plot_continuous {graphPAF}

Plot hazard ratios, odds ratios or risk ratios comparing differing values of a continuous exposure to a reference level

Description

Plot hazard ratios, odds ratios or risk ratios comparing differing values of a continuous exposure to a reference level

Usage

plot_continuous(
  model,
  riskfactor,
  data,
  S = 10,
  ref_val = NA,
  ci_level = 0.95,
  min_risk_q = 0.1,
  plot_region = TRUE,
  plot_density = TRUE,
  n_x = 10000,
  theylab = "OR",
  qlist = seq(from = 0.001, to = 0.999, by = 0.001),
  interact = FALSE
)

Arguments

model

A fitted model (either glm, clogit or coxph)

riskfactor

The string name of a continuous exposure or risk factor represented in the data and model

data

Data frame used to fit the model

S

Default 10. The integer number of random samples used to calculate average differences in linear predictors. Only relevant to set when interact=TRUE

ref_val

The reference value used in plotting. If left at NA, the median value of the risk factor is used

ci_level

Numeric. A number between 0 and 1 specifying the confidence level

min_risk_q

Default .1. A number between 0 and 1 representing the desired risk quantile for the continuous risk factor

plot_region

Default TRUE. Logical specifying whether the targeted region corresponding to an intervention setting the continuous risk factor at a quantile min_risk_q or lower is to be plotted

plot_density

Default TRUE. Logical specifying whether density of distribution of risk factor is to be added to the plot

n_x

Default 10000. How many values of riskfactor will be used to plot spline (when interact=FALSE)

theylab

Default "OR". Y-axis label of the plot

qlist

Vector of quantile values for q, corresponding to the plotted values of PAF_q for each risk factor/exposure

interact

Default "FALSE". Set to TRUE spline models enter as interactions.

Value

A ggplot2 plotting object

References

Ferguson, J., Maturo, F., Yusuf, S. and O’Donnell, M., 2020. Population attributable fractions for continuously distributed exposures. Epidemiologic Methods, 9(1)

Examples

library(survival)
library(splines)
model_continuous <- glm(formula = case ~ region * ns(age, df = 5) +
 sex * ns(age, df = 5) + education +exercise + ns(diet, df = 3) +
 alcohol + stress + ns(lipids,df = 3) + ns(waist_hip_ratio, df = 3) +
  high_blood_pressure, family = "binomial", data = stroke_reduced)
plot_continuous(model_continuous,riskfactor="diet",data=stroke_reduced)