Iris Eekhout, PhD

Statistician

TNO

Biography

Iris Eekhout holds a double master in clinical psychology and methodology and statistics of psychology (Leiden University). She obtained her PhD at the Department of Epidemiology and Biostatistics of the VU University medical centre in Amsterdam. Her dissertation work resulted in novel ways of dealing with missing data in questionnaire items and total scores. Currently, Iris teaches a course on missing data analysis in the epidemiology masters program at VU University medical centre. At TNO, Iris works on a variety of projects as a methodologist and statistical analyst related to child health, e.g., measuring child development (D-score) and adaptive screenings for psycho-social problems (psycat). Her main interests and expertise are missing data methods (multiple imputation), longitudinal (multilevel) modeling, R programming, developing shiny applications, interactive visualizations, psychometrics and measurement.

Interests

Missing data methods (multiple imputation)
Longitudinal (Multilevel) modelling
R programming/shiny apps
Psychometrics and measurement

Education

PhD in Epidemiology & Biostatistics, 2015
VU University, Amsterdam
MSc in Methodology and Statistics, 2010
Leiden University
MSc in Clinical Psychology, 2010
Leiden University
BSc in Psychology, 2008
Leiden University

Posts

Missing data mechanisms

In research, missing data occur when a data value is unavailable. Many empirical studies encounter missing data. Missing data can occur in many stages of research due to many different causes in many different forms. Each type of missing data may have different reasons, and also different implication for the methods to deal with the missing data.The underlying reasons for missing data can be described as missing data mechanisms.

Last updated on Jun 28, 2022 15 min read

Missing value analysis

Missing observations are defined as NA in R. Missing data can have different implications for data summaries, analyses and conclusions based on the data with missing values. In this post, different types of missing data are reviewed and explored in data examples.

Last updated on Jun 28, 2022 7 min read

Passive multiple imputation

This post demonstrates how to perform passive multiple imputation to deal with missing items in a multi-item questionnaire.

Last updated on Jun 28, 2022 10 min read

Agreement for dichotomous outcomes

This document describes the use of the Agree package for the data example that was used in the paper on specific agreement on dichotomous outcomes in the situation of more than two raters.

Last updated on Mar 2, 2022 3 min read

Agreement for polytomous outcomes

This document describes the use of the Agree package for two data examples that are used in the paper on specific agreement on polytomous outcomes in the situation of more than two raters. The first data example is an example of ordinal ratings and the second example of nominal rating.

Last updated on Mar 2, 2022 7 min read

Projects

Don’t Miss Out!

Missing data occurs in many empirical studies. It is vital for study results to handle the missing data correctly. The best solution to deal with missing data depends on the reasons for the occurrence of missing data and on the analysis that is planned. In the project a guide was developed to find the best way to deal with missing data in multi-item questionnaires. The website www.missingdata.nl also provides a lot of information about missing data and methodology.

D-score

Children learn to walk, speak, and think at an astonishing pace. The D-score captures this process as a one-number summary. Application of the D-score enables comparisons in child development across populations, groups and individuals.

Rater agreement & reliability

In studies where more than one rater gives a judgement on a certain characteristic, the agreement between the judgements is of interest. Historically, mostly a kappa statistic is used to assess the agreement. However, the kappa statistic is a reliability measure instead of an agreement measure. It is more informative to use the percentage of absolute agreement instead. The reliability of ratings can also be obtained via different methods. The choice between the ICC oneway, ICC consistency and ICC agreement depends on the study design and goals.

Recent Publications

S. Dorresteijn, T.E. Gladwin, I. Eekhout, E. Vermetten, E. Geuze (2019). Childhood trauma and the role of self-blame on psychological well-being after deployment in male veterans. European Journal of Psychotraumatology.

Cite DOI

J.J.M. Rijnhart, J.W.R. Twisk, I. Eekhout, M.W. Heymans (2019). Comparison of logistic-regression based methods for simple mediation analysis with a dichotomous outcome variable. BMC Medical Research Methodology.

Cite DOI

L.C. Bosman, C.A.M. Roelen, J.W.R. Twisk, I. Eekhout, M.W. Heymans (2019). Development of Prediction Models for Sick Leave Due to Musculoskeletal Disorders. Journal of Occupational Rehabilitation.

Cite DOI

K. Oude Hengel, S.J.W. Robroek, I. Eekhout, A.J. Van Der Beek, A. Burdorf (2019). Educational inequalities in the impact of chronic diseases on exit from paid employment among older workers: A 7-year prospective study in the Netherlands. Occupational and Environmental Medicine.

Cite DOI

R.M. Vink, P. van Dommelen, S.M. van der Pal, I. Eekhout, F.D. Pannebakker, M. Klein Velderman, M. Haagmans, T. Mulder, M. Dekker (2019). Self-reported adverse childhood experiences and quality of life among children in the two last grades of Dutch elementary education. Child Abuse and Neglect.

Cite DOI

See all publications

Contact

R Packages & shiny

The projects highlighted on the website have R packages attached. The D-score project has multiple R packages available and several shiny application that demonstrate functionailies. The agreement project evolves around one R package and one shiny application.

Reliability and Agreement

Agree: An R package with functions to compute the Reliability and Agreement between multiple raters.
ICC power: A shiny application that can assist sample size decisions for studies on reliability.

Child development with the D-score

Missing data

Missing data mechanism