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Perform Step-Down Rao-Scott Adjusted Cochran-Armitage Trend Test Procedure

Source: R/RSCABS_AO.R
step_down_RSCABS.Rd

This function performs a step-down procedure using Rao-Scott adjusted Cochran-Armitage trend tests (RSCABS). The procedure systematically excludes the highest treatment groups to identify at which treatment level the dose-response relationship becomes significant.

Usage

step_down_RSCABS(
  data,
  treatment_col = "tmt",
  treatment_order = NULL,
  max_score = NULL,
  min_score = 1,
  score_cols = NULL,
  replicate_col = "tank",
  total_col = "total",
  direction = "greater",
  alternative = "greater",
  include_fisher = TRUE
)

Arguments

data

A data frame containing fish injury data

treatment_col

Name of the column containing treatment groups (default: "tmt")

treatment_order

Optional vector specifying the order of treatment groups from lowest to highest dose. If NULL, alphabetical order is used (default: NULL)

max_score

Maximum score value to consider (default: NULL, auto-detected)

min_score

Minimum score value to consider (default: 1)

score_cols

Character vector of column names containing injury scores (default: NULL, auto-detected)

replicate_col

Name of the column containing tank/replicate IDs (default: "tank")

total_col

Name of the column containing total counts (default: "total")

direction

Character string indicating threshold direction: "greater" for \ge threshold, "less" for \le threshold (default: "greater")

alternative

Character string specifying the alternative hypothesis: "two.sided", "greater" (proportion increases with treatment level), or "less" (proportion decreases with treatment level) (default: "greater")

include_fisher

Logical indicating whether to use Fisher's exact test when RSCA fails (default: TRUE)

Value

A list of class "StepDownRSCABS" containing:

combined_results

Data frame with test results for all steps and thresholds

step_results

List of RSCABS objects for each step

summary

Summary of significant findings by step

lowest_significant

Information about the lowest significant treatment level

parameters

List of parameters used for the analysis

Examples

# Example data
fish_data <- data.frame(
  tmt = c(rep("Control", 8), rep("Low", 4), rep("Medium", 4), rep("High", 4)),
  tank = c(paste0("C", 1:8), paste0("L", 1:4), paste0("M", 1:4), paste0("H", 1:4)),
  S0 = c(3, 2, 3, 2, 2, 1, 2, 3, 3, 3, 4, 2, 2, 3, 2, 2, 2, 3, 1, 2),
  S1 = c(1, 2, 0, 1, 2, 2, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 2, 0),
  S2 = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1),
  S3 = c(0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1),
  total = c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)
)

# Run step-down procedure with default parameters
result <- step_down_RSCABS(fish_data, treatment_col = "tmt",
                          treatment_order = c("Control", "Low", "Medium", "High"))
#> Warning: Some treatment groups have zero affected individuals at threshold S3+. RSCA test may not be valid.
#> Warning: Some treatment groups have zero affected individuals at threshold S3+. RSCA test may not be valid.
#> Warning: Some treatment groups have zero affected individuals at threshold S3+. RSCA test may not be valid.

# Print results
print(result)
#> Step-Down RSCABS Analysis
#> ========================
#> 
#> Parameters:
#>   Direction: greater 
#>   Alternative hypothesis: greater 
#>   Treatment levels: Control, Low, Medium, High 
#> 
#> Summary of findings:
#> Step 1 : Included treatments: Control, Low, Medium, High 
#>   No significant findings
#> Step 2 : Included treatments: Control, Low, Medium 
#>   No significant findings
#> Step 3 : Included treatments: Control, Low 
#>   No significant findings

# Plot results
plot(result)