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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Comparing the Survival Analysis of Two or More Groups

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Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Kaplan-Meier Approach01:24

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

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Identifying population groups with low palliative care program enrolment using classification and regression tree

Jun Gao1, Grace M Johnston, M Ruth Lavergne

  • 1Health Canada, Centre for Vaccine Evaluation, Biologics and Genetic Therapies Directorate, Ottawa, Ontario, Canada.

Journal of Palliative Care
|August 3, 2011
PubMed
Summary
This summary is machine-generated.

Classification and Regression Tree (CART) analysis identified cancer patients with lower palliative care program (PCP) enrollment. Key factors included advanced age, distance from care, and short diagnosis-to-death intervals, informing targeted interventions.

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Area of Science:

  • Palliative Care
  • Health Services Research
  • Oncology

Background:

  • Palliative care programs (PCP) aim to improve quality of life for patients with serious illnesses.
  • Understanding factors influencing PCP enrollment is crucial for equitable access to supportive care.

Purpose of the Study:

  • To identify specific subpopulations with lower palliative care program enrollment rates using advanced analytical methods.
  • To explore demographic, social, medical, and health system predictors associated with reduced PCP utilization.

Main Methods:

  • Classification and Regression Tree (CART) analysis was employed for recursive partitioning of predictor variables.
  • Multiple Logistic Regression (MLR) findings were used for comparative analysis.
  • Data comprised 6,892 adult cancer deaths in Nova Scotia (2000-2005).

Main Results:

  • Overall PCP enrollment was 72%.
  • Lowest enrollment rates were observed in nursing home residents over 82 (27%), individuals living >43 km from PCP (31%), and those diagnosed <2 weeks before death (37%).
  • Highest enrollment (86%) was seen in patients receiving palliative radiation.

Conclusions:

  • CART analysis effectively identified subpopulations with significantly lower palliative care program enrollment.
  • Interactions among demographic, social, medical, and health system factors define these low-enrollment groups.
  • Findings highlight the need for targeted strategies to improve PCP access for vulnerable cancer patient populations.