I am putting this here because I know I won’t remember it but I think it’s moderately useful to know. As I see it it’s more about a study design rather than anything special about the censoring of the data which is right censoring.
Details #
Censored data (see entry) is data where at least some values are not know accurately but are known to be bigger than a given value, less than a given value, or between two values.
Type I censoring happens in a study where a certain number of participants enter a study and are followed through to a predetermined time point at which point the study finishes and all remaining participants are right censored as to whatever the endpoint that was being watched for was.
This is actually a very common, almost the ubiquitous design of naturalistic and routine data studies, for example the ITAMITED study I co-run with colleagues in Spain. In that study we started recruiting clients coming into a number of eating disorder services on 2.xi.2017, continued recruiting to 3.x.2020 and are now following them through to a predetermined termination date (31.x.2025). Any participants still in contact with the services at that final date will be right censored as to duration of treatments.
As always, censoring matters because if we ignore the censoring we introduce a bias into our estimation of general durations of therapies for that study. However, there are statistical methods that handle censoring and will avoid that bias (given some sensible assumptions mostly that the pattern of duration hasn’t actually changed across the period of the study). See survival analysis for probably the biggest class of such methods.
Try also #
Censored data
Frailty analysis
Interval censoring
Right censoring
Survival analysis
Type II censoring
Chapters #
Not covered in the OMbook.
Online resources #
None forseeable.
Dates #
First created 13.xii.24, tweaked 15.xii.24.
