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Table 2 Quartile contrasts at each internal (Non-Terminal) node

From: Classification and regression trees for epidemiologic research: an air pollution example

Internal node no.a N Quartile contrastb Wald P-valuec Subset of pollutant quartiles to which contrast appliesd
     CO NO2 O3 PM2.5
1 3879 PM2.5: 4 vs. 1-3 0.000 All All All All
2 2878 NO2: 3–4 vs. 1-2 0.003 All All All 1-3
3 1001 NO2: 3–4 vs. 1-2 0.019 All All All 4
4 1560 O3: 4 vs. 1-3 0.096 All 1,2 All 1-3
5 1318 PM2.5: 2–3 vs. 1 0.123 All 3,4 All 1-3
7 685 O3: 4 vs. 1-3 0.128 All 3,4 All 4
8 1401 NO2: 2 vs. 1 0.086 All 1,2 1-3 1-3
9 159 CO: 3–4 vs. 1-2 0.043 All 1,2 4 1-3
14 244 NO2: 4 vs. 3 0.096 All 3,4 1-3 4
16 703 O3: 3 vs. 1-2 0.140 All 1 1-3 1-3
17 698 O3: 2–3 vs. 1 0.062 All 2 1-3 1-3
33 309 PM2.5: 3 vs. 1-2 0.033 All 1 3 1-3
  1. aThe node numbers correspond to the numbering in Figure 1 (where each node, n, produces two child nodes numbered 2n and 2n + 1).
  2. bBased on the indicator variable chosen for the best split.
  3. c P-value based on a Wald test that the beta coefficient for the quartile contrast indicator is zero.
  4. dEach subset of pollutant concentration levels represents an effect modifier of the quartile contrast and relates directly to the branching of the tree in Figure 1. Note that in the first split of the tree there is no effect modification by any of the pollutants because the entire dataset is used.