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. Author manuscript; available in PMC: 2011 Apr 14.
Published in final edited form as: Biometrics. 2009 Jul 23;66(2):558–566. doi: 10.1111/j.1541-0420.2009.01290.x

Table 1.

Monte carlo empirical rejection rates in simulations study

True dropout model Sample size
Mechanism % dropout (α0, δ0) n=25 n=50 n=100 n=250
Nominal test level Nominal test level Nominal test level Nominal test level
.01 .05 .10 .01 .05 .10 .01 .05 .10 .01 .05 .10
Random 20 (2.55, 0) .010 .080 .155 .006 .081 .158 .009 .072 .129 .007 .064 .124
dropouts 40 (1.67, 0) .013 .065 .143 .014 .073 .148 .017 .060 .127 .013 .055 .119
60 (1.02, 0) .004 .067 .150 .001 .043 .112 .004 .050 .118 .012 .057 .114
Non-random 20 (1.34, 3) .014 .071 .152 .014 .073 .150 .011 .063 .138 .014 .063 .118
dropouts 40 (.34, 3) .012 .098 .178 .006 .054 .133 .006 .067 .131 .010 .074 .122
60 (−.52, 3) .007 .059 .130 .008 .037 .099 .012 .064 .127 .015 .058 .125
Monte carlo s.e. .003 .007 .009 .003 .007 .009 .003 .007 .009 .003 .007 .009