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Determine standardized effect size of component causes

Source: R/create_scc.R
effect_size.Rd

SCC models teach us that effect strength, e.g., a risk ratio, is no natural constant but depends on the prevalence of component causes and, therefore, differs between populations. However, even without any population, this function derives effect sizes for every component cause by comparing how many sets of component causes with and without a certain cause are sufficient to cause the outcome of interest.

Usage

effect_size(scc, depends = TRUE, output = c("nice", "table"))

Arguments

scc

An object of class epicmodel_scc.

depends

TRUE (default) or FALSE. If FALSE, only includes sufficient causes with suffciency status "always".

output

A single element of type character, either "nice" (default) or "table". If "table", returns a data.frame. If "nice", a nicely formated output is printed in the console.

Value

Either a dataframe (output = "table") with one row for every component cause and with variables id (step ID), desc (step description), suff_true, suff_false, num_combos_true, num_combos_false, and ratio, or a nicely formated output in the console (output = "nice"). See Details for more information.

Details

The following algorithm is used to derive effect sizes from SCC models:

  • The effect size is derived for one specific component cause. The following steps are repeated for all of them.

  • Get all potential combinations of component causes

  • Remove combinations that contain incompatible component causes (ICC), as specified in the steplist

  • Split the set of possible combinations of component causes into two parts: Sets, in which the component cause of interest is present & sets, in which the component cause of interest is absent. The numbers are recorded and returned in the output table (output = "table") as variables num_combos_true (cause is present) and num_combos_false (cause is absent). If there are no incompatible component causes (ICC), both values should be the same.

  • Check for all possible combinations of component causes, if they are sufficient for the outcome to occur. The number of sufficient combinations are counted separately for combinations with the component cause of interest present and combinations with the component cause of interest absent. The numbers are recorded and returned in the output table (output = "table") as variables suff_true (cause is present) and suff_false (cause is absent).

  • A ratio is calculated using the following formula: (suff_true / num_combos_true) / (suff_false / num_combos_false). In the output table (output = "table"), this value is stored in variable ratio. In the nice output (output = "nice"), it is reported in the column Impact, which shows: ratio [suff_true/num_combos_true vs. suff_false/num_combos_false]

  • There are two special cases when calculating the ratio. When suff_true > 0 but suff_false == 0, the outcome only occurs if the corresponding component cause is present. The ratio then gets value necessary. When suff_true == 0 and suff_false == 0, the ratio gets value not a cause.

Examples

# Create some SCC model
steplist_checked <- check_steplist(steplist_rain)
#> 
#> ── Checking epicmodel_steplist steplist_rain ───────────────────────────────────
#> ✔ Checking WHAT IDs was successful.
#> ✔ Checking DOES IDs was successful.
#> ✔ Checking WHERE IDs was successful.
#> ✔ Checking Module IDs was successful.
#> ✔ Checking ICC IDs was successful.
#> ✔ Checking WHAT keywords was successful.
#> ✔ Checking DOES keywords was successful.
#> ✔ Checking WHERE keywords was successful.
#> ✔ Checking Module keywords was successful.
#> ✔ Checking Modules was successful.
#> ✔ Checking ICC entries was successful.
#> ✔ Checking WHAT segments was successful.
#> ✔ Checking DOES segments was successful.
#> ✔ Checking WHERE segments was successful.
#> ✔ Checking references was successful.
#> ✔ Checking start/end steps was successful.
#> ✔ Checking THEN statements was successful.
#> ✔ Checking THEN/IF/IFNOT equality was successful.
#> ✔ Checking outcome definitions was successful.
#> ── Summary ─────────────────────────────────────────────────────────────────────
#> ✔ Checking successful!
scc_model <- create_scc(steplist_checked)
#> 
#> ── Create SCC Model ──
#> 
#> ✔ 15/15 | Check if set of component causes is sufficient
#> ✔ 5/5 | Check if sufficiency dependends on IFNOT conditions
#> ✔ 5/5 | Check if sufficient cause is minimal
#> ℹ 2/5 sufficient causes are minimal

# Use the SCC model in effect_size()
effect_size(scc_model)
#> ✔ 4/4 | Check impact of every component cause
#>                        Component Cause                  Impact
#> 1                                 rain necessary [5/8 vs. 0/8]
#> 2                        get groceries      4.00 [4/8 vs. 1/8]
#> 3 IFNOT take vacation THEN no vacation      1.50 [3/8 vs. 2/8]
#> 4                              weekday      1.50 [3/8 vs. 2/8]