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This meant that the model could be developed on one sample (the estimation sample), and the results validated using two further samples.
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Because our data are based on rather small samples, the estimation procedure that we adopt accounts for the Bartlett correction following Johansen (2000).
For the sample size the estimation GRANMO 5.1 was used.
Using this sample size, the estimation of a 14% prevalence of AAD would have a 95% confidence interval of 11.6-16.4.
We demonstrate that clustered sampling decreases the estimation precision when individuals form clusters, while sampling designs do not affect the estimation accuracy when individuals are distributed randomly.
where M min is denoted as the minimum required number of the beaconing result samples for the estimation.
Each MCMC chain ran for 75000 steps of which the last 25000 contributed with 250 samples for the estimation.
Whatever the sampling size, the estimation was relatively good when at least 12 markers were used.
Additionally, with the increasing sample size, the estimations are more and more accurate.
Moreover, in the preliminary work that allowed us to formulate the hypothesis for the sample size estimation, the 90-day functional outcome was evaluated using the GOS [4].
The sample size for the estimation was good (ratio of n / estimated parameters = 14).
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