Who can handle clustered survival data?
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In the field of survival research, it is difficult to study a single individual’s or group’s behavior at once. Instead, survival data is often grouped into clusters, which are categorized according to characteristics that explain variation in the survival rate (Agarwal et al., 2015). This group-based analysis is useful for identifying the factors affecting survival outcomes, which can guide healthcare providers in creating interventions to optimize survival chances. But, in most studies, the type of clustering being used can be a
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In the recent times, researchers have started using survival analysis to analyze the time trends in the clinical outcomes of patients. click now The clinical outcomes are defined as the time from the patient’s diagnosis to the time of their event, and the time trend is defined as the change in time from one time point to another. Clustered survival data is an important application of survival analysis that involves analyzing the survival data of multiple patients who belong to the same cluster or group, where each patient belongs to a group that has a well-defined probability
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