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. Author manuscript; available in PMC: 2024 Jan 10.
Published in final edited form as: Stat Med. 2023 Apr 5;42(14):2420–2438. doi: 10.1002/sim.9730

Table 3.

Class assignment results: Median (25th, 75th percentile) number of subjects in assigned class versus true class from 3-class model for 500 randomly generated datasets with 3 classes and 100 subjects per class by estimation method and correlation structure and level assumed when generating the data.

LCTA Method
Generated as Assigned Class 1 Assigned Class 2 Assigned Class 3
True ∑ True ρ True C1 True C2 True C3 True C1 True C2 True C3 True C1 True C2 True C3
I = AR(1) = CS 0.00 93 (91,95) 6 (4,9) 0 (0,0) 7(5,9) 87 (84,90) 6 (4,9) 0 (0,0) 6 (4,9) 94 (91,96)
AR(1) 0.05 93 (90,95) 7(5,10) 0 (0,0) 7(5,10) 86 (82,89) 7(5,10) 0 (0,0) 7(5,10) 93 (90,95)
AR(1) 0.10 91 (88,94) 8 (5,10) 0 (0,0) 9 (6,12) 84 (81,88) 8 (6,12) 0 (0,0) 7(5,10) 92 (88,94)
AR(1) 0.25 88 (84,92) 10 (6,13) 0 (0,0) 12 (8,16) 81 (76,84) 12 (8,16) 0 (0,0) 9 (6,13) 88 (84,92)
AR(1) 0.50 82 (75,87) 12 (8,16) 0 (0,0) 18 (13,24) 76 (71,79) 19 (14,25) 0 (0,0) 11 (8,16) 81 (75,86)
CS 0.05 91 (88,94) 7(5,10) 0 (0,0) 9 (6,12) 84 (81,88) 8 (6,11) 0 (0,0) 7(5,10) 92 (89,94)
CS 0.10 90 (86,93) 9 (6,12) 0 (0,0) 10 (7,14) 82 (78,86) 11 (7,15) 0 (0,0) 8 (5,12) 89 (85,93)
CS 0.25 84 (78,89) 11 (7,15) 0 (0,0) 16 (11,21) 77 (73,81) 16 (11,22) 0 (0,0) 10 (7,15) 84 (78,89)
CS 0.50 78 (71,84) 14 (10,19) 0 (0,0) 22 (16,29) 71 (67,75) 25 (18,31) 0 (0,0) 13 (9,18) 75 (69,82)
CPMM Method Modeled as AR(1)
Generated as Assigned Class 1 Assigned Class 2 Assigned Class 3
True ∑ True ρ True C1 True C2 True C3 True C1 True C2 True C3 True C1 True C2 True C3
I = AR(1) = CS 0.00 93 (91,95) 6 (4,9) 0 (0,0) 7(5,9) 87 (84,90) 6 (4,9) 0 (0,0) 6 (4,9) 94 (91,96)
AR(1) 0.05 93 (90,95) 7(4,10) 0 (0,0) 7(5,10) 86 (82,88) 7(5,10) 0 (0,0) 7(5,10) 93 (90,95)
AR(1) 0.10 92 (89,95) 8 (5,11) 0 (0,0) 8 (5,11) 84 (80,87) 8 (6,11) 0 (0,0) 8 (5,11) 92 (89,94)
AR(1) 0.25 90 (84,93) 10 (6,15) 0 (0,0) 10 (7,16) 79 (73,83) 10 (7,15) 0 (0,0) 10 (6,14) 90 (85,93)
AR(1) 0.50 86 (78,91) 13 (8,20) 0 (0,0) 14 (9,22) 73 (65,78) 15 (8,24) 0 (0,0) 13 (7,20) 84 (76,92)
CS 0.05 91 (88,94) 7(5,10) 0 (0,0) 9 (6,12) 84 (81,88) 8 (6,11) 0 (0,0) 7(5,10) 92 (89,94)
CS 0.10 90 (87,93) 9 (6,12) 0 (0,0) 10 (7,13) 82 (78,85) 11 (7,15) 0 (0,0) 8 (5,12) 89 (85,93)
CS 0.25 85 (78,89) 11 (7,16) 0 (0,0) 15 (11,22) 77 (73,81) 16 (11,22) 0 (0,0) 11 (7,15) 84 (78,89)
CS 0.50 78 (69,85) 13 (8,18) 0 (0,0) 22 (15,30) 73 (68,77) 23 (16,32) 0 (0,0) 13 (8,18) 76 (68,84)
CPMM Method Modeled as CS
Generated as Assigned Class 1 Assigned Class 2 Assigned Class 3
True ∑ True ρ True C1 True C2 True C3 True C1 True C2 True C3 True C1 True C2 True C3
I = AR(1) = CS 0.00 93 (91,96) 6 (4,9) 0 (0,0) 7 (4,9) 87 (84,90) 6 (4,9) 0 (0,0) 6 (4,9) 94 (91,96)
AR(1) 0.05 93 (90,95) 7(5,10) 0 (0,0) 7 (5,10) 85 (82,88) 7(5,10) 0 (0,0) 7(5,10) 93 (90,95)
AR(1) 0.10 92 (89,95) 8 (5,11) 0 (0,0) 8 (5,11) 83 (79,87) 8 (6,11) 0 (0,0) 8 (5,11) 92 (89,94)
AR(1) 0.25 87 (71,94) 18 (9,44) 0 (0,1) 12 (3,29) 60 (8,79) 11 (2,26) 0 (0,1) 18 (9,42) 88 (72,95)
AR(1) 0.50 57 (4,73) 27 (11,63) 14 (2,30) 32 (1,56) 22 (10,39) 32 (2,57) 15 (2,31) 28 (11,63) 57 (4,72)
CS 0.05 92 (88,94) 8 (5,11) 0 (0,0) 8 (6,12) 84 (80,88) 8 (5,11) 0 (0,0) 8 (5,11) 92 (89,95)
CS 0.10 92 (88,94) 10 (6,14) 0 (0,0) 8 (6,12) 81 (76,85) 10 (6,14) 0 (0,0) 9 (5,13) 90 (86,94)
CS 0.25 87 (81,92) 12 (7,17) 0 (0,0) 13 (8,19) 75 (69,80) 13 (8,18) 0 (0,0) 12 (8,18) 87 (82,92)
CS 0.50 85 (76,91) 12 (7,19) 0 (0,0) 15 (9,24) 73 (66,78) 14 (8,22) 0 (0,0) 13 (7,20) 86 (78,92)

(1) The class output of CPMM and LCTA was aligned to the true classes in the simulation model based on estimated trajectory parameters.

(2) I = Independent; AR(1) = auto-regressive lag 1; CS = compound symmetric; LCTA method always modeled as Independent; C# = Class #.