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joinpointR

🇪🇸 Español

El objetivo de joinpointR es ajustar modelos de regresión joinpoint por grupos y generar resúmenes en formato tidy del Cambio Porcentual Anual (APC) y del Cambio Porcentual Anual Promedio (AAPC), facilitando el análisis de tendencias en estudios epidemiológicos.

🇬🇧 English

The goal of joinpointR is to fit joinpoint regression models by groups and generate tidy summaries of the Annual Percent Change (APC) and the Average Annual Percent Change (AAPC), facilitating trend analysis in epidemiological studies.

Installation / Instalación

You can install the development version from GitHub / Podés instalar la versión en desarrollo desde GitHub:

# install.packages("pak")
pak::pak("datos-ine/joinpointR")

Repository: https://github.com/datos-ine/joinpointR

Workflow / Flujo de trabajo

The package provides a simple and reproducible workflow / El paquete propone un flujo simple y reproducible:

• Fit joinpoint models by group / Ajustar modelos joinpoint por grupo
• Extract APC by segment / Extraer APC por segmento
• Compute AAPC / Calcular AAPC
• Generate summary tables / Generar tablas resumen

Main functions / Funciones principales

• model_jp() → fit joinpoint models by group / ajusta modelos joinpoint por grupo
• get_apc() → extract APC by segment / extrae APC por segmento
• get_aapc() → compute AAPC / computa AAPC
• summary_jp() → generate summary tables (tibble or flextable) / genera tablas resumen (tibble o flextable)

Example / Ejemplo

library(joinpointR)
library(dplyr)

df <- tibble(
  year = rep(2000:2010, 2),
  rate = c(runif(11, 10, 20), runif(11, 5, 15)),
  group = rep(c("Male", "Female"), each = 11)
)

mods <- model_jp(
  data = df,
  value = "rate",
  time = "year",
  group = "group"
)

# APC (only works when class segmented lm)
get_apc(mods$Male, digits = 1, time = "year", dec = ".") # Will generate an error

get_apc(mods$Female, digits = 1, time = "year", dec = ".")

# AAPC with 95% CI
get_aapc(mods$Male, show_ci = TRUE)

# AAPC with significance stars
get_aapc(mods$Male, show_ci = FALSE)

# Summary Table
summary_jp(mods)

Formatted table / Tabla formateada

# English (default)
summary_jp(mods, ft = TRUE, lan = "en", var1 = "Sex")

# Spanish
summary_jp(mods, ft = TRUE, lan = "es", var1 = "Sexo")

Returns a table ready for reporting (e.g., Word) using flextable. / Devuelve una tabla lista para exportar a Word o informes mediante flextable.

Output/Salida

The generated table includes / La tabla generada incluye:

• Number of joinpoints / Número de joinpoints (JP)
• Time periods for each segment / Períodos de cada segmento
• APC per segment / por segmento
• 95% Confidence intervals / Intervalos de confianza al 95%
• AAPC (global tendency / tendencia global)

Plots / Gráficos

# Plot results
mods |>
  gg_joinpoint(obs = TRUE, jp = TRUE, facets = FALSE)

# Facets by group
mods |>
  gg_jpoint(obs = TRUE, jp = TRUE, facets = TRUE)

# Hide observed
mods |>
  gg_jpoint(obs = FALSE, jp = TRUE, facets = FALSE)

# Hide joinpoints
mods |>
  gg_jpoint(obs = TRUE, jp = FALSE, facets = FALSE)

Dependencies / Dependencias

The package uses / El paquete utiliza:

• segmented for fitting jointpoint regression models / para regresión joinpoint
• dplyr, purrr, tidyr, tibble for data management / para manipulación de datos
• ggplot2 for plotting results / para graficar resultados
• flextable for summary tables / para tablas formateadas

Notes / Notas

• The response variable is log-transformed / La variable respuesta se transforma logarítmicamente
• Model selection is based on the Bayesian Information Criterion (BIC) / La selección de modelos se basa en el Criterio de Información Bayesiano (BIC)
• Results are returned in tidy format / Los resultados se devuelven en formato tidy para facilitar su uso en análisis reproducibles
• Formatted tables are optional / La creación de tablas formateadas es opcional (ft = TRUE)

Licence / Licencia

MIT License

Author / Autora

Tamara Ricardo Instituto Nacional de Epidemiología (INE), Argentina ORCID: https://orcid.org/0000-0002-0921-2611

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.