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was developed to help researchers with the implementation of precision
medicine in R. A key objective of precision medicine is to determine the
optimal treatment separately for each patient instead of applying a
common treatment to all patients. Personalizing treatment decisions
becomes particularly relevant when treatment response differs across
patients, or when patients have different preferences about benefits and
harms. This package offers statistical methods to develop and validate
prediction models for estimating individualized treatment effects. These
treatment effects are also known as the conditional average treatment
effects (CATEs) and describe how different subgroups of patients respond
to the same treatment. Presently, precmed focuses on the personalization
of two competitive treatments using randomized data from a clinical
trial (Zhao et al. 2013) or using real-world data (RWD) from a
non-randomized study (Yadlowsky et al. 2020).
Precision medicine, also known as personalized medicine, is a rapidly growing field that aims to provide individualized treatment decisions based on a patient's unique genetic, biochemical, and medical profile. Precision medicine recognizes that each patient is unique and that their medical needs and responses to treatments can be different. By taking these differences into account, precision medicine offers the potential for more effective and efficient treatments, and improved health outcomes for patients.
The importance of precision medicine can be explained by several key benefits:
In conclusion, precision medicine represents a major step forward in the field of healthcare, delivering customized solutions that meet the unique needs of each patient. We have developed an R package to help researchers in implementing precision medicine in practice. We have elaborated the package here ??
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.
oecalc (Debray et al. 2017,
et al. 2017).
valmeta (Debray et al. 2017,
et al. 2017).
et al. 2018).
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'.
mice.impute.2l.2stage.bin (binary data),
mice.impute.2l.2stage.norm (continous data) and
mice.impute.2l.2stage.pois (count data). See Audigier
et al. 2018.
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).
et al. 2018).
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.