Missing inaction: the dangers of ignoring missing data.

By Shinichi Nakagawa, R. Freckleton. Trends in Ecology & Evolution, 2008

367 citations recorded by the source corpus.

Subfields: Genetics, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/be0a7a5b196a00eb70a428e4676f1797037e01ba

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Citations in the corpus

A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, background): “See also Gelman & Hill (2007 p. 45).”
Global gaps in trait data for terrestrial vertebrates (2020, background): “The simplest one consists of retaining complete cases only by filtering out missing values (case deletion; see Nakagawa & Freckleton (2008)).”
General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi‐trait models for continuous and categorical characters (2010, methodology): “…important in the context of meta-analysis and comparative analysis because they may be able to correct for the publication bias that arises through nonrandom sampling of taxa, for example when common or ‘fluffy’ species are over-represented (Fisher et al., 2003; Nakagawa & Freckleton, 2008).”
How much of the world is woody? (2014, methodology): “This sampling bias also affects many commonly used methods in ecological and evolutionary research (e.g. Ackerly 2000; Nakagawa & Freckleton 2008; Pennell & Harmon 2013; Pakeman 2014) in addition to its well-understood effects on conventional statistics.”
Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework (2010, methodology): “Ultimately, as the IT-AIC approach simply assesses the relative consistency of models, given the data at hand, the success of an IT-AIC analysis depends on the models proposed and the quality of the data (e.g. Nakagawa and Freckleton 2008 ).”
The seven deadly sins of comparative analysis (2009, background): “The problems of missing data in evolutionary biology have been reviewed recently by Nakagawa & Freckleton (2008).”
A social network perspective on measurements of dominance hierarchies (2012, methodology): “Randomization procedures are often used to reduce bias in such imputation methods (Nakagawa & Freckleton 2008).”
BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography (2015, background): “These approaches provide useful imputations in some cases, but whilst deleting missing cases can introduce bias in model parameters (Nakagawa & Freckleton, 2008), taking the ‘mean’ adds new data points without adding new information, which results in incorrect confidence limits.”
Imputation of missing data in life‐history trait datasets: which approach performs the best? (2014, background): “Methods in Ecology and Evolution © 2014 British Ecological Society consequence of removing incomplete observations is a decrease in statistical power, due to decreased sample size (Nakagawa & Freckleton 2008).”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “that observations are available for each individual and for each variable ( Nakagawa and Freckleton 2008, 2010 ).”
Robustness of linear mixed‐effects models to violations of distributional assumptions (2020, background): “If missing data depend on covariate values they are called missing at random (MAR) while when missing data depend on themselves (i.e. the response variable) or unobserved predictors, they are called missing not at random (MNAR; Nakagawa, 2015; Nakagawa & Freckleton, 2008).”
Model averaging, missing data and multiple imputation: a case study for behavioural ecology (2010, methodology): “In behavioural ecology, it is probably reasonable to assume that missing data are not MCAR because there are often biological reasons behind the pattern of missingness (the aforementioned example of animal personality; Hadfield 2008; Nakagawa and Freckleton 2008).”
Changing philosophies and tools for statistical inferences in behavioral ecology (2009, methodology): “The problem of missing data, another aspect of data quality, is a neglected topic in the field of ecology and evolution (Hadfield 2008; Nakagawa and Freckleton 2008), although missing data issues are probably present in most data sets behavioral ecologists deal with.”
A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion (2010, background): “There are ways around this problem, recently outlined by Nakagawa and Freckleton (2008, 2010) , including modelbased augmentation and multiple imputation methods.”
Preferred reporting items for systematic reviews and meta‐analyses in ecology and evolutionary biology: a PRISMA extension (2021, methodology): “…analysis as a method of dealing with missing data, but it is important to be explicit about this step, both for the sample size implications (Item 20) and because of the potential to introduce bias when data are not ‘missing completely at random’ (Nakagawa & Freckleton, 2008; Little & Rubin, 2020).”