Veterans Administration Lung Cancer Survival Analysis

The rampant growth of lung cancer necessitates improvement in medical services an imperative undertaking. Gaining a deep understanding of the stage of the lung cancer is a crucial step because it helps to determine the survival rates based on the previous studies that have been conducted. The survival rates help to determine the portion of patients with the same lung cancer type, as well as, stage are still alive eat a given amount of time after they are diagnosed. The statistics are crucial because they indicate how long you can live. Additionally, one is able to better understand how likely the administered treatment will be successful. According to a report released by MHS, scores of veterans have been diagnosed with stage I and II of lung cancer which stands at 32.

8% compared to the community members. The high prevalence of lung cancer stage I and II is attribute to tobacco smoking behavior among the veterans. However, the MHS indicated that veterans that are 65 years and below diagnosed with early stage disease are less likely to survive when they are subjected to surgical resection. As stated before, lung cancer is categorized into two major types – small cell lung cancer (SCLC), and non-small cell lung cancer (NSCLC). 1 Hazard Function Distribution of T has an alternative characterization, the hazard function. It can be defined as instantaneous rate of occurrence of the event, Hazard function is a conditional density, given that the event in question has not yet occurred prior to time t. The numerator above is express as conditional probability that the event will occur in an interval [t, t+dt) given that it has not occurred before, and the denominator is the width of the interval.

Diving the two terms can result to rate of event occurrence per unit time. And taking the limit as the width of the integral goes down to zero to obtain the instantaneous rate of occurrence. Lung Cancer Patients. Data of survival chances of patients with respect to time to event can help patients develop a proactive approach in making choices for the treatment Definition of Terms Squamous cell carcinoma – cancers start in early versions of squamous cells; usually found in the central part of lungs, near main airway, specifically in the bronchus (ACS, 2016) Squamous cell – flat cells that line the inside of the airways in the lungs (ACS, 2016) Adenocarcinoma – cancers start in early versions of the cells that normally secrete substances such as mucus; usually found in the outer parts of the lung (ACS, 2016) Large cell lung cancer – grows and spreads quickly to other organs, which can make harder to treat; can appear in any part of the lung (ACS, 2016) Chapter 2 Review of Related Literature Previous research studies have made in depth research about the factors that influence the survival rate among lung cancer patients.

According to a research study conducted by Siegel et al. , (2015) revealed that age, sex, comorbidities, as well as, the quality of health care have a significant influence on the differential treatment correlated with the response in non-small-cell lung cancer. The study also explored the role of prognostic factors that tend to play a crucial role in a clinical outcome when the patient is subjected to a medical therapy. The recent past medical studies have discovered that sub-groups of patients reported with non-small-cell lung cancer who were female, Asian, and they were non-smokers or previously light smokers and the patients had tumors responded differently to medical therapies Davis et al. First, the research study discovered patients with the mentioned characteristics were likely to respond to epidermal growth factor receptor (EGFR) inhibitors compared to patients without these conditions.

The study conclude that patients whose tumors are associated with activating epidermal growth factor receptor (EGFR) mutations were likely to respond favorably to EGFR inhibitors. Consequently, the findings of the study were used in the clinical settings so that to predict the response behavior of patients associated to have gene mutations. The prediction was intended to prescribe medical treatment based on whether the tumors of the patient had activating EGFR mutations or not. The objective of this paper is to analyze the survival time of patients suffering from non-small cell lung cancer, identify the time interval crucial for the survival and identify the effects of various factors such as age, gender and treatment type. The performances of the models built are analyzed to understand the significance of cubic splines used in these models.

Chapter 3 Methodology Data use is secondary data. All survival results will be generated through R software, which is free software environment for statistical computing and graphics, according to R Foundation. Survival Rate In this study the researcher uses Kaplan-Meier estimator to generate the survival rate. 0000 1st Qu. 00 Median :1. 000 adeno :27 Median : 80. 0 Median :1. 0000 Median :60. 0 Max. 0000 Max. 00 diagtime age prior Min. 000 Min. 00 Min. 000 3rd Qu. 00 3rd Qu. 00 Max. 000 Max. 00 Max. Using these results, we can calculate the individuals’ survival probability at each year so that we can derive annual deaths caused of the cancer. Our model is expected to contribute to construct the government’s budget into health and welfare as well as to predict the demands for insurance industry related to the cancer.