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We provide statistical support for pharmaceutical industry, data science services and out-of-the-box solutions. We focus on solutions that are flexible, adaptable, and robust.Schedule a meeting
We assist in the collection, quality appraisal and cleaning of large structured and unstructured datasets.
We develop and evaluate state-of-the-art models for prediction, classification and causal inference.
We conduct literature reviews and (network) meta-analyses
Research & Innovation
We investigate, customize and improve algorithms for statistical inference and machine learning.
We offer ad hoc support to address your questions on data collection, manipulation, analysis and interpretation.
We prepare macros, review source code, and implement algorithmic procedures in various programming languages.
Stats and Figures
So far, we served 6 clients with an average contract duration of 8 months.Get in touch now
We are experienced in R, Python, Java, and many other programming languages.
Statistical analysis and data visualization
Estimation of complex statistical models
Estimation of complex statistical models
Deployment of web-based applications that involve artificial intelligence and machine learning.
Development of graphical user interfaces
Development of web applications
Meta-Analysis of Diagnosis and Prognosis Research Studies
Meta-analysis of diagnostic and prognostic modeling studies. Summarize estimates of prognostic factors, diagnostic test accuracy and prediction model performance. Validate, update and combine published prediction models. Develop new prediction models with data from multiple studies.
- R package available from CRAN and R-Forge.
- Maintained by Thomas Debray & Valentijn de Jong
- Data preparation for systematic reviews of prediction model performance via
oecalc(Debray et al. 2017, 2018 and Snell et al. 2017).
- Meta-analysis of prediction model performance via
valmeta(Debray et al. 2017, 2018 and Snell et al. 2017).
- Evaluation of funnel plot asymmetry and publication bias via
fat(Debray et al. 2018).
- Generation of forest plots via
Multivariate Imputation by Chained Equations
Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011). Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Multiple Imputation by Chained Equations with Multilevel Data
The micemd package provides methods to perform multiple imputation using chained equations in the presence of multilevel data. It includes imputation methods that account for both sporadically and systematically missing values of continuous, binary and count variables. Following the recommendations of Audigier et al. (2018), the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation for 'mice'.
- R package available from CRAN and GitHUB (maintained by Vincent Audigier).
- Imputation of sporadically and systematically missing values in multilevel data via
mice.impute.2l.2stage.norm(continous data) and
mice.impute.2l.2stage.pois(count data). See Audigier et al. 2018.
- Imputation of univariate missing data using a Bayesian generalized linear mixed model with non-informative prior distributions via
mice.impute.2l.glm.norm(continous data) and
mice.impute.2l.glm.pois(count data). See Jolani, Debray et al. (2015) and Audigier et al. (2018).
- Predictive mean matching imputation for multilevel data via
mice.impute.2l.2stage.pmm(Audigier et al. 2018).