Right Truncation
Right Truncation - Related Videos
01:09
Truncation in Survival Analysis
0 Views •
2025
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation. Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
04:06
Superior Lacrimal Gland Removal: A Surgical Technique to Remove the Palpebral and Orbital Parts of Superior Lacrimal Gland from Rabbit Model
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2025
This video describes the surgical procedure to remove the two parts: palpebral and orbital of the superior lacrimal gland from a rabbit to create a dry eye disease model. This rabbit model is suitable for studies of ocular surface homeostasis, pathophysiology and ocular therapeutics.
01:28
Database Searching
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2026
Effective research begins with the ability to navigate academic databases efficiently. Unlike general web search engines, scholarly databases are designed to index peer-reviewed articles, conference proceedings, and other credible academic publications. Developing a structured search strategy ensures that researchers can identify relevant, high-quality sources with precision. For example, when studying how sleep patterns relate to circadian rhythms in humans, researchers should move beyond...
11:50
Metal-silicate Partitioning at High Pressure and Temperature: Experimental Methods and a Protocol to Suppress Highly Siderophile Element Inclusions
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Cited by 5 •
2015
We present a procedure to determine the metal-silicate partitioning of siderophile elements, emphasizing techniques that suppress the formation of metal inclusions in experiments for the noble metals. The results of these experiments are used to demonstrate the effect of core-formation on the highly siderophile element composition of the mantle.
08:09
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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Cited by 15 •
2017
Here, the authors present a simple and efficient protocol to define a linear antigenic epitope using a purified monoclonal antibody and peptide scanning through dot-blot hybridization. The identified epitope can then be used in therapeutic and diagnostic applications.
FAQs
Right truncation changes the observed event-time distribution because inclusion depends on whether the event meets the threshold. Unlike ordinary censoring, an unobserved later event does not appear as a record with incomplete follow-up that can be modeled directly. This distinction matters because treating missing later events as censored can misrepresent the sample and distort estimated survival or incidence patterns.
The truncation threshold acts as a selection rule rather than merely a calendar boundary. Events that fall on the included side enter the dataset, while events outside it leave no direct trace. Consequently, the observed records can contain a disproportionate number of earlier failures. Analyses must represent this event-dependent selection when estimating the underlying survival distribution.
Event-time relationships are affected when truncation determines which observations are available. Associations involving failure time may reflect the inclusion condition as well as the process under study. Statistical models for right-truncated data therefore aim to separate the event-time pattern from the mechanism that selected the records, supporting more meaningful estimates of incidence and survival.
An analysis should first identify the event time, the truncation threshold, and the rule determining which records enter the dataset. Researchers can then choose a statistical model that accounts for the selection mechanism rather than applying methods designed for complete observations. This preparation clarifies which events are represented, which are absent, and how estimates should be interpreted.
These models can support estimates of survival distributions, incidence patterns, and relationships involving event times. Their value comes from adjusting interpretation for the fact that the available sample does not represent all events equally. Results can therefore describe the underlying event process more appropriately than summaries based only on the observed records.
Right truncation is relevant when records become available only after an event has occurred or within a defined observation window. In epidemiology, it can inform event and incidence analyses; in reliability research, it applies to failure-time data; and in astronomy, it helps address event records shaped by limited observation conditions.