Table 4.

Summary ^* of Performance of Three Estimators: 1) Likelihood Estimator assuming Geometric Model; 2) Likelihood Estimator assuming Poisson Model; 3) Likelihood Estimator assuming Geometric Model that drops out intercepts as well as subjects with only one measurement. Censoring process was assumed to be informative and dependent on only individual intercepts (1), non-informative (2), and informative and dependent on only individual slopes (3).

	Estimator
Simulation Parameters	Performance indicator×10	1) Geometric	2) Poisson	3) Geometric that drops out intercepts and subjects with only one measurement
1) γ2 = 0 γ1 = −3.115	Bias_α	−0.082	−0.083	---
	Bias_β	0.038	0.070	0.083
	MSE(a)_α	0.098	0.100	---
	MSE(a)_β	0.076	0.095	0.098
	MSE(b)_α	0.097	0.129	---
	MSE(b)_β	0.046	0.048	0.062
2) γ2 = 0 γ1 = 0	Bias_α	−0.078	−0.080	---
	Bias_β	0.033	0.064	0.068
	MSE(a)_α	0.084	0.096	---
	MSE(a)_β	0.071	0.092	0.095
	MSE(b)_α	0.093	0.118	---
	MSE(b)_β	0.041	0.047	0.084
3) γ2 = −5.2 γ1 = 0	Bias_α	−0.071	−0.083	---
	Bias_β	0.042	0.073	0.077
	MSE(a)_α	0.086	0.099	---
	MSE(a)_β	0.064	0.079	0.084
	MSE(b)_α	0.094	0.110	---
	MSE(b)_β	0.036	0.040	0.083

^*Datasets simulated from an underlying Geometric model with α=3.542 β=−0.131 γ₀ = 5.988, ${\sigma }_{\alpha }^2=0.070,{\sigma }_{\beta }^2=0.033$, σ_αβ= −0.029 and ${\sigma }_{\epsilon }^2=0.12$, with 2000 simulations.