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Table 2 Set up of simulation studies

From: Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals

 

\(\varvec{\alpha }_{\varvec{1}}\)

\(\varvec{\alpha }_{\varvec{2}}\)

\(\varvec{\alpha }_{\varvec{3}}\)

\(\varvec{\alpha }_{\varvec{4}}\)

Model 1 (LME with a linear time trend)

  Scenario 1 (low correlation)

1

1

0

0

  Scenario 2 (high correlation)

0

0

1

1

  Scenario 3 (both groups)

1

1

1

1

  Scenario 4 (null case)

0

0

0

0

  Scenario 5a (complete null case)

0

0

0

0

Model 2 (LME with a quadratic term for time)

  Scenario 1 (low correlation)

1

1

0

0

  Scenario 2 (high correlation)

0

0

1

1

  Scenario 3 (both groups)

0.5

0.5

0.5

0.5

  Scenario 4 (null case)

0

0

0

0

  Scenario 5a (complete null case)

0

0

0

0

Model 3 (LME with a 3-knot spline function for time)

  Scenario 1 (low correlation)

1

1

0

0

  Scenario 2 (high correlation)

0

0

1

1

  Scenario 3 (both groups)

1

1

1

1

  Scenario 4 (null case)

0

0

0

0

  Scenario 5a (complete null case)

0

0

0

0

  1. aScenario 5 is an additional scenario based on Scenario 4 where we did not censor biomarker trajectories by death times. Details about this scenario were explained in the Results section