MEAFA Professional Development Workshop on Survival Analysis Using Stata, 1822 July 2011
Brief description of Survival Analysis
Survival analysis is just another name for timetoevent analysis or reliability analysis.
The term survival analysis was coined by biomedical sciences because of the focus on measuring time to death of subjects (hence their 'survival'). Timetoevent analysis is a term mostly used in social sciences where researchers measure the time to an event such as entries or exits to job market, household changes, bankruptcies, equity restructuring etc. Reliability analysis is a term used by the engineering sciences, also sometimes referred to as failure time analysis, because of the measurement of time to failure of a machine element.
Survival analysis spans over many disciplines and many of its methods have been developed more than once, each time being labelled with a different name. The resulting field of survival analysis is replete with jargon: censoring, hazards, delayed entry, competing risks etc. The key to mastering survival analysis is in decoding the jargon and in realizing that survival analysis is not really a distinct field of statistics requiring its own theory. Instead, survival analysis consists of a set of adjustments that need to be made to standard, wellknown analyses. These adjustments, while necessary when your data measures time to failure or some other event of interest, are more easily understood when compared directly to their nonsurvival analogs. This 2day course will be taught in that spirit.
Presenter of Survival Analysis
Rory Wolfe is expert in Stata, Survival Analysis and Panel Data Analysis. He is an established Biostatistician and the PhD Program Coordinator in Epidemiology and Preventive Medicine and the codirector of the Biostatistics Consulting Service at Monash University. He is also an expert at the NHMRC Centre for Clinical Research Excellence in Gait Analysis. He has a long history with Stata and has published in the Stata Technical Bulletin and the Stata Journal, and has contributed opensource Stata commands in the Statistical Software Components library. Rory also runs short courses on Survival Analysis with Stata for the Australian Psychology Society.
Workshop description
You may attend any one or any combination of the following days:
Day 1 (Monday, July 18): Working efficiently with Stata 11 and intro to data management by Demetris Christodoulou, MEAFA General Convener
This day assumes no previous knowledge of Stata. An overall introduction to Stata will be provided and ways to customise/personalise the software will be discussed, as well as the handling of key data structures, the analysis of different types of variables and various introductory data management techniques. Some examples of graphing, tables and the management of output will be presented. The focus of day is working efficiently with reproducible and tractable routines. This day is of interest to those who are new or have limited experience with Stata or want become more efficient in their work. 
Day 2 (Tuesday, July 19): Two parallel sessions  you can choose only one to attend
Introduction to Stata programming by Demetris Christodoulou This day assumes working knowledge of Stata but no knowledge of programming with Stata or with any other software. By the end of this day you will be able to produce fast automated routines for data management, statistical analysis, econometric estimation, creation of tables, graphing etc. This day is appropriate for those who wish to become step up their knowledge of statistical computing and start producing more complex routines with Stata. 
Econometric modelling and statistical testing using Stata by Andrey Vasnev This day assumes familiarity with Stata and a basic understanding of quantitative methods. It uses applications to demonstrate the use of statistical analysis, hypothesis testing and basic econometric modelling for validating assumptions and expectations. This day is of interest to those who wish to know how to apply various quantitative methods using Stata. Detail notes on theory will be provided as background reading. 
N.B.: MEAFA reserves the right to cancel a parallel session in case of low demand. 
Day 3 (Wednesday, July 20): Two parallel sessions  you can choose only one to attend
Graphing with Stata 11 by Demetris Christodoulou This day assumes working knowledge of Stata but no knowledge of graphing with Stata or any other software. The day provides an in depth analysis of Stata's graphing logic, syntax and capabilities. Graphing examples will be demonstrated for a variety of data structures. By the end of this day you should be able to produce informative, robust, complex and beautiful graphs using reproducible routines. If you have no or limited experience with Stata then you are strongly advised to attend Day 1 first. Programming elements from Day 2 will also be used for producing more complex graphs. 
Time series analysis by Richard Gerlach This day assumes working knowledge of Stata and basic knowledge of econometric principles. It details the theory for modelling univariate time series and forecasting, and offers extensive applications using Stata. This day is of interest to those who wish to learn how to model and estimate univariate time series using Stata. Detailed notes on theory will be provided as background reading. 
N.B.: MEAFA reserves the right to cancel a parallel session in case of low demand. 
Days 45 (ThursdayFriday, July 2122): Survival analysis using Stata by Rory Wolfe, Associate Professor, Epidemiology and Preventive Medicine, Monash University.
These two days assume basic knowledge of Stata and working with Stata dofiles. A basic knowledge of standard statistical techniques is also assumed (such as linear/logistic regression). The course will be taught from first principles (see also description on top of this page). Following the introduction to survival analysis, the 2day workshop will breakdown the topic by method: nonparametric analysis, semiparametric analysis and parametric analysis. More advanced topics will be addressed at the end of the second day. Detailed notes, logfiles, dofiles and datasets will be provided outlining all theory and applications. The course will be interactive, use real data, and offer ample opportunity for working exercises to reinforce what is learned. 
Enrollment and Fees
You may attend any one day or any combination of days. See the description of each day to determine which days are of most interest to you. Fees are fixed at $500 per day but the 2days on Survival Analysis go together as a package (prices exclude GST):
 Each one of Day 1, Day 2 and Day 3 at $500 per day
 Days 4 & 5 on Survival Analysis at $1000
Fees include extensive course material, dofiles and data sets, use of computing facilities, temporary use of Stata 11 licenses and full catering.
Numbers are limited and places are reserved on a firstcome firstserved basis following the completion of the online Reservation Form. Successful attendees will be notified shortly after reservation and invoices will be issued accordingly. Due to the limited places, MEAFA maintains a no refund policy following payment. For more information on enrollment and fees contact meafa@econ.usyd.edu.au.
N.B. Proceedings from the workshop go to funding MEAFA PhD scholarships.
Discounts
You may qualify for one of the following discounts:
 35% discount for a restricted number of nonemployed fulltime PhD students.
 10% discount for additional attendees from the same organisation or academic unit.
Venue and computing facilities
The workshop will take place at the computer labs of The University of Sydney Business School, at the ground level of Building H69, cnr Codrington & Rose streets (see interactive map).
PCs and Stata 11 licenses for Microsoft Windows will be provided onsite. It is also possible to to work on your own laptop but you will not be able to access the web. You can also install a temporary onemonth license for Stata 11.
Timetable
All days have the following schedule:
 08:4009:00  Welcome tea and coffee
09:0010:30  Session 1  10:3010:45  Morning break
10:4512:15  Session 2  12:1513:15  Lunch
13:1514:45  Session 3  14:4515:00  Afternoon break
15:0016:30  Session 4  16:3017:00  Buffertime and userspecific questions
Detailed Programme
Day 1 (Monday, 18 July): Working efficiently with Stata 11 and intro to data management 


Session 1: Introduction to Stata 11 environment The Stata environment; configuration; special features; updates; personalised system; obtain help and perform search; Stata syntax; working with dofiles. 

Session 2: Data handling and adding metadata Data formats; Import, export, load and save datasets; simulated datasets; sorting and ordering; review and document the dataset; display formatting; append and merge. 

Session 3: Data structures and types of variables Categorical vs. continuous data; numerical, string and date/time variables; missing data; dummy variables; special purpose variables. 

Session 4: Output management and special features Logs for output; tables; export output; some statistical and estimation commands; prefixes; stored and saved results. 
Day 2 (Tuesday, 19 July): Parallel sessions 


Introduction to Stata programming 
Econometric modelling and statistical testing using Stata 
Session 1: Basics of Stata programming Properly structured dofiles; comments; writing long commands; do vs. run; combination of preserve and restore; the command display; accessing Stata parameters and Stata constants. 
Session 1: Statistical description and linear regression analysis Means, variances and higher order moments; medians and modes; confidence intervals; ordinary least squares; predicted values and residuals; correlation and standardized regression coefficients; hypothesis testing; problems with regression. 
Session 2: It's all about Macros! What is a Stata macro; local macros; global macros; numerical macros; string macros; compound punctuation; macro evaluation; formatting macro output; nested macros. 
Session 2: Multiple regression analysis Multiple regression models; partial effects; variable selection; ttests for individual coefficients; Ftests for sets of coefficients; multicollinearity; interaction effects; intercept and slope dummy variables. 
Session 3: Special features of macros, and loops Incrementing/decrementing macros; combining incrementation with evaluation; macro expansion; foreach loop; forvalues loop; nested loops; return codes. 
Session 3: Statistical description and nonlinear regression functions Graphing the data; modelling nonlinear regression functions; transformations; polynomials and logarithms; interactions (incl. continuous and dummy variables); internal and external validity. 
Session 4: Automating routines and other special features Capturing saved results; macro evaluation with saved results; scalars and precision; creating tables using stored results; the command file; explicit subscripting. 
Session 4: Regression with a binary dependent variable Binary dependent variables and the linear probability model; Probit and Logit regression; estimation and inference in the binary models; applications. 
Day 3 (Wednesday, 20 July): Parallel sessions 


Graphing with Stata 11 
Time series analysis using Stata 
Session 1: Basics of graphing with Stata 11 Dialog boxes vs. dofile routines; inspecting the data prior graphing; reducing the data dimension to speed up graphing; setting range of variation; graph example  histogram; titles; axes; labels; bars; adding notes; the concepts of box, position, line, text, colour and font.  Session 1: Introduction to forecasting and time series Why forecast?; Stata time series structure; describing, graphing time series; smoothing and time series components; data transformations; exponential smoothing and forecasting; forecast accuracy; the concept and application of stationarity; autocorrelation and ACF plots; modelling and forecasting Australian beer production. 
Session 2: Subgroups and overlays Graphing by categorical groups; subgroup options; formatting graph text; using special characters; graph aspect and size; superimposing densities and other graphs; legends for multiple graphs; multiple axes; graph help files; saving and modifying graphs; graph export formats. 
Session 2: Time series modelling and forecasting The autoregressive process (AR); the moving average process (MA); ARMA processes; Basic time series regression; HoltWinters for trends; seasonal HoltWinters; modelling and forecasting electricity production data. 
Session 3: So many graphs! The twoway command; an example  the scattergraph; nonparametric density estimators; parametric density estimators; patterns in variance and nonlinearities; timeseries graphs; panel data graphs. 
Session 3: Integrated and seasonal BoxJenkins models Trends and integration; ARIMA processes; detecting trends and/or mean nonstationarity; ARIMA model forecast behaviour; Seasonal ARIMA models; pure additive and factored models; models for outliers, level shifts and other interventions; modelling and forecasting sales data. 
Session 4: Advanced graphing Using loops for multiple overlays; combining multiple graphs sidebyside; recasting twoway plots; reproducing formatting; dofile options; graph editor recording; existing graph schemes; creating your own graph scheme; the graph editor as a scheme maker; special graphs. 
Session 4: Time series regression and volatility modelling Advanced time series regression; distributed lag models; modelling and forecasting inflation and unemployment data; the concept of conditional heteroskedasticity (CH); ARCH and Generalised ARCH processes (GARCH); modelling and forecasting asset return volatility and ValueatRisk. 
Day 4 (Thursday, 21 July): Survival analysis using Stata, Part A 


Session 1: Introduction to survival analysis The problem of survival analysis; hazards; cumulative hazards; survival functions; censoring; truncation; delayed entry. 

Session 2: Survival analysis with Stata Span data versus snapshot data; the st (survivaltime data) suite of commands; stset; summary statistics: stdes, stsum etc. 

Session 3: Nonparametric analysis Kaplan?Meier curves; Nelson?Aalen curves; estimating the hazard function via smoothing; mean and median survival time; tests of hypotheses. 

Session 4: Semiparametric analysis The Cox regression; basics of stcox; hazard ratios and proportional hazards; estimating baseline functions. 
Day 5 (Friday, 22 July): Survival analysis using Stata, Part B 


Session 1: Semiparametric analysis Stratified models; using stcurve for predicted functions; timevarying covariates/coefficients; model diagnostics. 

Session 2: Parametric analysis The basics of streg; parametric proportionalhazards models; accelerated failure time models; using stcurve for predicted functions; predictions and diagnostics. 

Session 3: Complex data for survival analysis Complex survey data and survival analysis; missing data and multiple imputation with survival analysis. 

Session 4: Advanced survival analysis Frailty models; power and sample size calculations; competing risks. 
N.B. The precise content is subject to minor adjustments.
Reservation Form
Numbers are limited and places are reserved on a firstcome firstserved basis following the completion of the Reservation Form.