However, that can generate bias and can lead to exclude several individuals, causing loss of precision and power '"`UNIQ--ref-0000000A-QINU`"'. The risk of bias from missing data depends on the cause '"`UNIQ--ref-0000000B-QINU`"': Missing completely at random: There are no systematic differences between the missing values and the observed values. Missing at random: Any systematic difference between the missing values and the observed values can be explained by differences in observed data. Missing not at random: Systematic differences remain between the missing values and the observed values. The determination of the type of missing values is difficult due to the nature of missing values '"`UNIQ--ref-0000000C-QINU`"'. Therefore, practical guidelines are needed to deal with missing data.
important for: Legal Advisor
important for: Researcher
important for: Research Ethics Committee Member
important for: Health / Clinical Researcher
involves: Victor Urbiola-Salvador
is about: Missing data is always a limitation in the interpretation of clinical trial results. This missing data may seriously affect the inference from clinical trials. Therefore, it is necessary to establish the criteria to handle missing data prior to the clinical trial. Imputation is a potential tool to overcome the bias from missing data but it must be carefully used.
main category: Category:Theme
related to research area: LS 02.10 - Bioinformatics
related to research area: LS 02.12 - Biostatistics
theme type: Good Practices
title: Imputation of missing data in clinical trials
wiki page creation date: 2022-05-30T14:47:59Z
creation date#aux: 2459730.1166551
display title of: Imputation of missing data in clinical trials
last editor is: User:0000-0003-4024-8548
wiki page modification date: 2026-06-02T15:58:52Z
modification date#aux: 2461194.1658796
is a new page: false
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page creator: User:0000-0002-8177-2744
page author: User:0000-0003-4024-8548
page author: User:0000-0002-8177-2744

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