mcda: Multi criteria decision analysis
In InnovationValueInitiative/IVI-NSCLC: The IVI-NSCLC model

Description Usage Arguments See Also Examples

Conduct a multi criteria decision analysis (MCDA) and compute scores for competing treatment strategies using output from a probabilistic sensitivity analysis (PSA).

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mcda(x, sample, strategy, criteria, criteria_min = NULL,
  criteria_max = NULL, optimal = NULL, weights, score_min = 0,
  score_max = 100)

`x`	A `data.frame` or `data.table` of simulation output characterizing the probability distribution of model outcomes.
`sample`	Character name of column from `x` denoting a randomly sampled parameter set from the PSA.
`strategy`	Character name of column from `x` denoting treatment strategy.
`criteria`	A vector of character names of columns from `x` denoting the criteria to use in the MCDA.
`criteria_min`	A vector of minimum values for each criterion. If `NULL`, then the minimum value is computed automatically.
`criteria_max`	A vector of maximum values for each criterion. If `NULL`, then the maximum value is computed automatically.
`optimal`	A character vector denoting whether the optimal value of each criteria is `"low"` or `"high"`. If an element is `"low"`, then lower performance on that criterion is better, and, if an element is `"high"`, then higher performance on that criterion is better. Must be specified if either `criteria_min` or `criteria_min` is `NULL`.
`weights`	Weights to apply to each criteria. Internally normalized to sum to 1.
`score_min`	Minimum of total value score. Default is 0.
`score_max`	Maximum of total value score. Default is 100.

performance_matrix, lpvf_plot_data

n_samples <- 5
strategies <- c("Strategy 1", "Strategy 2")
outcome1 <- c(rnorm(n_samples, mean = 10, sd = 5),
              rnorm(n_samples, mean = 8, sd = 4))
outcome2 <- c(rnorm(n_samples, mean = 1500, sd = 90),
              rnorm(n_samples, mean = 1000, sd = 100))
outcomes <- data.frame(sample = rep(1:n_samples, length(strategies)),
                       strategy_id = rep(strategies, each = n_samples),
                       criteria1 = outcome1,
                       criteria2 = outcome2)

# Performance matrix
performance_mat <- performance_matrix(outcomes, 
                                      strategy = "strategy_id", 
                                      criteria = c("criteria1", "criteria2"),
                                      rownames = c("Criteria 1", "Criteria 2"), 
                                      colnames = strategies)
print(performance_mat)
                                                                                                
# MCDA                        
weights <- c(.7, .3)
mcda <- mcda(outcomes, sample = "sample", strategy = "strategy_id",
             criteria = c("criteria1", "criteria2"),
             weights = weights,
             optimal = c("low", "high"))
names(mcda)

# Scores on common scale
print(mcda$scores)

# "Total value"
print(mcda$total_value)

# "Total value" decomposed by criteria
print(mcda$weighted_scores)

# Probability of ranking
print(mcda$prob_rank)