The document presents information on multiple decrement life tables (MDLT). It defines MDLT as modeling simultaneous operation of several causes of decrement where an individual fails due to one cause. Examples include double and triple decrement life tables. MDLT is compared to conventional and other types of life tables. Its uses include analyzing population socioeconomic characteristics, labor force growth/changes, and calculating average working hours. Assumptions of MDLT include each death from a single cause and independent probabilities of dying from different causes. The document concludes that MDLT can be used where other life tables have limitations due to modeling multiple decrements.
Introduction:
Life table:
Life table is a comprehensive method of describing mortality, survival and other vital events in a population.
It is composed of several sets of values showing how a group of infants who are under unchanging conditions would gradually die.
It provides concise measures of longevity of that population.
Separate tables are prepared for males and females after each decennium census.
It is also called as the “Biometer” of the population by William Farr.
Introduction:
Life table:
Life table is a comprehensive method of describing mortality, survival and other vital events in a population.
It is composed of several sets of values showing how a group of infants who are under unchanging conditions would gradually die.
It provides concise measures of longevity of that population.
Separate tables are prepared for males and females after each decennium census.
It is also called as the “Biometer” of the population by William Farr.
Population Studies / Demography IntroductionMuteeullah
Presentation and Assignment on Population / Demography including mortality, fertility and their measure, population census, vital registration, demography survey, House hold survey, population composition, errors in demographic data, demographic measures.................By Muteeullah Channa University of Sindh
This Presentation course will help you in understanding the Machine Learning model i.e. Generalized Linear Models for classification and regression with an intuitive approach of presenting the core concepts
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Logistic Loglogistic With Long Term Survivors For Split Population ModelWaqas Tariq
Split population models are also known as mixture model . The data used in this paper is Stanford Heart Transplant data. Survival times of potential heart transplant recipients from their date of acceptance into the Stanford Heart Transplant program [3]. This set consists of the survival times, in days, uncensored and censored for the 103 patients and with 3 covariates are considered Ages of patients in years, Surgery and Transplant, failure for these individuals is death. Covariate methods have been examined quite extensively in the context of parametric survival models for which the distribution of the survival times depends on the vector of covariates associated with each individual. See [6] for approaches which accommodate censoring and covariates in the ordinary exponential model for survival. Currently, such mixture models with immunes and covariates are in use in many areas such as medicine and criminology. See for examples [4][5][7]. In our formulation, the covariates are incorporated into a split loglogistic model by allowing the proportion of ultimate failures and the rate of failure to depend on the covariates and the unknown parameter vectors via logistic model. Within this setup, we provide simple sufficient conditions for the existence, consistency, and asymptotic normality of a maximum likelihood estimator for the parameters involved. As an application of this theory, the likelihood ratio test for a difference in immune proportions is shown to have an asymptotic chi-square distribution. These results allow immediate practical applications on the covariates and also provide some insight into the assumptions on the covariates and the censoring mechanism that are likely to be needed in practice. Our models and analysis are described in section 5.
Briefly describing:
(1.) Crude Death Rate
(2.) Specific Death Rate
(3.) Proportional Mortality Rate
(4.) Maternal Mortality Ratio (MMR)
(5.) Odds Ratio
(6.) Standardized Mortality Ratio (SMR)
(7.) Case Fatality Rate (CFR)
Population Studies / Demography IntroductionMuteeullah
Presentation and Assignment on Population / Demography including mortality, fertility and their measure, population census, vital registration, demography survey, House hold survey, population composition, errors in demographic data, demographic measures.................By Muteeullah Channa University of Sindh
This Presentation course will help you in understanding the Machine Learning model i.e. Generalized Linear Models for classification and regression with an intuitive approach of presenting the core concepts
General Linear Model is an ANOVA procedure in which the calculations are performed using the least square regression approach to describe the statistical relationship between one or more prediction in continuous response variable. Predictors can be factors and covariates. Copy the link given below and paste it in new browser window to get more information on General Linear Model:- http://www.transtutors.com/homework-help/statistics/general-linear-model.aspx
Logistic Loglogistic With Long Term Survivors For Split Population ModelWaqas Tariq
Split population models are also known as mixture model . The data used in this paper is Stanford Heart Transplant data. Survival times of potential heart transplant recipients from their date of acceptance into the Stanford Heart Transplant program [3]. This set consists of the survival times, in days, uncensored and censored for the 103 patients and with 3 covariates are considered Ages of patients in years, Surgery and Transplant, failure for these individuals is death. Covariate methods have been examined quite extensively in the context of parametric survival models for which the distribution of the survival times depends on the vector of covariates associated with each individual. See [6] for approaches which accommodate censoring and covariates in the ordinary exponential model for survival. Currently, such mixture models with immunes and covariates are in use in many areas such as medicine and criminology. See for examples [4][5][7]. In our formulation, the covariates are incorporated into a split loglogistic model by allowing the proportion of ultimate failures and the rate of failure to depend on the covariates and the unknown parameter vectors via logistic model. Within this setup, we provide simple sufficient conditions for the existence, consistency, and asymptotic normality of a maximum likelihood estimator for the parameters involved. As an application of this theory, the likelihood ratio test for a difference in immune proportions is shown to have an asymptotic chi-square distribution. These results allow immediate practical applications on the covariates and also provide some insight into the assumptions on the covariates and the censoring mechanism that are likely to be needed in practice. Our models and analysis are described in section 5.
Briefly describing:
(1.) Crude Death Rate
(2.) Specific Death Rate
(3.) Proportional Mortality Rate
(4.) Maternal Mortality Ratio (MMR)
(5.) Odds Ratio
(6.) Standardized Mortality Ratio (SMR)
(7.) Case Fatality Rate (CFR)
Abstract- Statistical models include issues such as statistical characterization of numerical data, estimating the probabilistic future behaviour of a system based on past behaviour, extrapolation or interpolation of data based on some best-fit, error estimates of observations or model generated output. If the statistical model is used to analyse the survival data it is known as statistical model in survival analysis. There are different statistical data. Censored data is one of its kinds. Censoring means the actual survival time is unknown. Censoring may occur when a person does not experience the event before the study ends or lost to follow-up during the study period or withdraws from the study. For this type of censored data the suitable model is survival models. Survival models are classified as non-parametric, semi-parametric and parametric models. The survival probability can be obtained using these models. Using the health data of cancer registry in Tiruchirappalli, Tamil Nadu , a study on survival pattern of cancer patients was explored, the non-parametric modelling that is Kaplan-Meier method was used to estimate the survival probability and the comparison of survival probability of obtained by life table and Kaplan Meier methods for each stage of the disease were made. Log rank test has been used for the comparison between the estimates obtained at the different stages of the disease.
PRIVATE AGE ADJUSTMENTWhen analyzing epidemiologic dat.docxsleeperharwell
PRIVATE
AGE ADJUSTMENT
When analyzing epidemiologic data, researchers often wish to adjust for the influence of some variable so that the "true" effect of other variables can be seen more clearly. Consider the example of a study to determine if gray hair is related to mortality risk. Two statements stand out in this study:
1. People with gray hair have a higher death rate when compared to other people.
2. People with gray hair are older than others people.
Because of this second statement the meaning of statement one is obscure. The possible link between gray hair and mortality risk is confused by the effect of age on mortality risk. Age is considered a confounding factor that needs to be accounted for to accurately assess the impact of gray hair on mortality rates. Epidemiologists use many tools to sort through information and overcome this confusion of information by adjusting data. The purpose of data adjustment is to disentangle the relationship so that we can evaluate a variables effect free from confusion and distortion. For the gray hair investigation, adjustment would permit us to determine whether persons of the same age who have gray hair have different mortality risks. (Sempos 1989)
Confounding Variables
Confounding variables are variables whose effects confuse the true relationships between factors and diseases. This is why there is a need for data adjustment. In order for a variable to be considered a confounding variable, it must be related to the disease or condition of interest and to the risk factor being investigated (Miettinen 1970). But if the possible confounding variable is truly related only to the disease of interest, it may still be desirable to adjust for it (Mantel 1986). One reason is the adjustment could possibly reduce the sampling variance of the comparison that is being investigated.
Adjustments
A common example of data adjustment is the age adjustment of mortality rates. While the age adjustment technique is most often applied to mortality (death) rates, it could also be applied to incidence of disease, prevalence, or any other kind of proportional rates. Age adjustment allows comparison of mortality risk for various groups free from the distortion of one group having a different age distribution than another. There are two types of age adjustments in relation to mortality rates -- direct and indirect age adjustments.
Direct Adjustment
Direct adjustment, or direct standardization, is to superimpose the age distribution of a standard population on the two study groups to be compared. Standardized rates are then calculated for each population, making use of the standard age distribution. These adjusted rates are then compared, and any difference between them can no longer be due to difference in age distribution because age has been taken into account. The direct method uses two inputs called age-specific rates and standard population.
Age-Specific Rates
A set of age-specif.
report “Rabbits and Wolves”Discuss the changes in parameters and how.pdfkostikjaylonshaewe47
report “Rabbits and Wolves”Discuss the changes in parameters and how they affected the
population growth curves for each organism (be sure to mention particular changes in the graphs
and ending populations). Think about how this simulation could apply to the real world. What
other factors or variables would have to be included. What if humans were added as a fourth
organism, how would they affect this simulation? Also, do you think that a computer model is a
useful tool to science? More precisely do you think there are times when such a model is useful
and when such a model is not useful? Be sure to start your discussion with a statement of
objectives - I have listed objectives above, write and state if you think you obtained the
objectives and explain why or why not. There is absolutely no penalty for thinking that you
didn\'t complete one of the objectives.
Solution
Ans.) To understand the different models that are used to represent population dynamics, first
understand the general equation for the population growth rate (it is the change in number of
individuals in a population over time):
dT/dN=rN
In this equation, T = growth rate of the population
NNN = population size
TTT = time
rrr = per capita rate of increase (that is, how quickly the population grows per individual already
in the population).
The equation above is very general, and we can make more specific forms of it to describe two
different kinds of growth models: exponential and logistic.
A positive growth rate implies that the population is increasing, whereas a negative growth rate
shows that the population is reducing. A growth ratio of zero indicates that there is a balance i.e.
that there were the same number of organisms at the beginning and end of the particular time
period. Sometimes, growth rate may be zero even when there are significant changes in the birth
rates, death rates, immigration rates and age distribution between the two times period.
Situation which includes exponential development, different age-independent density variable
influencing survival, three influencing fertility. The one function meeting the presumptions of
the calculated model delivered a strategic development bend typifying the right values or rm and
K. The others created sigmoid bends to which self-assertive strategic bends could be fitted with
differing achievement. In view of population time slacks, two of the capacities influencing
fruitfulness created overshoots and damped motions amid the underlying way to deal with the
enduring state.
The other factors or variables would have been included in the given situation would be the
climate condition.
In my opinion computer model is a useful and informative tool in analyzing the population
growth curves of any organism. In most of the cases, the computer model for population growth
studies is logical..
Use Proportional Hazards Regression Method To Analyze The Survival of Patient...Waqas Tariq
The Kaplan Meier method is used to analyze data based on the survival time. In this paper used Kaplan Meier procedure and Cox regression with these objectives. The objectives are finding the percentage of survival at any time of interest, comparing the survival time of two studied groups and examining the effect of continuous covariates with the relationship between an event and possible explanatory variables. The variables (Age, Gender, Weight, Drinking, Smoking, District, Employer, Blood Group) are used to study the survival patients with cancer stomach. The data in this study taken from Hiwa/Hospital in Sualamaniyah governorate during the period of (48) months starting from (1/1/2010) to (31/12/2013) .After Appling the Cox model and achieve the hypothesis we estimated the parameters of the model by using (Partial Likelihood) method and then test the variables by using (Wald test) the result show that the variables age and weight are influential at the survival of time.
Morbidity has been defined as any departure, subjective or objective, from a state of physiological or psychological well-being. In practice, morbidity encompasses disease, injury, and disability.
A Moment Inequality for Overall Decreasing Life Class of Life Distributions w...inventionjournals
:A moment inequality is derived for the system whose life distribution is in an overall decreasing life (ODL) class of life distributions. A new nonparametric test statistic for testing exponentiality against ODL is investigated based on this inequality. The asymptotic normality of the proposed statistic is presented. Pitman's asymptotic efficiency, power and critical values of this test are calculated to assess the performance of the test. Real examples are given to elucidate the use of the proposed test statistic in the reliability analysis. Wealso proposed a test for testing exponentiality versus ODL for right censored data and the power estimates of this test are also simulated for censored data for some commonly used distributions in reliability. Finally, real data are used as an example for practical problems.
Generalized SEIR Model on Large NetworksDatabricks
SEIR model is a widely used model for simulating the spread of infectious diseases. In its simplest form, the SEIR model assumes that individuals in the population can assume any of the four states: Susceptible, Exposed, Infected and Recovered (or Removed), and the evolution of the system is modeled as a system of ordinary differential equations.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Presentation on Multiple Decrement Life Table by amin
1. Presentation on MULTIPLE
DECREMENT LIFE TABLE
Presented By
Aminul Islam
ID:14115570
Department of Population Science
and Human Resource Development,
University of Rajshahi
2. Contents
Defintion of Multiple Decrement Life Table
Comparative Study of MDLT with Other Life Tables
Applications of MDLT
Assumptions of MDLT
Construction of MDLT
Conclusion
3. Multiple Decrement Life Table
• Multiple decrement models are extensions of
standard mortality models whereby there is
simultaneous operation of several causes of
decrement.A life fails because of one of these
decrements.
4. Examples of MDLT:
Double Decrement Life Table
MDLT Triple Decrement Life Table
Increment Decrement Life Table
5. Comparative Study of MDLT with Other Life Tables
CONVENTIONAL LIFE TABLE
DOUBLE DECREMENT LIFE TABLE
MDLT
INCREMENT DECREMENT LIFE TABLE
MULTIPLE DECREMENT LIFE TABLE
MULTISTATE LIFE TABLE
6.
7. Uses of MDLT
1)Analysis of various socio-economic characterstics
of the population.
2) For studying growth and change in the labour
force and related topics.
3) Provides information on age specific rate of
accession and separation from the labor force.
4)For calculating the average number of working
hours out of the labour force.
8. Assumptions of MDLT
Each death is due to a single cause
Each individual in population has exactly the one
probability of dying from any of the causes
operating the population.
Probability of dying from any cause is
independent from the probability of dying from
other causes
MDLT is concerned with the probability that an
individual will die of a certain cause in the
presence of other causes.
9.
10.
11.
12.
13.
14. Total Force Of Decrement
Since the number of decrement due to all
causes in the interval 𝑥 to 𝑥 + ℎ are 𝑙 𝑥, 𝑡 →
𝑙(𝑥 + ℎ, 𝑇) and the exposure is ℎ. 𝑙(𝑥, 𝑇) in the
multiple decrement, then the force of decrement
at age 𝑥 is defined as -----
=
1
lim
𝑙 𝑥+ℎ,𝑇 −𝑙(𝑥,𝑇)
15. Conclusion
• So finally we can tell that multiple decrement
life table is used for multiple purposes.where
other life table has some limitation,multiple
decrement life table can be used in such case.
