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Life Tables
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
A life table based on longitudinal data is called a cohort life table. It follows the same population from beginning to end, and provides a complete history. With humans, if we are interested in the entire lifespan, we would need to wait more than 100 years to obtain all the necessary data. In practice, when calculation of a cohort life table for an actual population is not feasible, we settle for an approximation. In this chapter, we investigate a construct to calculate an ersatz life table, called a period life table, that can stand in for the cohort life table. The period life table requires we collect data over only two years. Technically, it is a method to convert cross-sectional data, or data obtained over a brief time-period, into longitudinal data. The period life table is designed to explain a life experience close in time to that of the group in which we are interested.
STATISTICAL EVALUATION OF THE RISK OF CANCER MORTALITY AMONG INDUSTRIAL POPULATIONS
Published in Richard G. Cornell, Statistical Methods for Cancer Studies, 2020
Michael J. Symons, John D. Taulbee
Chapter 5). Fundamental to the process is the conversion of the specific rates to the probabilities of dying during an age-band after surviving to the lower age of the age-band. Various assumptions are employed to make the conversion, one based upon the Gompertz hazard as a model for the specific death rates. It is known as Greville’s method, as in Spiegelman (1955, p. 88). Such a life table then provides estimates of life expectancy and probabilities of dying at or surviving to various ages.
Prognosis: Studies of disease course and outcomes
Published in Milos Jenicek, Foundations of Evidence-Based Medicine, 2019
A life table is a summarizing technique used to describe the pattern of mortality (or of any other event of clinical interest) and survival (i.e. non-event) in populations. The clinical life table ‘describes the outcome experience of a group or cohort of individuals classified according to their exposure or treatment history’.32
Survival status and predictors of mortality among children who underwent ventriculoperitoneal shunt surgery at public hospitals in Addis Ababa, Ethiopia
Published in International Journal of Neuroscience, 2023
Azene Bantie Wubie, Girum Sebsibe Teshome, Wudie Eneyew Ayele, Fikirtemariam Abebe, Tewodros Mulugeta Nigussie, Yalemgeta Biyazin Alemu, Migbar Sibhat Mekonnen
After the completion of data collection, the collected data were checked thoroughly by observation for any unfilled or inappropriate responses. Next, the data were entered into Epi-data manager version 4.4.2.1 and exported to SPSS version 25 and STATA version 14 for cleaning, edition, coding, and analysis. The nature of data such as normality and presence of outliers were determined before data analysis. Then, the data were described using relative frequency, percent, mean with standard deviation, and median based on its applicability. Life-table was used to estimate the cumulative probabilities of death at different time intervals. Kaplan Meier’s survival curve was considered to estimate median survival time during the follow-up period and log-rank tests to compare survival curves for possible differences in mortality among the groups.
30-day and 1-year mortality after skeletal fractures: a register study of 295,713 fractures at different locations
Published in Acta Orthopaedica, 2021
Camilla Bergh, Michael Möller, Jan Ekelund, Helena Brisby
Mortality rates at 30 days and at 1 year were calculated as the number of patients who died divided by the total number of patients (for each fracture localization, and total, as appropriate) and expressed as a percentage. The SMR was calculated using mortality among patients registered in the SFR and the corresponding life tables for 2012–2018 retrieved from Statistics Sweden (www.scb.se). The life tables used report the 1-year mortality rates for each year of age and sex separately, for each of the relevant years. When calculating SMR for the 30-day period, this was done under the assumption that the expected number of deaths during 30 days would be 1/12th of that expected at 1 year based on the 1-year life tables. The SMR was calculated as the ratio between observed and expected mortality with 95% confidence interval (CI), according to the method by Vandenbroucke (1982). All calculations for tables and figures were performed using SAS (v9.4; SAS Institute, Cary, NC, USA).
Lethal and sublethal effects of thiacloprid on Schizaphis graminum (Rondani) (Hemiptera: Aphididae) and its predator Hippodamia variegata (Goeze) (Coleoptera: Coccinellidae)
Published in Toxin Reviews, 2021
Pezhman Aeinehchi, Bahram Naseri, Hooshang Rafiee Dastjerdi, Gadir Nouri-Ganbalani, Ali Golizadeh
In addition to direct exposure to pesticides, which is estimated based on mortality rates, sublethal concentrations of pesticides can negatively be effective on physiology, life history, and behavior of the insect pests and their natural enemies (Stark et al. 1992, Stark and Banks 2003, Desneux et al. 2006, 2007, Garratt and Kennedy 2006, Rezaei et al. 2007). However, positive sublethal effects of some pesticides have been reported in several natural enemies (Fisher et al. 1999, Johnson and Tabashnik 1999, Cutler et al. 2009, Fernandes et al. 2010, de Castro et al. 2013). Sublethal effects of pesticides can be determined by studying life table parameters. A life table is an important analytical tool that can provide a comprehensive perspective of survival, development, and life span of insect populations (Chi and Yang 2003). These studies are necessary for evaluating the population growth and the effectiveness of natural enemies in pests’ control (Stark et al. 2007). The age-stage two-sex life table method has been used to describe the population characteristics of many pests and natural enemies under the influence of pesticides sublethal concentrations (Aleosfoor and Fekrat 2014, Almasi et al. 2016, Jalalipour et al. 2017).