Time series analysis of survival and oviposition cycle duration of

Jacques D Charlwood1,2,3, Thomas A Smith4,5, Ayubo Kampango2,6,7

  • 1DBL Centre for Health Research and Development, Department for Veterinary Pathobiology, Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark.

Peerj
|June 5, 2023
PubMed
Abstract

Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
301
Ovarian Cycle01:27

Ovarian Cycle

The menstrual cycle includes a critical component known as the ovarian cycle, which undergoes two main phases each month—the follicular phase and the luteal phase. The follicular phase is variable and averaging around 14 days. Ovulation, triggered by a surge in luteinizing hormone (LH), marks the transition between the two phases. The second phase, the luteal phase, is relatively consistent, lasting approximately 14 days, and is marked by the activity of the corpus luteum. While a cycle...
1.4K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
160
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
101
Life Histories01:29

Life Histories

Overview
18.1K
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
402