rate_eff_cov_table#

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The Rate Effect Covariate Table#

Improvements#

This table is a quick fix for the problem below. It uses the existing weight_table to represent an arbitrary bilinear function. A better fix would separate the covariate grid from the covariate values so that:

  1. The grid does not need to be repeated when only values change.

  2. weights are not used for purposes other than weighting.

Problem#

The data table only assigns one value for each covariate to each data point; see Covariates in the data table. This works fine for most cases. The problem is when a covariate multiplier affects a rate_value , and the covariate values changes with age or time , and the measurement is for an ode integrand.

Solution#

This table attaches a bilinear function of age and time to each covariate, node, and splitting_covariate value. The covariate values in this table are used, to compute covariate multiplier effects, when all the following conditions hold:

  1. The covariate appears in this table .

  2. The multiplier type is rate_value .

If the conditions above do not hold, the data table in the data and avgint tables are used to compute covariate multiplier effects.

Optional#

This table is optional. The case where there is not rate_eff_cov table in the database is the same as when the table has not entries.

rate_eff_cov_id#

This column has type integer and is the primary key for the rate_eff_cov table. Its initial value is zero, and it increments by one for each row.

covariate_id#

This column has type integer and is the covariate_id corresponding to this row. The covariate_id corresponding to the splitting covariate cannot appear in this column; i.e., the splitting covariate value is constant with respect to age and time.

node_id#

This column has type integer and is the node_id corresponding to this row.

split_value#

This column has type real and is the value of the splitting corresponding to this row. This value is not used when the splitting covariate is null .

weight_id#

This column has type integer and is the weight_id corresponding to this row. As a quick fix, we use weight_id to identify an arbitrary bilinear functions of age and time.

Rectangular Grid#

For each covariate_id that appears in this table, each node_id in the node table, each splitting covariate value in the data table, and each splitting covariate value in the avgint table, there must be one an only one row with that covariate_id, node_id, and splitting covariate value.

Example#

The file rate_eff_cov_table.py contains an example rate_eff_cov table.