Microdata Output

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Microdata output allows a model to output records containing the values of selected entity attributes during a run for later use. This topic describes microdata output from a model developer perspective.

Related topics

• Model Code
• Event Trace

Topic contents

• Introduction
• Topic outline
• Quick start How to build and run a model with microdata output
• Worked example 1a Entity life cycle
• Worked example 1b Entity life cycle with event context
• Worked example 1c Entity life cycle with event filtering
• Worked example 2a Output using a hook to a model event
• Worked example 2b Output using a hook to a self-scheduling attribute
• Worked example 2c Output by calling write_microdata in model code
• Worked example 3 Database output in a time-based model
• Worked example 4 Database output in a complex case-based model
• Microdata output modes Text mode and database mode
• Enabling microdata output
• Run-time settings Run-time settings
• Build-time settings Build-time settings
• Writing microdata from model code Controlling microdata output from model code
• The microdata key The purpose of the microdata key and how to set it

Introduction

A model built with microdata output capability can output records containing the values of entity attributes. As well as attribute values, each microdata output record contains a microdata key to match corresponding records between runs.

By default, a model does not have microdata output capability. See Enabling microdata output or Quick start on how to build a model with microdata output capability.

Two microdata output modes are supported: text mode and database mode. Text mode is targeted more to model developers, while database mode is targeted more to users of production models and to future OpenM++ run-time tabulation functionality. Both modes can be active in the same run.

Text mode writes microdata to text files in csv format. Text mode can filter output at run using event context. Text mode output can include an additional column showing the event context of the record.

Database mode writes microdata to the model database, from which it can be extracted using dbcopy or an API. Database mode will be used for future OpenM++ run-time tabulation functionality, including microdata comparisons between runs.

Microdata output is controlled by run-time settings, build-time settings, and model code.

Run-time settings specify which attributes are output during a run, provided the model was built with microdata output capability. All attributes are available for selection at run-time without rebuilding the model. Some run-time settings apply only to text mode. Those text mode settings can filter records by event context and can create an additional column showing the event context for each record.

Build-time settings are statements in model code which make the model capable of microdata output and control related warning messages. Build-time settings can also (optionally) determine when microdata output is written in the entity life cycle: on entrance, on exit, or on the occurrence of an event.

Model code can write microdata explicitly by calling the supplied entity member function write_microdata. The write_microdata function can be hooked to an existing entity function such as the implementation function of an event.

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Topic outline

Quick Start shows how to build a model capable of microdata output and how to activate that capability in a model run.

The quick start is followed by several worked examples with illustrative inputs and outputs, mostly using the RiskPaths model.

The first group of examples
entity life cycle,
entity life cycle with event context, and entity life cycle with event filtering
illustrate how to probe the life cycle of entities using microdata text mode.

A second group of examples
output using a hook to a model event,
output using a hook to a self-scheduling attribute, and
output by calling write_microdata in model code
illustrate how to control when microdata output occurs using model code.

The next example illustrates database output in a time-based model. The example outputs microdata for all entities in the IDMM model at the end of the run. A Base run and a Variant are performed, and the results compared at the microdata level using files exported by sbcopy.

The final example illustrates database output in a case-based model. The example outputs microdata for all entities in the complex case-based model OncoSim. A Base run and a Variant are performed, and summary microdata indicators (years lived and health system cost) are output for each Person at the end of each case. The results are exported by dbcopy and analyzed to identify all cases which differed due to the parameter change in the Variant run, and by how much.

The worked examples are followed by subtopics which explore specifics in more detail:

• Microdata output modes
• Enabling microdata output
• Run-time settings
• Build-time settings
• Writing microdata from model code
• The microdata key

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Quick start

This subtopic contains the following sections.

• 1. Build model with microdata output capability
• 2. Create model ini file with microdata output options
• 3. Run model using microdata output

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1. Build model with microdata output capability

Add the following statements to the model source code file RiskPaths/code/ompp_framework.ompp:

options microdata_output = on;
options microdata_write_on_exit = on;

Build the Release version of RiskPaths.
In Windows, the model executable will be RiskPaths/ompp/bin/RiskPaths.exe.
In Linux, the model executable will be RiskPaths/ompp-linux/bin/RiskPaths.

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2. Modify model ini file with microdata output options

In the same folder as the RiskPaths executable there may already be a copy of the default model ini file RiskPaths.ini. If not create it using your IDE or a text editor such as Notepad.

Edit RiskPaths.ini to have the following content:

[Parameter]
SimulationCases = 5

[Microdata]
ToCsv = yes
Person = age, union_status, parity_status

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3. Run model using microdata output

Launch the model in its bin directory using the ini file created in the previous step.

RiskPaths -ini RiskPaths.ini

In Windows you can run the Release version of RiskPaths from inside Visual Studio as follows:

• Solution Configurations to Release and Solution Platforms to x64
• Project Properties > Configuration Properties > Debugging > Command Arguments to
  -ini RiskPaths.ini
• Project Properties > Configuration Properties > Debugging > Working Directory to $(TargetDir)
• To launch the model, do Debug > Start without debugging or Press Ctrl-F5.

When the model run completes, the file RiskPaths.Person.microdata.csv should be present in the model bin directory and look like this:

key,age,union_status,parity_status
1,100,2,1
2,100,2,1
3,100,2,1
4,100,0,1
5,100,0,1

or formatted as a table, like this:

key	age	union_status	parity_status
1	100	2	1
2	100	2	1
3	100	2	1
4	100	0	1
5	100	0	1

The run-time settings output the attributes age, union_status, and parity_status. The leading column key can be used to match microdata records between runs. The build-time option microdata_write_on_exit causes a microdata record to be written whenever an entity leaves the simulation. In RiskPaths there is no mortality and Person entities exit the simulation at age 100. The values of union_status and parity_status are those at that age, for each Person entity in the run.

The model log contains the following warning, which is expected.

Warning : model can expose microdata at run-time with output_microdata = on

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Worked example 1a

This example is the first of three which probe entity life cycle using microdata output in text mode. It continues the quick start example to output multiple microdata records for a single entity: when it enters the simulation, at each event, and when it leaves the simulation.

In ompp_framework.ompp, change the build-time microdata settings to

options microdata_output = on;

options microdata_write_on_enter = on;
options microdata_write_on_exit = on;
options microdata_write_on_event = on;

Change the run-time settings in RiskPaths.ini to consist of only one case

[Parameter]
SimulationCases = 1

[Microdata]
ToCsv = yes
Person = age, union_status, parity_status

and run the model.

Here's the resulting microdata output in RiskPaths.Person.microdata.csv, with some rows elided.

key	age	union_status	parity_status
1	0	0	0
1	1	0	0
1	2	0	0
1	3	0	0
...	...	...	...
1	22.5	0	0
1	23	0	0
1	24	0	0
1	24.2609992115357	1	0
1	25	1	0
1	25.2609992115357	1	0
1	26	1	0
1	26.5378127283906	1	1
1	26.5378127283906	1	1
1	27	1	1
1	27.2609992115357	1	1
1	27.2609992115357	2	1
1	27.5	2	1
1	28	2	1
1	29	2	1
1	29.2609992115357	2	1
1	30	2	1
...	...	...	...
1	99	2	1
1	100	2	1
1	100	2	1
1	100	2	1

The microdata output shows the values of the attributes at every event in the life cycle. Multiple microdata records can occur at the same age due to multiple tied events at that age.

Worked example 1b

This example is the second of three which probe entity life cycle using microdata output in text mode. It continues the previous example, adding event context information to each microdata record.

Leave the build-time microdata settings in ompp_framework.ompp unchanged from the previous example:

options microdata_output = on;

options microdata_write_on_enter = on;
options microdata_write_on_exit = on;
options microdata_write_on_event = on;

Activate the CsvEventColumn option by modifying the run-time settings in RiskPaths.ini so that it looks like this:

[Parameter]
SimulationCases = 1

[Microdata]
ToCsv = yes
CsvEventColumn = true
Person = age, union_status, parity_status

Run the model.

Here's the resulting microdata output in RiskPaths.Person.microdata.csv, with some rows elided.

key	event	age	union_status	parity_status
1	(no event)	0	0	0
1	om_ss_event	1	0	0
1	om_ss_event	2	0	0
1	om_ss_event	3	0	0
...	...	...	...	...
1	om_ss_event	22.5	0	0
1	om_ss_event	23	0	0
1	om_ss_event	24	0	0
1	Union1FormationEvent	24.2609992115357	1	0
1	om_ss_event	25	1	0
1	om_ss_event	25.2609992115357	1	0
1	om_ss_event	26	1	0
1	FirstPregEvent	26.5378127283906	1	1
1	om_ss_event	26.5378127283906	1	1
1	om_ss_event	27	1	1
1	om_ss_event	27.2609992115357	1	1
1	UnionPeriod2Event	27.2609992115357	2	1
1	om_ss_event	27.5	2	1
1	om_ss_event	28	2	1
1	om_ss_event	29	2	1
1	om_ss_event	29.2609992115357	2	1
1	om_ss_event	30	2	1
...	...	...	...	...
1	om_ss_event	99	2	1
1	om_ss_event	100	2	1
1	DeathEvent	100	2	1
1	DeathEvent	100	2	1

The microdata output now contains an event column showing the name of the event being implemented when each microdata record was output. There is no event at the beginning of a case in a case-based model like RiskPaths, so when the first entity in the case enters the simulation (no event) is shown in the event column. If the event associated with microdata output is a self-scheduling event, om_ss_event is shown in the event column. The internal self-scheduling event for an entity implements all self-scheduling attributes in the entity. Note that Event Trace can be used to obtain more information about events, including the names of self-scheduling events.

The final three microdata output records all occur at age 100. Here's a detailed explanation of each of these apparent duplicate records:

The first is from the self-scheduling event which maintains the derived attribute self_scheduling_int(age). That derived attribute is in turn used in the declaration of the identity attribute integer_age:

actor Person 					//EN Individual
{
    //EN Current integer age
    LIFE integer_age = COERCE( LIFE, self_scheduling_int(age) );
...

The second is from the event DeathEvent which is triggered by model logic and the ProbMort parameter immediately when integer_age is 100:

TIME Person::timeDeathEvent()				
{
    TIME event_time = TIME_INFINITE;
    if (CanDie)										
    {
        if (ProbMort[integer_age] >= 1) 			
        {
            event_time = WAIT(0);
        }
...

The third occurs when the entity leaves the simulation, because the option microdata_write_on_exit is on in the example. The event DeathEvent was the active event when the entity left the simulation, so that's what's shown in the event column.

Although it's not illustrated in this example, the name in the event column can be prefixed by a *. This indicates that the active event is in a different entity than the one being output. This can occur in a time-based model or in a case-based model with multiple entities in a case. For example a ChildBirth event in a Person entity could cause a new Person entity to enter the simulation and generate a microdata output record. The microdata record for the newborn would contain *ChildBirth in the event column to indicate that the active event was in a different entity than the microdata record.

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Worked example 1c

This example is the third of three which probe entity life cycle using microdata output in text mode. It extends the previous example by filtering on specific events.

Leave the build-time microdata settings in ompp_framework.ompp unchanged from the previous example:

options microdata_output = on;

options microdata_write_on_enter = on;
options microdata_write_on_exit = on;
options microdata_write_on_event = on;

Modify the run-time settings in RiskPaths.ini to increase the number of cases to 5000, and restrict output to two named events using the Events option:

[Parameter]
SimulationCases = 5000

[Microdata]
ToCsv = yes
CsvEventColumn = true
Person = age, union_status, parity_status
Events = Union1FormationEvent, FirstPregEvent

Run the model.

The resulting microdata output RiskPaths.Person.microdata.csv has 8,128 records and looks like this:

key	event	age	union_status	parity_status
1	Union1FormationEvent	24.2609992115357	1	0
1	FirstPregEvent	26.5378127283906	1	1
2	Union1FormationEvent	22.0523726276488	1	0
2	FirstPregEvent	24.6780778011483	1	1
3	Union1FormationEvent	17.050111243303	1	0
3	FirstPregEvent	20.024664717724	1	1
4	FirstPregEvent	17.4107170399441	0	1
5	FirstPregEvent	24.1577392012077	0	1
6	Union1FormationEvent	22.502915072767	1	0
6	FirstPregEvent	24.7534475294375	1	1
...	...	...	...	...

This csv file can be used to perform multivariate statistical analysis. For example, the csv file can be opened in Excel, filtered to just FirstPregEvent and a histogram generated to visualize the first birth distribution by age:

The data could be additionally filtered in Excel using the union_status column to visualize how union status affects the age distribution to produce the overall pattern.

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Worked example 2a

This example is the first of three illustrating control of microdata output at build time using model code. It outputs microdata whenever a specific event occurs using a hook in model code, specifically whenever FirstPregEvent occurs in RiskPaths.

In RiskPaths, prepare the event implement function for hooks by adding the required statement at the end of the event implement function FirstPregEvent:

void Person::FirstPregEvent()
{
    parity_status = PS_PREGNANT;
    hook_FirstPregEvent();
}

Next, add code to hook the built-in function write_microdata to FirstPregEvent:

actor Person {
    hook write_microdata, FirstPregEvent;
};

In ompp_framework.ompp, turn off options which automatically write microdata, which were previously turned on in example 1.

//options microdata_write_on_enter = on;
//options microdata_write_on_exit = on;
//options microdata_write_on_event = on;

The statements inserted in example 1 were commented to revert to the default value off. This means that only explicit calls to write_microdata will generate microdata output.

Set the number of cases to 20 in RiskPaths.ini:

[Parameter]
SimulationCases = 20

[Microdata]
ToCsv = yes
Person = age, union_status, parity_status

Run the model.

The microdata output file RiskPaths.Person.microdata.csv should look like this:

key	age	union_status	parity_status
1	26.5378127283906	1	1
2	24.6780778011483	1	1
3	20.024664717724	1	1
4	17.4107170399441	0	1
5	24.1577392012077	0	1
6	24.7534475294375	1	1
7	18.2797585879836	1	1
8	22.110326319997	1	1
9	21.2430736420085	1	1
10	29.168835553187	1	1
12	37.7955780112222	2	1
14	26.9550960057145	1	1
15	21.6012847802494	0	1
16	20.3178392448776	1	1
18	22.8298415328563	1	1
19	26.7999269606788	1	1
20	19.0257883348614	1	1

The microdata file shows the values of attributes at all occurrences of the FirstPregEvent in the run. It could, for example, be used to chart the distribution of age at first birth using a downstream application like Excel or R, similar to example 1c.

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Worked example 2b

This example is the second of three illustrating control of microdata output at build time using model code. It outputs microdata records giving a snapshot of an entity at each integer age, using a hook to a self-scheduling attribute.

Change the hook in the previous example to

actor Person {
    hook write_microdata, self_scheduling_int(age);
};

and simulate a single case by modifying RiskPaths.ini:

[Parameter]
SimulationCases = 1

[Microdata]
ToCsv = yes
Person = age, union_status, parity_status

Run the model. Microdata output should look like this:

key	age	union_status	parity_status
1	1	0	0
1	2	0	0
1	3	0	0
1	4	0	0
...	...	...	...
1	26	1	0
1	27	1	1
1	28	2	1
1	29	2	1
...	...	...	...
1	100	2	1

The microdata output contains a snapshot of the attributes at each integer age.

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Worked example 2c

This example is the third of three illustrating control of microdata output at build time using model code. It outputs microdata directly by calling the entity function write_microdata explicitly in model code.

Remove any changes to RiskPaths model code made in previous examples.

In ompp_framework.ompp, insert the single statement

options microdata_output = on;

Insert a call to write_microdata in the implementation function of the FirstPreg event in the module Fertility.mpp:

void Person::FirstPregEvent()
{
    parity_status = PS_PREGNANT;
    write_microdata();
}

Set the run-time settings in RiskPaths.ini as follows:

[Parameter]
SimulationCases = 5

[Microdata]
ToCsv = yes
Person = age, union_status, parity_status

Run the model.

Output should look as follows:

key,age,union_status,parity_status
1,26.5378127283906,1,1
2,24.6780778011483,1,1
3,20.024664717724,1,1
4,17.4107170399441,0,1
5,24.1577392012077,0,1

This example could be accomplished without using a direct call to write_microdata. In a more complex model, a call to write_microdata could be placed inside conditional model logic, for example to output microdata when a rare causative path is taken in model logic, to probe correctness.

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Worked example 3

This example outputs microdata in database mode for the time-based model IDMM. Two runs Base and Variant are performed with an incremental parameter change. Microdata with infection status is output for all Host entities at the end of the run. Each run consists of multiple replicates. The dbcopy utility is used to extract the microdata for the two runs. Excel is used to import the microdata and construct a table showing the concordance of disease state at the microdata level between the Base and Variant runs.

Modify the IDMM model to activate microdata output when entities leave the simulation by adding the following statements to ompp_framework.ompp:

options microdata_output = on;
options microdata_write_on_exit = on;

Rebuild the model.

Arrange that IDMM uses the file IDMM.ini to get run-time settings (see quick start), and set the contents of IDMM.ini to create a run named Base as follows:

[OpenM]
SubValues = 5
Threads = 5
RunName = Base

[Parameter]
NumberOfHosts = 10000
ImmunePhaseDuration = 20.0

[Microdata]
ToDb = yes
Host = disease_phase

These settings create a Base run with 5 replicates, each with a population of 10,000 Host entities.

Run the model.

The log file should contain a line like

2023-01-13 17:01:04.874 Warning : model can expose microdata at run-time with output_microdata = on

which indicates that the version of IDMM is capable of writing microdata. It should also contain a line similar to

2023-01-13 17:01:08.295 Writing microdata into database, run: 103

which indicates that the model is merging microdata from replicates into the database when the run completes.

Change the file IDMM.ini, modifying RunName and ImmunePhaseDuration for a second run named Variant:

[OpenM]
SubValues = 5
Threads = 5
RunName = Variant

[Parameter]
NumberOfHosts = 10000
ImmunePhaseDuration = 22.0

[Microdata]
ToDb = yes
Host = disease_phase

The Variant run is the same as the Base run, except for a 10% increase in the duration of protective immunity from a previous infection.

Run the model.

The model database now contains results for the two runs Base and Variant.

Open a command shell. Change the current directory to the ompp/bin directory of the IDMM model. Run dbcopy to extract the microdata results from the model database to csv files using the command

dbcopy -dbcopy.To csv -dbcopy.ModelName IDMM

Console output should be similar to the following:

C:\Development\X\ompp\models\IDMM\ompp\bin>%OM_ROOT%\bin\dbcopy -dbcopy.To csv -dbcopy.ModelName IDMM
2023-01-13 17:01:45.580 Model IDMM
2023-01-13 17:01:45.599 Model run 102 Base
2023-01-13 17:01:45.600   Parameters: 13
2023-01-13 17:01:45.609   Tables: 3
2023-01-13 17:01:45.622   Microdata: 1
2023-01-13 17:01:45.688 Model run 103 Variant
2023-01-13 17:01:45.690   Parameters: 13
2023-01-13 17:01:45.700   Tables: 3
2023-01-13 17:01:45.712   Microdata: 1
2023-01-13 17:01:45.781 Workset 101 Default
2023-01-13 17:01:45.782   Parameters: 13
2023-01-13 17:01:45.798 Done.

The console output above was done on Windows. There would be minor cosmetic differences in Linux. Note the use of the global environment variable OM_ROOT to ensure that the version of dbcopy matches the version of OpenM++ used to build the model.

The dbcopy log output shows the extraction of the microdata for the two runs Base and Variant.

dbcopy creates a folder IDMM The folder structure of dbcopy output looks like

C:\OMPP\MODELS\IDMM\OMPP\BIN\IDMM
├───run.Base
│   ├───microdata
│   ├───output-tables
│   └───parameters
├───run.Variant
│   ├───microdata
│   ├───output-tables
│   └───parameters
└───set.Default

Each microdata sub-folder contains a file named Host.csv containing the microdata of Host entities for the run. Had microdata for the Ticker actor been requested in the run, a file Ticker.csv would also be present. The first few records of IDMM/run.Base/microdata/Host.csv look like this:

key	disease_phase
10	DP_SUSCEPTIBLE
11	DP_LATENT
12	DP_IMMUNE
13	DP_LATENT
14	DP_IMMUNE
15	DP_LATENT
16	DP_SUSCEPTIBLE
17	DP_LATENT
18	DP_SUSCEPTIBLE
19	DP_IMMUNE
20	DP_IMMUNE

For large output files, one can use the dbcopy option -dbcopy.IdCsv to output numeric id's instead of alphanumeric codes.

The default microdata key entity_id is used in this example. entity_id is unique for all entities in a run, and will correspond to the same entity in two IDMM runs provided the runs have the same number of entities per replicate and the same number of replicates.

The two files run.Base/microdata/Host.csv and run.Variant/microdata/Host.csv were imported to Excel, and the 50,000 rows matched one-to-one. Below is an Excel PivotTable (aka cross-tab) which shows the 50,000 Host entities at the end of the runs, classified by the disease status in the Base run (rows) and the disease status in the Variant run (columns).

Base↓/Variant→	DP_IMMUNE	DP_INFECTIOUS	DP_LATENT	DP_SUSCEPTIBLE	All
DP_IMMUNE	24284	806	390	2649	28129
DP_INFECTIOUS	1849	137	92	334	2412
DP_LATENT	2268	94	67	354	2783
DP_SUSCEPTIBLE	13932	421	352	1971	16676
All	42333	1458	901	5308	50000

The lexicographic ordering of disease phase in this Excel PivotTable does not follow the ordering in model code, which makes the table harder to interpret. The intuitive order is susceptible, latent, infectious, immune. That could be addressed by revising classification codes in IDMM model code to align lexicographic order with model code order, e.g.

classification DISEASE_PHASE	//EN Disease phase
{
    //EN Susceptible
    DP0_SUSCEPTIBLE,

    //EN Latent
    DP1_LATENT,

    //EN Infectious
    DP2_INFECTIOUS,

    //EN Immune
    DP3_IMMUNE
};

Alternatively, the microdata could have been exported using the option -dbCopy.IdCsv, which would have displayed 0,1,2,3. However, numeric id's are not particularly intuitive either.

The level of coherence between the Base and Variant runs is not high in this example. Perhaps this is because a 10% increase in the duration of immunity is not as minor as one might think a priori. The IDMM model simulates a highly interacting population which can diverge rapidly from a small initial perturbation. That divergence can be even faster because IDMM does not use entity-specific random number generators for decoherence control.

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Worked example 4

This example illustrates run comparison at the microdata level using a large scale complex case-based model (a working version of the Statistics Canada OncoSimX model). This example is divided into the following sections:

Example 4 sections

• Summary
• Build steps
• Run steps
• Microdata extraction
• Downstream analysis

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Summary

The default microdata key entity_id is not suitable for run comparison in OncoSimX, so a model-specific definition of get_microdata_key was added to model code. A pair of attributes (years lived and health system cost) were output for each Person entity at the end of each case. A Base run with 500,000 cases and 12 replicates was performed with microdata output enabled, in database mode. A Variant run was performed, changing a single scalar parameter. Results for both runs were exported using dbcopy to csv files and analyzed in Excel to identify all cases which differed between Base and Variant runs for either of the two attributes.

The mechanical steps in this example are similar to those in the previous example.

[back to example 4 sections]
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Build steps

The model code was modified to enable microdata output when the Person in each case exits the simulation by adding the following statements to model code.

options microdata_output = on;
options microdata_write_on_exit = on;

In OncoSimX a case contains exactly one Person entity, but might contain other entities depending on the simulation, such as one or more Tumour entities. Because the built-in attribute entity_id is incremented whenever a new entity is created, entity_id is unsuitable as a microdata key to match corresponding Person entities between two OncoSimX runs. However, the built-in attribute case_id is suitable as a microdata key for Person because it has a one-to-one relationship with the single Person entity in each case, and this relationship is robust across runs provided the runs have the same number of cases and replicates. A function definition of Person::get_microdata_key was added to model code so that case_id is used as the microdata key for Person entities instead of entity_id:

uint64_t Person::get_microdata_key()
{
    return case_id;
}

[back to example 4 sections]
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Run steps

The model was run using the settings file OncoSimX/ompp/bin/OncoSimX.ini, like previous examples.

The following run settings were used for the Base run:

[OpenM]
SubValues = 12
Threads = 12
RunName = Base

[Parameter]
SimulationSeed = 1
SimulationCases = 500000
MaxConsecutiveHpvTreatmentAllowed = 2

[Microdata]
ToDb = yes
Person = age, cancer_cost_all

The parameter MaxConsecutiveHpvTreatmentAllowed was chosen arbitrarily for this example. A scalar parameter rather than an array parameter was chosen to make this example simpler, because the value of a scalar parameter can be specified in a model run ini file, obviating the need to set up and use a directory for Variant parameters which differ from Base.

Because the microdata for a Person entity is output when a Person leaves the simulation at death, the attribute cancer_cost_all will contain lifetime cancer-related costs and the age attribute will contain the duration of life in years. These two attributes are measures of benefit and cost at the Person level. The case_seed attribute can be useful to probe a case of interest in a subsequent run, but there is no need to include it in the Person microdata attributes because the key column already contains the value of case_seed, as described above.

For Variant, the parameter MaxConsecutiveHpvTreatmentAllowed was changed from 2 to 1, and RunName was changed to name the run Variant:

[OpenM]
SubValues = 12
Threads = 12
RunName = Variant

[Parameter]
SimulationSeed = 1
SimulationCases = 500000
MaxConsecutiveHpvTreatmentAllowed = 1

[Microdata]
ToDb = yes
Person = age, cancer_cost_all

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Microdata extraction

After the runs completed, microdata results were extracted from the database using dbcopy as in the previous example. Here's the Windows command session:

C:\Development\X\models\OncoSimX\ompp\bin>%OM_ROOT%\bin\dbcopy -dbcopy.To csv -dbcopy.ModelName OncoSimX
2023-01-14 18:00:10.259 Model OncoSimX
2023-01-14 18:00:10.392 Model run 102 Base
2023-01-14 18:00:10.392   Parameters: 402
2023-01-14 18:00:16.227     250 of 402: IncidenceRatesHpvMultiplier
2023-01-14 18:00:18.842   Tables: 27
2023-01-14 18:00:25.153     0 of 27: CervicalCancer_TreatmentCost_Table all accumulators
2023-01-14 18:00:31.685     1 of 27: Cervical_Cancer_Cases_PAY_Table all accumulators
2023-01-14 18:00:36.055     7 of 27: Cervical_Cancer_ICER_Table_Discounted all accumulators
2023-01-14 18:00:42.357     26 of 27: Hpv_Screening_Costs_Prov_Table all accumulators
2023-01-14 18:00:43.923   Microdata: 1
2023-01-14 18:00:45.023 Model run 103 Variant
2023-01-14 18:00:45.023   Parameters: 402
2023-01-14 18:00:51.051     250 of 402: IncidenceRatesHpvMultiplier
2023-01-14 18:00:53.794   Tables: 27
2023-01-14 18:01:00.062     0 of 27: CervicalCancer_TreatmentCost_Table all accumulators
2023-01-14 18:01:06.623     1 of 27: Cervical_Cancer_Cases_PAY_Table all accumulators
2023-01-14 18:01:11.144     8 of 27: Cervical_Cancer_LifetimeCost_Table
2023-01-14 18:01:17.385     26 of 27: Hpv_Screening_Costs_Prov_Table all accumulators
2023-01-14 18:01:18.976   Microdata: 1
2023-01-14 18:01:20.115 Workset 101 Default
2023-01-14 18:01:20.116   Parameters: 402
2023-01-14 18:01:26.157     250 of 402: IncidenceRatesHpvMultiplier
2023-01-14 18:01:29.024 Done.

The first rows of microdata output for the Base run in the file OncoSimX/ompp/bin/OncoSimX/run.Base/microdata/Person.csv look like this:

key,age,cancer_cost_all
0,79.4991129115706,45100.08867191
1,67.281040126587,2229.937944223
2,87.4865314659319,1670.3732276699
3,0.379665603266858,0

The first rows of microdata for the Variant run are identical. However, some of the 500,000 microdata output records differ between Variant and Base.

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Downstream analysis

An Excel workbook was created and used to

• load the csv microdata for Base and Variant as queries, renaming columns to distinguish Base and Variant;
• merge the two queries matching on key to create a new query with one row for each case and Base and Variant microdata in distinct columns.
• add a column to the merge query to compute the Variant-Base difference in years lived;
• add a column to the merge query to compute the Variant-Base difference in lifetime cancer-related costs;
• add a column named Differs to compute whether a microdata record differed in either years lived or cost between Base and Variant.

A dynamic filter was applied to the Differs column of the Excel table for the merge query to display all records which differed between Variant and Base. 13 of the 500,000 microdata records differed, as follows:

key	life(base)	cost(base)	life(variant)	cost(variant)	life(delta)	cost(delta)	Differs
26847	82.90	9,099	82.90	10,570	0.0000	1,471	TRUE
59368	89.07	60,812	89.07	61,528	0.0000	717	TRUE
208131	72.68	40,304	98.16	19,647	25.4839	-20,657	TRUE
214559	94.60	31,285	94.60	27,932	0.0000	-3,353	TRUE
229714	86.53	25,446	86.53	13,450	0.0000	-11,996	TRUE
231202	95.18	101,255	95.18	100,388	0.0000	-867	TRUE
247895	97.40	40,914	97.40	9,396	0.0000	-31,518	TRUE
290098	92.17	13,059	92.17	14,461	0.0000	1,402	TRUE
302510	78.51	63,695	78.51	54,770	0.0000	-8,926	TRUE
357201	78.91	8,080	78.91	9,482	0.0000	1,402	TRUE
436603	39.75	112,787	39.75	111,870	0.0000	-916	TRUE
438020	65.36	84,806	63.36	80,545	-2.0000	-4,261	TRUE
447567	94.15	34,830	94.15	32,333	0.0000	-2,498	TRUE

The key column contains the value of case_seed and could be used to re-simulate any (or all) of these differing cases using Event Trace to explore the different causative pathways taken in the Base and Variant runs, and how those different pathways affected Person attributes.

These differences suggest that it might be interesting to understand how the change in MaxConsecutiveHpvTreatmentAllowed from 2 to 1 resulted in

• an additional ~25 years of life for case_id 208131,
• both positive and negative changes in health system costs for cases which experienced no change in years lived,
• case_id 438020 living an exact integer number of years 2.0000 less in Variant compared to Base.

Quite possibly all these differences are explained by different but realistic causative pathways taken in the two runs.

This example illustrates how differences at the microdata level from a marginal change in a model parameter can probe model causative pathways and model logic.

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Microdata output modes

Under construction

Text mode:

• to trace file or to entity-specific csv files
• runs using multiple instances have a distinct csv files for each instance.
• optional event context column
• optional event filtering

Database mode:

• all microdata output, including from possible multiple instances, is merged into the model database.
• no run-time event filtering (but can be done in model code with build-time settings).
• dbcopy can be used to extract microdata to csv files, supports numeric id's or codes.
• an API can be used to extract
• will support future funcitonlaity for run-=time tabulation, including microdata compare (winner-loser)

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Enabling microdata output

A model is capable of writing microdata if and only if model code contains the following statement:

options microdata_output = on;

A model with microdata capability will write the following warning to the log whenever it is run:

 Warning : model can expose microdata at run-time with microdata_output = on

If this is not a concern, for example if the model generates entities synthetically, this warning can be disabled by the following statement:

options microdata_output_warning = off;

A weight-enabled model which is also microdata-enabled will write the following message to the log when run

Note : model is weight-enabled and microdata-enabled, include entity_weight in Microdata for downstream weighted operations

as a reminder that the attribute entity_weight needs to be included in microdata output for downstream weighted tabulation.

Some internal entity attributes are created by the OpenM++ compiler. For example, the compiler creates an identity attribute to implement the filter of an entity table. These internal entity attributes are normally hidden. They can be made visible, including as microdata, using the following statement:

options all_attributes_visible = on;

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Run-time settings

Under construction

Run time settings are specified as options, on the command line or in an ini file. Below are commented examples of all run-time microdata settings:

[Microdata]

; Person = ageGroup,sex,age,income,isOldAge,pension

; Store all non-internal attributes of Person entity
;
; Person = All

; Store all non-internal attributes of all entities
; NOT recommended for production, use for debug only
;
; All    = true

; Allow to store entities internal attributes
; NOT recommended for production, use for debug only
;
; UseInternal = true

; Write microdata entity attributes into database
; Important: each microdata entity MUST have unique key
;
; ToDb  = false

; Write microdata entity attributes and events (if enabled) into CSV file(s)
; each microdata entity is written in it's own file
;
; ToCsv = false

; Write microdata entity(s) attributes and events (if enabled) into model Trace output
; Trace output must be enabled to produce any results;
; see Trace options in [OpenM] section above
;
; ToTrace = false

; Write selected events into Trace or CSV file
;
; Events = Birth,Union,Death

; If true then write event name into CSV file
;
; CsvEventColumn = true

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Build-time settings

Build-time settings which enable a model to output microdata are described in Enabling microdata output. Other build-time options determine when microdata is written during the simulation of each entity. The available options are:

Option	Default	Description
microdata_write_on_enter	off	microdata is written when the entity enters the simulation, before any event occurs in the entity.
microdata_write_on_exit	off	microdata is written when the entity exits the simulation.
microdata_write_on_event	off	microdata is written after an event occurs in the entity.

These options can be combined. If none of these options are on no microdata will be written unless model code does so explicitly by calling or hooking the built-in function write_microdata.

Note that attributes of an entity can change due to events in other linked entities in a model with interacting entities. So, even if these options are on, changes in attributes of an entity can be absent from microdata output for that entity. To see all changes in attributes in a model with interacting entities, consider using Event Trace.

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Writing microdata from model code

Under construction

Microdata can be written by calling the built-in entity function write_microdata() from model code, either directly or by using a hook statement.

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The microdata key

Under construction

A key is a unique identifier used to match entities or microdata records across runs. It is a 64-bit value of C++ type uint64_t.

The key for an entity is returned by the entity member function get_entity_key. If this function is not defined in model code, the OpenM++ compiler will provide a definition which returns the value of entity_id.

The key for a microdata output record is produced by the entity member function get_microdata_key If this function is not defined in model code, the OpenM++ compiler will provide a definition which returns the value of the function get_entity_key.

The function xz_crc64 is provided to create a 64-bit key using the crc-64 open source checksum (hash) algorithm. The key can be based on a single value, or on multiple values, as illustrated in the following examples.

Here's a generic example using a location in memory, and a size in bytes.

uint64_t key = xz_crc64(ptrToBytes, sizeInBytes, 0); // calculate crc-64 for bytes array

Here's an example using multiple attributes of a Person entity:

uint64_t key = xz_crc64( &((uint8_t *)(Person->age)), sizeof(int), 0); // start new crc-64 key
key = xz_crc64( &((uint8_t *)(Person->income)), sizeof(double), key);  // continue crc-64 key calculation
key = xz_crc64( &((uint8_t *)(Person->isOld)), sizeof(bool), key);     // continue crc-64 key calculation 

This example just illustrates how to create a key. A real example would use attributes which allow matching across runs.

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