Statistical methods

2021 ◽  
pp. 207-228
Author(s):  
Gail Williams ◽  
Robert S. Ware
Keyword(s):  
Statistical Methods ◽  
Probability Distributions ◽  
Epidemiological Data ◽  
Target Population ◽  
Categorical Variables ◽  
Complex Data ◽  
Survival Times ◽  
Time To Event Data ◽  
Ease Of Access ◽  
Inference Methods

This chapter provides an introduction to statistical methods with illustrative examples from public health and epidemiological research. The chapter begins by distinguishing between a study sample and a target population. It goes on to outline different methods of sampling, including probability and non-probability sampling methods. In the following section, the distributions of epidemiological variables are considered, leading on to discussion of probability distributions and statistical inference. Methods for comparing data from two or more groups are then outlined, including methods for continuous and categorical variables. Analysis of time-to-event data to evaluate survival times is then outlined. The final section of the chapter discusses the application of multivariable models to epidemiological data, including extensions of basic models to more complex data distributions. The chapter concludes by cautioning that increasing ease of access to sophisticated statistical methods may increase the risk of erroneous application. There is little substitute for consulting a qualified statistician, particularly with complex designs.

scholarly journals Statistical Methods for Banking Sectors to Detect the Eligible Customers for Home Loan

2019 ◽  
Vol 8 (4) ◽  
pp. 1849-1853
Keyword(s):  
Sensitivity Analysis ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Categorical Variables ◽  
Type I ◽  
Type Ii ◽  
Type I Errors ◽  
Fuzzy Analysis ◽  
Type Ii Errors

Nowadays people are interested to avail loans in banks for their needs, but providing loans to all people is not possible to banks, so they are using some measures to identify eligible customers. To measure the performance of categorical variables sensitivity and specificity are widely used in Medical and tangentially in econometrics, after using some measures also if banks provide the loans to the wrong customers whom might not able to repay the loans, and not providing to customers who can repay will lead to the type I errors and type II errors, to minimize these errors, this study explains one, how to know sensitivity is large or small and second to study the bench marks on forecasting the model by Fuzzy analysis based on fuzzy based weights and it is compared with the sensitivity analysis.

Physiological Exophoria Did Not Increase The Incidence of Myopia in Rural School Children in Taiwan

2021 ◽  
Author(s):  
Jui-Hung Hsu ◽  
Li-Ju Lai ◽  
Tao-Hsin Tung ◽  
Wei-Hsiu Hsu
Keyword(s):  
Elementary School ◽  
Logistic Regression ◽  
Elementary School Students ◽  
Multinomial Logistic Regression ◽  
Significant Risk ◽  
Epidemiological Data ◽  
Significant Risk Factor ◽  
Categorical Variables ◽  
School Students

Abstract Purpose:This study evaluated the incidence rate and risk factors for developing myopia in elementary school students in Chiayi, Taiwan.Methods:This prospective cohort study comprised 1816 students without myopia (grades 1 to 5 in Chiayi County). The students underwent a noncycloplegic ocular alignment examinations using an autorefractometer and completed a questionnaires at baseline and at a 1-year follow-up. A univariate logistic regression was used to assess the effects of the categorical variables on new cases of myopia. A multinomial logistic regression was then conducted. A chi-squared test was used to compare new cases of myopia in terms of ocular alignment. A Cox hazard ratio model was then used to validate factors associated with changes in ocular alignment. A P value of <.05 was considered significant.Results: In 370 participants with new cases of myopia out of 1816 participants, a spherical error of −1.51 ± 0.6 diopters was noted at follow-up. The baseline ocular alignment was not a significant risk factor for developing myopia (exophoria vs orthophoria: OR 1.26, 95% CI 0.97-1.62; other vs. orthophoria: OR 1.15, 95% CI 0.73-1.82). However, new cases of myopia (HR 1.36, 95% CI 1.14-1.61), and baseline ocular alignment (exophoria vs orthophoria: HR 3.76, 95% CI 3.20-4.42; other vs orthophoria: HR 3.02, 95% CI 2.05-4.45) were associated with exophoria at follow-up.Conclusions: This study provided epidemiological data on the incidence of myopia in elementary school students in Chiayi, Taiwan. It also demonstrated that physiological exophoria does not predispose patients to developing myopia.

Measuring the Global Burden of Disease

2020 ◽  
Keyword(s):  
Burden Of Disease ◽  
Philosophy Of Medicine ◽  
Epidemiological Data ◽  
Global Burden Of Disease ◽  
Global Burden ◽  
Complex Data ◽  
Health States ◽  
Research Collaborations ◽  
Disease Study ◽  
Philosophical Questions

In this volume, a group of leading philosophers, economists, epidemiologists, and policy scholars continue a twenty-year discussion of philosophical questions connected to the Global Burden of Disease Study (GBD), one of the largest-scale research collaborations in global health. Chapters explore issues in ethics, political philosophy, metaphysics, the philosophy of economics, and the philosophy of medicine. Some chapters identify previously unappreciated aspects of the GBD, including the way it handles causation and aggregates complex data while others offer fresh perspectives on frequently discussed topics such as discounting, age-weighting, and the valuation of health states. The volume concludes with a set of chapters discussing how epidemiological data should and shouldn’t be used.

scholarly journals The use of statistical methods by MBA graduates in South African management and its implications for curriculum design

1994 ◽  
Vol 25 (2) ◽  
pp. 86-93
Author(s):  
Trevor Wegner ◽  
Stephanie Stray ◽  
Peter Naudé
Keyword(s):  
South African ◽  
Statistical Methods ◽  
Curriculum Design ◽  
Quantitative Methods ◽  
Teaching Strategy ◽  
Target Population ◽  
Methods Course ◽  
Course Planning ◽  
The United Kingdom ◽  
Similar Survey

With this study we aim to identify the degree of penetration of statistical methods in South African management. Consequently, that section of the management population with past exposure to quantitative methods is targetted. Thus the target population was all MBA alumni from South African Business Schools operating in South African companies. A response rate of 27% (408 usable responses) was achieved. The findings of this study correlate highly with those of a similar survey conducted in the United Kingdom in 1991. In addition to reporting these findings, we also sought to examine the implications of these results on future statistical methods course planning. We recommend a change in teaching strategy to promote greater utilization of this discipline in practice.

scholarly journals Statistical Methods for Eliciting Probability Distributions

2005 ◽  
Vol 100 (470) ◽  
pp. 680-701 ◽  
Author(s):  
Paul H Garthwaite ◽  
Joseph B Kadane ◽  
Anthony O'Hagan
Keyword(s):  
Statistical Methods ◽  
Probability Distributions

Survival (time to event) data: median survival times

BMJ ◽  
2011 ◽  
Vol 343 (aug10 3) ◽  
pp. d4890-d4890 ◽  
Author(s):  
P. Sedgwick ◽  
K. Joekes
Keyword(s):  
Survival Time ◽  
Median Survival ◽  
Event Data ◽  
Survival Times ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Keivan Sadeghzadeh ◽  
Nasser Fard
Keyword(s):  
Failure Time ◽  
High Dimensional ◽  
Complex Data ◽  
Redundant Information ◽  
Event Data ◽  
Selection Methods ◽  
Time To Event ◽  
Time To Event Data ◽  
Preventive Actions ◽  
Censored Observations

Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.

scholarly journals A framework for inserting visually-supported inferences into geographical analysis workflow: application to road safety research

2022 ◽  
Author(s):  
Roger Beecham ◽  
Robin Lovelace
Keyword(s):  
Data Analysis ◽  
Medical Research ◽  
Road Safety ◽  
Categorical Variables ◽  
Complex Data ◽  
Geographical Analysis ◽  
Policy Interventions ◽  
Safety Research ◽  
Analysis Workflow ◽  
Road Crash

Road safety research is a data-rich field with large social impacts. Like in medical research, the ambition is to build knowledge around risk factors that can save lives. Unlike medical research, road safety research generates empirical findings from messy observational datasets. Records of road crashes contain numerous intersecting categorical variables, dominating patterns that are complicated by confounding and, when conditioning on data to make inferences net of this, observed effects that are subject to uncertainty due to diminishing sample sizes. We demonstrate how visual data analysis approaches can inject rigour into exploratory analysis of such datasets. A framework is presented whereby graphics are used to expose, model and evaluate spatial patterns in observational data, as well as protect against false discovery. The framework is supported through an applied data analysis of national crash patterns recorded in STATS19, the main source of road crash information in Great Britain. Our framework moves beyond typical depictions of exploratory data analysis and helps navigate complex data analysis decision spaces typical in modern geographical analysis settings, generating data-driven outputs that support effective policy interventions and public debate.

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