# Survival Analysis Using SPSS. By Hui Bian Office for Faculty Excellence

Size: px
Start display at page:

Download "Survival Analysis Using SPSS. By Hui Bian Office for Faculty Excellence"

## Transcription

1 Survival Analysis Using SPSS By Hui Bian Office for Faculty Excellence

2 Survival analysis What is survival analysis Event history analysis Time series analysis When use survival analysis Research interest is about time-to-event and event is discrete occurrence. Examples of survival analysis Duration to the hazard of death Adoption of an innovation in diffusion research Marriage duration Characteristics of survival analysis At any time point, events may occur Factors influence events include two types: time-constant and time-dependent (age).

3 Survival analysis Survival analysis focuses on hazard function Hazard: the event of interest occurring Hazard might be death, engine breakdown, adoption of innovation, etc. Hazard rate: is the instantaneous probability of the given event occurring at any point in time. It can be plotted against time on the X axis, forming a graph of the hazard rate over time. Hazard function: the equation that describe this plotted line is the hazard function. Hazard ratio: also called relative risk: Exp(B) in SPSS.

4 Survival analysis Type of survival analysis Nonparametric: no assumption about the shape of hazard function. Hazard function is estimated based on empirical data, showing change over time, for example, Kaplan-Meier survival analysis. Semi-parametric: no assumption about the shape of hazard function, but make assumption about how covariates affect the hazard function, for example: Cox regression Parametric: specify the shape of baseline hazard function and covariates effects on hazard function in advance. Maximum likelihood method Used when time is itself considered a meaningful independent variable. Used for predictive modeling Software: Stata

5 Survival analysis Terms Events: what terminates an episode (such as death, adoption of an innovation), it is the change which causes the subject to transition from one state to another. Durations: the number of time units an individual spends in a given state. Dependent: probability of an event. Survival function, s(t): is the cumulative frequency of the proportion of the sample Not experiencing the event by time t. In another word, it is the probability of event will NOT occur until time t. Censored cases: data are censored if events start before (leftcensored) or ended after (right-censored) the period of observation.

6 Survival analysis Censored cases

7 Survival analysis Censored cases: unique characteristics of survival analysis. For some cases, the event simply doesn t occur before the end of study. For some cases, they drop out from the study for reasons unrelated to the study. For some cases, we lost track of their status sometime before the end of the study.

8 Survival analysis Outline of topics Life tables Kaplan-Meier Cox regression Cox regression with a time-dependent covariate

9 Life tables Life Tables is a descriptive procedure for examining the distribution of time-to-event variables. We also can compare the distribution by levels of a factor variable. The basic idea of life tables is to subdivide the period of observation into smaller time intervals. Then the probability from each of the intervals are estimated.

10 Life tables Variables Time variable (duration variable): must be a continuous variable. Status variable: binary or categorical variable, represents the event of interest. Factor variable: categorical variable. Assumption The probability for the event of interest should depend only on time. Cases that enter the study at different times should behave similarly. No systematic differences between censored and uncensored cases

11 Life tables Example (from IBM SPSS 20.0): data file name: telco Examine distribution of customer time to churn by customer category. Time variable: tenure (in month) Status variable: churn (binary: 1 = Churn, 0 = Not churn) Factor: custcat (four categories) Go to Analyze > Survival > Life Tables

12 Life tables Run analysis

13 Life tables Click Options 1. Survival: display the cumulative survival function on a linear scale 2. Hazard: display the cumulative hazard function on a linear scale.

14 Life tables SPSS Outputs: life table

15 Life tables SPSS outputs: life table Interval Start Time. The beginning value for each interval. Each interval extends from its start time up to the start time of the next interval. Number Withdrawing during Interval: the number of censored cases in this interval. These are still active customers, but so far they have not been customers longer than the time period indicated by this interval. Number Exposed to Risk. The number of surviving cases minus one half the censored cases. This is intended to account for the effect of the censored cases.

16 Life tables SPSS outputs: life table Number of Terminal Events. The number of cases that experience the terminal event in this interval. These are customers with churn = 1. Proportion Terminating. The ratio of terminal events to the number exposed to risk (10/264.5=0.0378). Proportion Surviving. One minus the proportion terminating.

17 Life tables SPSS Outputs: life table

18 Life tables SPSS Outputs: life table Cumulative Proportion Surviving at End of Interval. The proportion of cases surviving from the start of the table to the end of the interval ( )/266= (second row). Probability Density. An estimate of the probability of experiencing the terminal event during the interval. Hazard Rate. An estimate of experiencing the terminal event during the interval, conditional upon surviving to the start of the interval.

19 Life tables SPSS Outputs: life table The greatest number and proportion of terminal events occur within the first year, which suggests that customers should be monitored more closely during their first year to be sure of their satisfaction with the company's service.

20 Life tables SPSS Outputs: survival function 1.The horizontal axis shows the time to event. The vertical axis shows the probability of survival. 2. Any point on the survival curve shows the probability that a customer of a given service category will remain a customer past that time. 3. Total service and Basic service customers have the lowest survival curves, and E- service customers have lower curves than Plus service customers.

21 Life tables SPSS Outputs 1.Wilcoxon test is used to compare survival distribution among groups, with the test statistic based on differences in group mean scores. 2. Since the significance value of the test is less than 0.05, we conclude that the survival curves are different across the group. 3. Pairwise comparisons show which two groups are significantly different in survival curves.

22 Kaplan-Meier procedure The Kaplan-Meier procedure is a method of estimating time-to-event models in the presence of censored cases. A descriptive procedure for examining the distribution of time-to-event variables. We also can compare the distribution by levels of a factor variable or produce separate analyses by levels of a stratification variable. Censored cases (right-censored cases) are those for which the event of interest has not yet happened.

23 Kaplan-Meier procedure Assumptions Probabilities for the event of interest should depend only on time after the initial event without covariates effects. Cases that enter the study at different times (for example, patients who begin treatment at different times) should behave similarly. Censored and uncensored cases behave the same. If, for example, many of the censored cases are patients with more serious conditions, your results may be biased.

24 Kaplan-Meier procedure Example (from IBM SPSS 20.0) : data file: pain_medication A pharmaceutical company is developing an anti-inflammatory medication for treating chronic arthritic pain. The research interest is the time it takes for the drug to take effect and how it compares to an existing drug. Shorter times to effect are considered better. Event: drug takes effect

25 Kaplan-Meier procedure Variables Time variable (duration variable): must be a continuous variable Status variable: categorical or continuous variable, represents the event of interest (drug has effect or not). Factor variable: categorical variable, represents a causal effect (type of treatment for example). Stratification variable: categorical variable.

26 Kaplan-Meier procedure We have Factor variable: Treatment (0 = New drug, 1 = old drug), Status variable: status ( 0 = censored, 1 = take effect), Time variable: time We want to compare the effect of two different drugs. Null hypothesis: whether survival function is the same between different levels of the factor variable (between old and new drug).

27 Kaplan-Meier procedure Go to Analyze > Survival > Kaplan-Meier

28 Kaplan-Meier procedure Analyze data

29 Kaplan-Meier procedure Click Compare Factor Log rank: Tests equality of survival functions by weighting all time points the same. Breslow: Tests equality of survival functions by weighting all time points by the number of cases at risk at each time point. Tarone-Ware: Tests equality of survival functions by weighting all time points by the square root of the number of cases at risk at each time point.

30 Kaplan-Meier procedure Compare factor Pooled over strata: a single test is computed for all factor levels, testing for equality of survival function across all levels of the factor variable. Pairwise over strata: a separate test is computed for each pair of factor levels when a pooled test shows non-equality of survival functions. For each stratum: a separate test is computed for group formed by the stratification variable. Pairwise for each stratum: a separate test is computed for each pair of factor variable, for each stratum of the stratification variable.

31 Kaplan-Meier procedure Click Options

32 Kaplan-Meier procedure SPSS outputs: survival table The survival table is a descriptive table that details the time until the drug takes effect. The table is sectioned by each level of Treatment, and each observation occupies its own row in the table. The table is very large.

33 Kaplan-Meier procedure Survival table Time. The time at which the event or censoring occurred. Status. Indicates whether the case experienced the terminal event or was censored. Cumulative Proportion Surviving at the Time. The proportion of cases surviving from the start of the table until this time. When multiple cases experience the terminal event at the same time, these estimates are printed once for that time period and apply to all the cases whose drug took effect at that time.

34 Kaplan-Meier procedure Survival table N of Cumulative Events. The number of cases that have experienced the terminal event from the start of the table until this time. N of Remaining Cases. The number of cases that, at this time, haven t yet to experience the terminal event or be censored.

35 Kaplan-Meier procedure Means and Median for Survival time Since there is a lot of overlap in the confidence intervals, it is unlikely that there is much difference in the "average" survival time. If confidence intervals do not overlap between levels, differences in effect on time to event can be inferred.

36 Kaplan-Meier procedure Overall comparison This table provides overall tests of the equality of survival times across groups. Since the significance values of the tests are all greater than 0.05, there is no statistically significant difference between two treatments in survival time.

37 Kaplan-Meier procedure Survival function 1. The survival curves give a visual representation of the life tables. 2. The horizontal axis shows the time to event. In this plot, drops in the survival curve occur whenever the medication takes effect in a patient. 3. The vertical axis shows the probability of survival (probability of not experience the treatment effect).

38 Cox regression The Cox Regression procedure is useful for modeling the time to a specified event, based upon the values of given covariates. One or more covariates are used to predict a status (event). The central statistical output is the hazard ratio. Data contain censored and uncensored cases. Similar to logistic regression, but Cox regression assesses relationship between survival time and covariates.

39 Cox regression Terms Status variable: the dependent in Cox regression, should be binary variable. Time variable: measures duration to the event defined by the status variable (continuous or discrete). Covariates: independent/predictor variables. They can be categorical or continuous. They also can be time-fixed or timedependent. Interaction terms Categorical covariates: SPSS automatically convert them into a set of dummy variables, omitting one category.

40 Cox regression Example (from SPSS): data file: telco Use Cox Regression to determine which attributes are associated with shorter "time to churn. Time variable: tenure (month with services) Status variable: churn (0 = No, 1 = Yes). Covariates: age, marital, address, employ, retire, gender, reside, and custcat Go to Analyze > Survival > Cox Regression

41 Cox regression Run Cox regression

42 Cox regression Click Categorical

43 Cox regression Click Plots

44 Cox regression Click Options

45 Cox regression SPSS Outputs

46 Cox regression SPSS Outputs

47 Cox regression SPSS Outputs: variables in the equation Exp(B), which can be interpreted as the predicted change in the hazard for a unit increase in the predictor. For binary covariates, hazard ratio is the estimate of the ratio of the hazard rate in one group to the hazard rate in another group. The value of Exp(B) for marital means that the churn hazard for an unmarried customer is times that of a married customer. The value of Exp(B) for address means that the churn hazard is reduced by 100% (100% 0.943)=5.7% for each year a customer has lived at the same address.

48 Cox regression SPSS Outputs This table displays the average value of each predictor variable, plus a pattern for each level of custcat. The four patterns correspond to each of the customer types, each with otherwise "average" predictor values.

49 Cox regression SPSS Outputs: survival function The basic survival curve is a visual display of the modelpredicted time to churn for the "average" customer. The horizontal axis shows the time to event. The vertical axis shows the probability of survival.

50 Cox regression SPSS Outputs 1. The plots show the effect of customer category. 2. Total service and Basic service customers have lower survival curves because, they are more likely to have shorter times to churn.

51 Cox regression SPSS Outputs 1. The horizontal axis shows the time to event. 2. The vertical axis shows the cumulative hazard, equal to the negative log of the survival probability.

52 Cox regression with a time-dependent covariate Time-dependent covariates Simple product of the time variable and the covariate. Some variables may have different values at different time periods but aren't systematically related to time. In such cases, you need to define a segmented time-dependent covariate, which can be done using logical expressions.

53 Cox regression with a time-dependent covariates Example (from IBM SPSS 20.0): data file: recidivism A government law enforcement agency is concerned about recidivism rates in their area of jurisdiction. One of the measures of recidivism is the time until second arrest for offenders. The agency would like to model time to rearrest using Cox Regression, but are worried the proportional hazards assumption is invalid across age categories. Go to Analyze > Survival > Cox with time-dependent covariate

54 Cox regression with a time-dependent covariate Run analysis: the product of variable T_ and variable age

55 Cox regression with a time-dependent covariate Click Model and back to Cox regression window Dependent variable: arrest2 Time variable: time Covariates: age and new product variable: T_cov

56 Cox regression with a time-dependent covariate SPSS outputs The time-dependent covariate has a significance value less than 0.05, which means it contributes to the model, but the value of the coefficient is very small. We found that the effect of age on recidivism is time-dependent, and added a term to the model that helps to account for that dependence.

57

### Tips for surviving the analysis of survival data. Philip Twumasi-Ankrah, PhD

Tips for surviving the analysis of survival data Philip Twumasi-Ankrah, PhD Big picture In medical research and many other areas of research, we often confront continuous, ordinal or dichotomous outcomes

### Introduction. Survival Analysis. Censoring. Plan of Talk

Survival Analysis Mark Lunt Arthritis Research UK Centre for Excellence in Epidemiology University of Manchester 01/12/2015 Survival Analysis is concerned with the length of time before an event occurs.

### Binary Logistic Regression

Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including

### Statistics in Retail Finance. Chapter 6: Behavioural models

Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics:- Behavioural

### Introduction to Event History Analysis DUSTIN BROWN POPULATION RESEARCH CENTER

Introduction to Event History Analysis DUSTIN BROWN POPULATION RESEARCH CENTER Objectives Introduce event history analysis Describe some common survival (hazard) distributions Introduce some useful Stata

### SUMAN DUVVURU STAT 567 PROJECT REPORT

SUMAN DUVVURU STAT 567 PROJECT REPORT SURVIVAL ANALYSIS OF HEROIN ADDICTS Background and introduction: Current illicit drug use among teens is continuing to increase in many countries around the world.

### SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg

SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg IN SPSS SESSION 2, WE HAVE LEARNT: Elementary Data Analysis Group Comparison & One-way

### Survey, Statistics and Psychometrics Core Research Facility University of Nebraska-Lincoln. Log-Rank Test for More Than Two Groups

Survey, Statistics and Psychometrics Core Research Facility University of Nebraska-Lincoln Log-Rank Test for More Than Two Groups Prepared by Harlan Sayles (SRAM) Revised by Julia Soulakova (Statistics)

### LOGISTIC REGRESSION ANALYSIS

LOGISTIC REGRESSION ANALYSIS C. Mitchell Dayton Department of Measurement, Statistics & Evaluation Room 1230D Benjamin Building University of Maryland September 1992 1. Introduction and Model Logistic

### SPSS Explore procedure

SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stem-and-leaf plots and extensive descriptive statistics. To run the Explore procedure,

### Lecture - 32 Regression Modelling Using SPSS

Applied Multivariate Statistical Modelling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 32 Regression Modelling Using SPSS (Refer

### Tests for Two Survival Curves Using Cox s Proportional Hazards Model

Chapter 730 Tests for Two Survival Curves Using Cox s Proportional Hazards Model Introduction A clinical trial is often employed to test the equality of survival distributions of two treatment groups.

### ˆx wi k = i ) x jk e x j β. c i. c i {x ik x wi k}, (1)

Residuals Schoenfeld Residuals Assume p covariates and n independent observations of time, covariates, and censoring, which are represented as (t i, x i, c i ), where i = 1, 2,..., n, and c i = 1 for uncensored

### How to interpret scientific & statistical graphs

How to interpret scientific & statistical graphs Theresa A Scott, MS Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott 1 A brief introduction Graphics:

### Regression Modeling Strategies

Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions

### SPSS Multivariable Linear Models and Logistic Regression

1 SPSS Multivariable Linear Models and Logistic Regression Multivariable Models Single continuous outcome (dependent variable), one main exposure (independent) variable, and one or more potential confounders

### Biostatistics: A QUICK GUIDE TO THE USE AND CHOICE OF GRAPHS AND CHARTS

Biostatistics: A QUICK GUIDE TO THE USE AND CHOICE OF GRAPHS AND CHARTS 1. Introduction, and choosing a graph or chart Graphs and charts provide a powerful way of summarising data and presenting them in

### Introduction to Survival Analysis

John Fox Lecture Notes Introduction to Survival Analysis Copyright 2014 by John Fox Introduction to Survival Analysis 1 1. Introduction I Survival analysis encompasses a wide variety of methods for analyzing

### SPSS Notes (SPSS version 15.0)

SPSS Notes (SPSS version 15.0) Annie Herbert Salford Royal Hospitals NHS Trust July 2008 Contents Page Getting Started 1 1 Opening SPSS 1 2 Layout of SPSS 2 2.1 Windows 2 2.2 Saving Files 3 3 Creating

### Gamma Distribution Fitting

Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics

### Simple Linear Regression One Binary Categorical Independent Variable

Simple Linear Regression Does sex influence mean GCSE score? In order to answer the question posed above, we want to run a linear regression of sgcseptsnew against sgender, which is a binary categorical

### Correctly Compute Complex Samples Statistics

SPSS Complex Samples 16.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample

### Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

### Paper Let the Data Speak: New Regression Diagnostics Based on Cumulative Residuals

Paper 255-28 Let the Data Speak: New Regression Diagnostics Based on Cumulative Residuals Gordon Johnston and Ying So SAS Institute Inc. Cary, North Carolina, USA Abstract Residuals have long been used

### Competing-risks regression

Competing-risks regression Roberto G. Gutierrez Director of Statistics StataCorp LP Stata Conference Boston 2010 R. Gutierrez (StataCorp) Competing-risks regression July 15-16, 2010 1 / 26 Outline 1. Overview

### Interpretation of Somers D under four simple models

Interpretation of Somers D under four simple models Roger B. Newson 03 September, 04 Introduction Somers D is an ordinal measure of association introduced by Somers (96)[9]. It can be defined in terms

### Event history analysis

Event history analysis Jeroen K. Vermunt Department of Methodology and Statistics Tilburg University 1 Introduction The aim of event history analysis is to explain why certain individuals are at a higher

### IBM SPSS Complex Samples 22

IBM SPSS Complex Samples 22 Note Before using this information and the product it supports, read the information in Notices on page 51. Product Information This edition applies to version 22, release 0,

### Introduction to Quantitative Methods

Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................

### SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES

SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR

### A Guide for a Selection of SPSS Functions

A Guide for a Selection of SPSS Functions IBM SPSS Statistics 19 Compiled by Beth Gaedy, Math Specialist, Viterbo University - 2012 Using documents prepared by Drs. Sheldon Lee, Marcus Saegrove, Jennifer

### Lecture 15 Introduction to Survival Analysis

Lecture 15 Introduction to Survival Analysis BIOST 515 February 26, 2004 BIOST 515, Lecture 15 Background In logistic regression, we were interested in studying how risk factors were associated with presence

### ANNOTATED OUTPUT--SPSS Logistic Regression

Logistic Regression Logistic regression is a variation of the regression model. It is used when the dependent response variable is binary in nature. Logistic regression predicts the probability of the

### Semester 1 Statistics Short courses

Semester 1 Statistics Short courses Course: STAA0001 Basic Statistics Blackboard Site: STAA0001 Dates: Sat. March 12 th and Sat. April 30 th (9 am 5 pm) Assumed Knowledge: None Course Description Statistical

### Univariable survival analysis S9. Michael Hauptmann Netherlands Cancer Institute Amsterdam, The Netherlands

Univariable survival analysis S9 Michael Hauptmann Netherlands Cancer Institute Amsterdam, The Netherlands m.hauptmann@nki.nl 1 Survival analysis Cohort studies (incl. clinical trials & patient series)

### Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)

Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared

### LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016

UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe

### Descriptive Statistics

Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

### Logistic Regression. Introduction. The Purpose Of Logistic Regression

Logistic Regression...1 Introduction...1 The Purpose Of Logistic Regression...1 Assumptions Of Logistic Regression...2 The Logistic Regression Equation...3 Interpreting Log Odds And The Odds Ratio...4

### , then the form of the model is given by: which comprises a deterministic component involving the three regression coefficients (

Multiple regression Introduction Multiple regression is a logical extension of the principles of simple linear regression to situations in which there are several predictor variables. For instance if we

### Scatter Plots with Error Bars

Chapter 165 Scatter Plots with Error Bars Introduction The procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each

### For example, enter the following data in three COLUMNS in a new View window.

Statistics with Statview - 18 Paired t-test A paired t-test compares two groups of measurements when the data in the two groups are in some way paired between the groups (e.g., before and after on the

### CALCULATIONS & STATISTICS

CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

### II. DISTRIBUTIONS distribution normal distribution. standard scores

Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

### 1/2/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2

PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 When and why do we use logistic regression? Binary Multinomial Theory behind logistic regression Assessing the model Assessing predictors

### Minitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab Step-By-Step

Minitab Guide This packet contains: A Friendly Guide to Minitab An introduction to Minitab; including basic Minitab functions, how to create sets of data, and how to create and edit graphs of different

### ANNOTATED OUTPUT--SPSS Simple Linear (OLS) Regression

Simple Linear (OLS) Regression Regression is a method for studying the relationship of a dependent variable and one or more independent variables. Simple Linear Regression tells you the amount of variance

### 11. Analysis of Case-control Studies Logistic Regression

Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

### HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION

HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate

### Survival Analysis And The Application Of Cox's Proportional Hazards Modeling Using SAS

Paper 244-26 Survival Analysis And The Application Of Cox's Proportional Hazards Modeling Using SAS Tyler Smith, and Besa Smith, Department of Defense Center for Deployment Health Research, Naval Health

### Ordinal Regression. Chapter

Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe

### The point estimate you choose depends on the nature of the outcome of interest odds ratio hazard ratio

Point Estimation Definition: A point estimate is a onenumber summary of data. If you had just one number to summarize the inference from your study.. Examples: Dose finding trials: MTD (maximum tolerable

### INTRODUCTORY STATISTICS

INTRODUCTORY STATISTICS FIFTH EDITION Thomas H. Wonnacott University of Western Ontario Ronald J. Wonnacott University of Western Ontario WILEY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore

### The first three steps in a logistic regression analysis with examples in IBM SPSS. Steve Simon P.Mean Consulting www.pmean.com

The first three steps in a logistic regression analysis with examples in IBM SPSS. Steve Simon P.Mean Consulting www.pmean.com 2. Why do I offer this webinar for free? I offer free statistics webinars

### IBM SPSS Complex Samples

IBM Software IBM SPSS Complex Samples 20 IBM SPSS Complex Samples Correctly compute complex samples statistics Highlights Produce correct estimates for complex sample data. Increase the precision of your

BIOSTATISTICS QUIZ ANSWERS 1. When you read scientific literature, do you know whether the statistical tests that were used were appropriate and why they were used? a. Always b. Mostly c. Rarely d. Never

### Survival Analysis of Dental Implants. Abstracts

Survival Analysis of Dental Implants Andrew Kai-Ming Kwan 1,4, Dr. Fu Lee Wang 2, and Dr. Tak-Kun Chow 3 1 Census and Statistics Department, Hong Kong, China 2 Caritas Institute of Higher Education, Hong

### When to Use Which Statistical Test

When to Use Which Statistical Test Rachel Lovell, Ph.D., Senior Research Associate Begun Center for Violence Prevention Research and Education Jack, Joseph, and Morton Mandel School of Applied Social Sciences

### Statistical Inference and t-tests

1 Statistical Inference and t-tests Objectives Evaluate the difference between a sample mean and a target value using a one-sample t-test. Evaluate the difference between a sample mean and a target value

### Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS

Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction

### By Hui Bian Office for Faculty Excellence

By Hui Bian Office for Faculty Excellence 1 Email: bianh@ecu.edu Phone: 328-5428 Location: 2307 Old Cafeteria Complex 2 When want to predict one variable from a combination of several variables. When want

### An Introduction to Survival Analysis

An Introduction to Survival Analysis Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda Survival Analysis concepts Descriptive approach 1 st Case Study which types

### Lecture 2 ESTIMATING THE SURVIVAL FUNCTION. One-sample nonparametric methods

Lecture 2 ESTIMATING THE SURVIVAL FUNCTION One-sample nonparametric methods There are commonly three methods for estimating a survivorship function S(t) = P (T > t) without resorting to parametric models:

### Logistic Regression: Basics

Evidence is no evidence if based solely on p value Logistic Regression: Basics Prediction Model: Binary Outcomes Nemours Stats 101 Laurens Holmes, Jr. General Linear Model OUTCOME Continuous Counts Data

### Elementary Statistics. Scatter Plot, Regression Line, Linear Correlation Coefficient, and Coefficient of Determination

Scatter Plot, Regression Line, Linear Correlation Coefficient, and Coefficient of Determination What is a Scatter Plot? A Scatter Plot is a plot of ordered pairs (x, y) where the horizontal axis is used

### Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.)

Unit 12 Logistic Regression Supplementary Chapter 14 in IPS On CD (Chap 16, 5th ed.) Logistic regression generalizes methods for 2-way tables Adds capability studying several predictors, but Limited to

### 11/20/2014. Correlational research is used to describe the relationship between two or more naturally occurring variables.

Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection

### ID X Y

Dale Berger SPSS Step-by-Step Regression Introduction: MRC01 This step-by-step example shows how to enter data into SPSS and conduct a simple regression analysis to develop an equation to predict from.

### Simple Linear Regression Excel 2010 Tutorial

Simple Linear Regression Excel 2010 Tutorial This tutorial combines information on how to obtain regression output for Simple Linear Regression from Excel and some aspects of understanding what the output

### Modeling Customer Lifetime Value Using Survival Analysis An Application in the Telecommunications Industry

Paper 12028 Modeling Customer Lifetime Value Using Survival Analysis An Application in the Telecommunications Industry Junxiang Lu, Ph.D. Overland Park, Kansas ABSTRACT Increasingly, companies are viewing

### F. Farrokhyar, MPhil, PhD, PDoc

Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

### The Philosophy of Hypothesis Testing, Questions and Answers 2006 Samuel L. Baker

HYPOTHESIS TESTING PHILOSOPHY 1 The Philosophy of Hypothesis Testing, Questions and Answers 2006 Samuel L. Baker Question: So I'm hypothesis testing. What's the hypothesis I'm testing? Answer: When you're

### The Cox Proportional Hazards Model

The Cox Proportional Hazards Model Mario Chen, PhD Advanced Biostatistics and RCT Workshop Office of AIDS Research, NIH ICSSC, FHI Goa, India, September 2009 1 The Model h i (t)=h 0 (t)exp(z i ), Z i =

### Data Analysis, Research Study Design and the IRB

Minding the p-values p and Quartiles: Data Analysis, Research Study Design and the IRB Don Allensworth-Davies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB

### Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

### LOGIT AND PROBIT ANALYSIS

LOGIT AND PROBIT ANALYSIS A.K. Vasisht I.A.S.R.I., Library Avenue, New Delhi 110 012 amitvasisht@iasri.res.in In dummy regression variable models, it is assumed implicitly that the dependent variable Y

### DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS

DEPARTMENT OF HEALTH AND HUMAN SCIENCES HS900 RESEARCH METHODS Using SPSS Session 2 Topics addressed today: 1. Recoding data missing values, collapsing categories 2. Making a simple scale 3. Standardisation

### Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### Module 9: Nonparametric Tests. The Applied Research Center

Module 9: Nonparametric Tests The Applied Research Center Module 9 Overview } Nonparametric Tests } Parametric vs. Nonparametric Tests } Restrictions of Nonparametric Tests } One-Sample Chi-Square Test

### Comparing two groups (t tests...)

Page 1 of 33 Comparing two groups (t tests...) You've measured a variable in two groups, and the means (and medians) are distinct. Is that due to chance? Or does it tell you the two groups are really different?

### SPSS Workbook 4 T-tests

TEESSIDE UNIVERSITY SCHOOL OF HEALTH & SOCIAL CARE SPSS Workbook 4 T-tests Research, Audit and data RMH 2023-N Module Leader:Sylvia Storey Phone:016420384969 s.storey@tees.ac.uk SPSS Workbook 4 Differences

### CHAPTER 3 COMMONLY USED STATISTICAL TERMS

CHAPTER 3 COMMONLY USED STATISTICAL TERMS There are many statistics used in social science research and evaluation. The two main areas of statistics are descriptive and inferential. The third class of

### SOLUTIONS TO BIOSTATISTICS PRACTICE PROBLEMS

SOLUTIONS TO BIOSTATISTICS PRACTICE PROBLEMS BIOSTATISTICS DESCRIBING DATA, THE NORMAL DISTRIBUTION SOLUTIONS 1. a. To calculate the mean, we just add up all 7 values, and divide by 7. In Xi i= 1 fancy

### T-test & factor analysis

Parametric tests T-test & factor analysis Better than non parametric tests Stringent assumptions More strings attached Assumes population distribution of sample is normal Major problem Alternatives Continue

### Chapter 6: Answers. Omnibus Tests of Model Coefficients. Chi-square df Sig Step Block Model.

Task Chapter 6: Answers Recent research has shown that lecturers are among the most stressed workers. A researcher wanted to know exactly what it was about being a lecturer that created this stress and

### Statistical Significance and Bivariate Tests

Statistical Significance and Bivariate Tests BUS 735: Business Decision Making and Research 1 1.1 Goals Goals Specific goals: Re-familiarize ourselves with basic statistics ideas: sampling distributions,

### Part 3: Methods for Propensity Analysis

Part 3: Methods for Propensity Analysis SMDM Short Course on Propensity Charles Thomas Center for Health Care Research & Policy Outline Definition of the Propensity Score Estimating the Propensity Score

### SPSS: Descriptive and Inferential Statistics. For Windows

For Windows August 2012 Table of Contents Section 1: Summarizing Data...3 1.1 Descriptive Statistics...3 Section 2: Inferential Statistics... 10 2.1 Chi-Square Test... 10 2.2 T tests... 11 2.3 Correlation...

### Choosing the correct statistical test made easy

Classroom Choosing the correct statistical test made easy N Gunawardana Senior Lecturer in Community Medicine, Faculty of Medicine, University of Colombo Gone are the days where researchers had to perform

### Easily Identify Your Best Customers

IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do

### One-Sample t-test. Example 1: Mortgage Process Time. Problem. Data set. Data collection. Tools

One-Sample t-test Example 1: Mortgage Process Time Problem A faster loan processing time produces higher productivity and greater customer satisfaction. A financial services institution wants to establish

### NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )

Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates

### Adequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection

Directions in Statistical Methodology for Multivariable Predictive Modeling Frank E Harrell Jr University of Virginia Seattle WA 19May98 Overview of Modeling Process Model selection Regression shape Diagnostics

### An Application of the Cox Proportional Hazards Model to the Construction of Objective Vintages for Credit in Financial Institutions, Using PROC PHREG

Paper 3140-2015 An Application of the Cox Proportional Hazards Model to the Construction of Objective Vintages for Credit in Financial Institutions, Using PROC PHREG Iván Darío Atehortua Rojas, Banco Colpatria

### Chapter 5 Analysis of variance SPSS Analysis of variance

Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,

### One-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate

1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,