Analysis of Two Sets of Historical Data to Construct and Appropriate Survival Model

Survival Analysis for Evaluation of Mortality in Selected Body Weight Lines of Japanese quails Using Cox Model

New Community Jewish High School honors biology students conducted research on damselflies using representative data provided by a previous class. Using the lincoln petersen index, it was determined that there were approximately 140 damselflies. Using morositas index, it was discovered that the damselflies are arranged in a clumped distribution

In our collective effort to display and analyze data regarding drug arrests across a number of demographic lines in the United States a number of patterns have been recorded. Specifically, we’ve noticed a pronounced difference in proportionality among exactly who is arrested, with African Americans being arrested at far greater frequency.

Corrections Statistical Analysis Tool (CSAT) – Prisoners is a dynamic analysis tool that provides you to see the National Prisoners Statistics (NPS) on inmates in both federal and state facilities. People can immediately produce tables of numbers and rates of national and jurisdictional statistics, from 1978 to the latest year that NPS information are accessible (Carson, & Mulako). This web tool incorporates state-level prisoner information from the 50 state department of corrections. Also, people can make custom tables of every year by the numbers of inmates in custody or under legal jurisdiction, people who are in custody of private correctional facilities, the imprisonment rate of prisoners sentenced to over 1 year, which is a felony

Total length of stay for SDS Patient is [(8.660280 + Random.LogLogistic (3.983742, 94.44468)) + (23.998799 + Random. Gamma (1.820359, 29.215313)) + (15.451090 + Random. Lognormal (Math. Log (49.635313), 0.500759)) + (1 / Random. Weibull (2.503230, 0.019615))]

Bivariate survival data arise when each subject understudy experiences two events. For e.g., failure times of paired organs like kidneys, eyes, ears or any other paired organs of an individual, In industrial applications, the breakdown times of dual generators in a power plant or failure

a- Survival analysis curve based on criterion A; b- Survival analysis curve based on criterion B; c- Survival analysis curve based on criterion C

. In addition to patient perspectives on their treatment options and findings from a brief GA, a further consideration in estimating potential benefits of adjuvant therapy is the patient’s estimated life expectancy, to determine whether they are likely to live long enough to benefit from the treatment. Chronologic age is an integral component of such estimates; however, it is by no means a sufficient consideration. As our two clinical cases illustrate, two 70 year old breast cancer patients can have very different underlying health, physical activity, and social support systems and require a treatment approach that reflects these differences (Figure 1). It can be particularly challenging for physicians to estimate the life expectancy of their patients, especially for patients in advanced stages of cancer [35]. Fortunately, several tools are available to aid in the estimation of life expectancy in a variety of different patient populations (e.g. community dwelling or nursing home) and life expectancy time frame [36]. Many of these tools are available in an online calculator format on the “ePrognosis” website (www.eprognosis.com). For example the Lee- Schonberg index intakes into account the patient’s age, gender, body mass index, comorbidities, cigarette smoking, physical activity, previous hospitalizations, and limitations in activities of daily living due to physical, mental, emotional or cognitive problems to estimates the patient’s life expectancy [37-40]. None of these

In this modeling assignment, I will develop a model function for the relationship between time of day and the height of the tide. I will first show the data in a table form copied from the assignment sheet. Then I will use the data to construct a scatter graph of time against height, then I will develop a function that models the behavior noted in the graph analytically, and describe any variables, parameters or constraints for the model. In the end I will modify the same model using the regression feature of graphing software to develop a best-fit function for the data. This modeling task will allow me to find out what is the best time that a good

The main objective of the present project is to establish the existence of a statistical relation between Life Expectancy (y) at birth and GDP per capita (x). First, we will present in Section 2 the data, from an official governmental source, containing Life

η(X) is obtained through fitting a Weibull-Log Linear model (also known as Weibull-regression or Weibull-proportional-hazards).

This experiment was performed to understand the process of linear least-squared analysis as well as developing the skills to use EXCEL and having the criteria for the best line of fit to ones’ graphs. Linear least-squared analysis is a statistical method to determine a line of best fit by minimizing the sum of squares created by a mathematical function. A “square” is determined by squaring the distance between a data point and the regression line. For the 1st order kinetics graph, the y-axis was for the sum of ln(A). The straight line equation came out to be y=-0.0224x-0.546 while the regression line was at a 0.99564 which was very good. For the 2nd order kinetics graph, the y-axis was for the sum of 1/A. The straight line equation came out to be y=0.0575x+1.6104 while the regression line was at a 0.97327 which was also quite good. Each data set had 9 degrees of freedom. The F-value for the first graph was 2057.16 and the 2nd graph was 327.67.

Part results for outputs from the exponential and weibull distributions for these data are shown in Appendix~\ref{AppendixB}. These results confirm the evidence from Figure~\ref{HazCumHaz} and show that neither distributions fit the data well.

A Cox model is a statistical method used for exploring the relationship between the survival of a patient and several risk variables. A Cox model provides an estimate of the therapeutic procedure effect on survival after adjustment for other explanatory variables. It allows to estimate the risk of mortality, or other events of interest, for patients, given their prognostic variables. Cox model analysis involves examining the coefficients for each variable. A positive regression coefficient for an explanatory variable means that the hazard for patient having a high positive value on that particular variable is high. Conversely, a negative regression coefficient implies a better prognosis for patients with higher negative values of that variable.

This process is continued for selecting the best model for all the new stations (201 to 275). The R software package is used for this purpose. The extracted best models for the new stations are stored in .csv file.