RCI_newpatient: Calculate the Linear or Logistic Regression-Based Reliable...

In LogisticRCI: Linear and Logistic Regression-Based Reliable Change Index

Calculate the Linear or Logistic Regression-Based Reliable Change Index (RCI) for a New Patient Based on a Fitted Model

Description

This function calculates the RCI for a new patient based on a fitted lm or binomial glm model object.

Usage

RCI_newpatient(model, new)

Arguments

model	An lm or binomial glm object.
new	A data frame with data for the new patient.

Details

This function takes a fitted model object and new patient data as input and computes either the linear (for lm objects) or logistic (for binomial glm) regression-based reliable change index. The names of the variables in the new patient data have to match the names of the predictors and response variable for the fitted model.

Value

The function returns a numeric vector.

Author(s)

Rafael A. Moral, Unai Diaz-Orueta and Javier Oltra-Cucarella.

References

Moral, R.A., Diaz-Orueta, U., Oltra-Cucarella, J. (preprint) Logistic versus linear regression-based Reliable Change Index: implications for clinical studies with diverse sample sizes. DOI: 10.31234/osf.io/gq7az

Examples

data(RCI_sample_data)

## fitting models to sample
linear_fit <- lm(score ~ baseline + age + gender + education,
                 data = RCI_sample_data)

logistic_fit <- glm(cbind(score, 15 - score) ~ baseline + age + gender + education,
                    family = binomial,
                    data = RCI_sample_data)

## new patient data
new_patient <- data.frame("age" = 68,
                          "gender" = "male",
                          "score" = 9,
                          "baseline" = 11,
                          "education" = 12)

## calculating RCI for new patient without refitting model
RCI_newpatient(model = linear_fit, new = new_patient)
RCI_newpatient(model = logistic_fit, new = new_patient)

LogisticRCI documentation built on March 18, 2022, 5:20 p.m.