Table 2.

Parameter estimation results: Estimated mean bias (standard deviation) of trajectory parameter estimators from 3-class model of 500 randomly generated datasets with 3 classes and 100 subjects per class by estimation method, assigned class, and correlation structure and level assumed when generating the data.

LCTA Method
		Class 1		Class 2		Class 3
True ∑	True ρ	Intercept	Slope	Intercept	Slope	Intercept	Slope
I = AR(1) = CS	0.00	0.000 (0.082)	−0.002 (0.034)	−0.000 (0.094)	−0.007 (0.040)	0.001 (0.082)	−0.048 (0.035)
AR(1)	0.05	−0.017 (0.086)	0.002 (0.038)	0.006 (0.092)	−0.006 (0.041)	0.009 (0.088)	−0.046 (0.037)
AR(1)	0.10	−0.013 (0.095)	−0.003 (0.037)	−0.000 (0.101)	−0.003 (0.042)	0.024 (0.093)	−0.051 (0.039)
AR(1)	0.25	−0.057 (0.108)	0.001 (0.044)	0.007 (0.112)	−0.005 (0.051)	0.058 (0.104)	−0.047 (0.039)
AR(1)	0.50	−0.174 (0.129)	0.009 (0.043)	0.004 (0.118)	−0.002 (0.057)	0.178 (0.125)	−0.054 (0.044)
CS	0.05	−0.028 (0.095)	0.002 (0.035)	−0.003 (0.093)	−0.003 (0.041)	0.030 (0.085)	−0.049 (0.035)
CS	0.10	−0.061 (0.091)	0.007 (0.034)	0.002 (0.093)	−0.001 (0.044)	0.066 (0.089)	−0.054 (0.032)
CS	0.25	−0.170 (0.107)	0.021 (0.035)	0.000 (0.100)	−0.005 (0.049)	0.161 (0.111)	−0.065 (0.033)
CS	0.50	−0.316 (0.135)	0.038 (0.031)	0.013 (0.110)	0.001 (0.049)	0.344 (0.136)	−0.085 (0.029)
CPMM Method Modeled as AR(1)
		Class 1		Class 2		Class 3
True ∑	True ρ	Intercept	Slope	Intercept	Slope	Intercept	Slope
I = AR(1) = CS	0.00	0.000 (0.082)	−0.002 (0.034)	−0.000 (0.094)	−0.007 (0.040)	0.001 (0.082)	−0.048 (0.035)
AR(1)	0.05	0.006 (0.083)	−0.003 (0.034)	−0.003 (0.098)	−0.001 (0.042)	0.002 (0.088)	−0.048 (0.036)
AR(1)	0.10	0.002 (0.095)	−0.003 (0.037)	0.000 (0.104)	−0.003 (0.042)	0.009 (0.093)	−0.051 (0.039)
AR(1)	0.25	−0.007 (0.112)	0.003 (0.053)	0.008 (0.130)	−0.005 (0.062)	0.008 (0.104)	−0.048 (0.041)
AR(1)	0.50	−0.035 (0.213)	0.007 (0.110)	0.008 (0.174)	−0.001 (0.102)	0.027 (0.167)	−0.047 (0.063)
CS	0.05	−0.024 (0.095)	0.003 (0.035)	−0.003 (0.093)	−0.003 (0.041)	0.026 (0.086)	−0.049 (0.035)
CS	0.10	−0.054 (0.091)	0.007 (0.034)	0.002 (0.094)	−0.001 (0.044)	0.058 (0.089)	−0.054 (0.032)
CS	0.25	−0.153 (0.108)	0.020 (0.035)	−0.001 (0.103)	−0.006 (0.051)	0.143 (0.111)	−0.064 (0.033)
CS	0.50	−0.294 (0.151)	0.032 (0.033)	0.006 (0.126)	−0.002 (0.059)	0.311 (0.151)	−0.081 (0.030)
CPMM Method Modeled as CS
		Class 1		Class 2		Class 3
True ∑	True ρ	Intercept	Slope	Intercept	Slope	Intercept	Slope
I = AR(1) = CS	0.00	0.001 (0.083)	−0.002 (0.034)	−0.000 (0.094)	−0.007 (0.041)	0.000 (0.084)	−0.048 (0.035)
AR(1)	0.05	0.016 (0.089)	−0.005 (0.037)	−0.002 (0.105)	−0.002 (0.057)	−0.006 (0.094)	−0.048 (0.057)
AR(1)	0.10	0.025 (0.107)	−0.005 (0.052)	0.004 (0.122)	−0.004 (0.097)	−0.013 (0.109)	−0.051 (0.081)
AR(1)	0.25	0.116 (0.210)	0.092 (0.262)	0.002 (0.194)	−0.014 (0.365)	−0.110 (0.214)	−0.131 (0.244)
AR(1)	0.50	0.421 (0.166)	0.299 (0.425)	0.006 (0.131)	−0.007 (0.453)	−0.423 (0.163)	−0.345 (0.415)
CS	0.05	−0.002 (0.113)	0.002 (0.088)	−0.004 (0.103)	−0.003 (0.045)	0.005 (0.102)	−0.048 (0.078)
CS	0.10	−0.001 (0.098)	0.000 (0.036)	0.004 (0.114)	−0.001 (0.054)	0.008 (0.103)	−0.048 (0.070)
CS	0.25	−0.011 (0.115)	−0.001 (0.048)	−0.002 (0.153)	−0.004 (0.100)	0.001 (0.122)	−0.044 (0.046)
CS	0.50	−0.016 (0.139)	−0.007 (0.051)	−0.002 (0.174)	−0.010 (0.088)	0.020 (0.134)	−0.046 (0.042)

(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.