Limitations of native ehrQL dummy data

Complex dataset definitions may take a long time to generate patients
...or may not be able to generate the requested number of patients
Certain fields will generate values that are valid in format, but are not necessarily real data. E.g. SNOMED CT codes may be generated in a valid format, but may not be real codes. e.g. The following will produce codes that look like SNOMED CT codes, but are randomly generated. Any further analysis which tries to use the code to filter or summarise data will fail to match expected data.
```
code = clinical_events.sort_by(clinical_events.date).first_for_patient().snomedct_code
```
(To some extent building up your dummy data tables can work around this sort of thing.)
Semantic validity is difficult to ensure in generated dummy data
- Certain interactions between tables/columns that should reflect reality will not necessarily be respected unless they are specified in the dataset definition, e.g.
  - no COVID-19 vaccines before 2020
  (Although note that such events could happen in the real data.)
- Demographic or clinical tendencies that are expected in the cohort of interest, e.g.
  - more white people than black people in England
  - correlation between obesity and diabetes
  - statins more commonly prescribed in over 50s