Description
Preserve explicit study follow-up censoring metadata.
Statistical record calculations depend on risk-set boundaries, cluster assumptions, missingness codes and exposure definitions. Entry can be excluded while exit inclusion is a policy choice, and one design-effect formula assumes equal cluster sizes. A local flag or contribution does not fit a model or establish a clinical result.
Preserve explicit study follow-up censoring metadata.
When to use this tool
Use Person Visit Loss Follow Up Censoring Code when you already have participantId, visitId, suppliedCode and need to preserve explicit study follow-up censoring metadata.
How to use the tool and how it works
- Enter participantId, visitId, suppliedCode. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read token. Use the output convention below.
Enter participantId, visitId, suppliedCode using the field constraints below. Encode these values, in exactly that order, as a JSON array and return the serialized array as text in token. String values are preserved without trimming or external terminology lookup.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| participantId | Participant Id | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| visitId | Visit Id | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| suppliedCode | Supplied Code | Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 8192. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| token | Token | Required. Text; maximum UTF-16 length 8192. |
{
"input": {
"participantId": "P17",
"visitId": "V4",
"suppliedCode": "lost follow-up"
},
"output": {
"token": "[\"P17\",\"V4\",\"lost follow-up\"]"
}
}Limitations and assumptions
- The result records supplied values without assigning an event time or censoring mechanism. Sources and asserted relationships are not independently authenticated.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Alternative or Complementary methods
Use the complete study design, data and statistical model for inference. Check censoring, clustering, visit windows, exposure and missingness conventions before combining these terms, and assess uncertainty with the full analysis.
References
Similar or alternative tools
- Study Composite Component Event Tag
Associate an explicitly recorded event component with a supplied composite endpoint identifier.
- Study Treatment Policy Exposure Label
Record explicit treatment-policy exposure metadata and estimand source.