Interpret and Describe Causal Effect Estimates Using E-values (Deprecated)

`r lifecycle::badge("deprecated")`

This function is deprecated. Please use `margot_interpret_marginal()` instead.

This function interprets the output of causal effect analysis, providing textual descriptions of causal effect estimates. It categorises the strength of evidence for causality based on E-values and confidence intervals, and generates a detailed interpretation of the effect estimates according to specified causal scales (i.e., "causal_difference" or "risk_ratio") and estimands. This function supports interpreting results on both the causal difference and risk ratio scales.

Usage

margot_interpret_table(
  df,
  type = c("RD", "RR"),
  estimand = NULL,
  order = "default",
  original_df = NULL
)

Arguments

df

Data frame containing causal effect estimates, expected to include columns for outcome names, effect estimates (either differences or ratios), confidence intervals, E-values, and a summary estimate label. The structure of `df` should align with the specified `causal_scale`.

estimand

Optional character string indicating the type of causal estimand interpreted: "PATE" (Population Average Treatment Effect), "ATE" (Average Treatment Effect), "ATT" (Average Treatment Effect in the Treated), "CATE" (Conditional Average Treatment Effect), or "LMTP" (Longitudinal Modified Treatment Policy). Default is NULL.

order

Character string specifying the order of results. Default is "default".

causal_scale

Character string specifying the causal scale used in the analysis. Currently supports "causal_difference" for differences in means or medians, and "risk_ratio" for comparing ratios of probabilities or risks.

Value

A list containing two elements:

estimand_description

A character string describing the specified estimand, or NULL if no estimand was provided.

interpretation

A character string containing a detailed interpretation of each outcome in `df`, including the causal contrast, E-values, and the strength of evidence for causality.

Examples

if (FALSE) { # \dontrun{
# Assuming `group_tab_output` is the result from a causal analysis
result <- margot_interpret_table(group_tab_output, "causal_difference", "ATE")
cat(result$estimand_description)
cat(result$interpretation)

# Without specifying an estimand
result <- margot_interpret_table(group_tab_output, "risk_ratio")
cat(result$interpretation)
} # }