+44 (0) 20 8697 3377  
Home About Us Charities Sign in or Register info@timberlake.co.uk
Software Consultancy Training Support Shop
Select Region: 
 
London UK » Sep 2014 » Stata » Bookmark and Share

2014 Stata Autumn School:
Advanced Courses in Statistical Analysis using Stata

Date:
Location:
Duration:
Software:
Topics (Level):

Delivered by:



Course Code:
1-5 September 2014
Cass Business School, City University London, UK (View map)
5 x 1-day courses
Stata
Statistics - Various Methods / Applications
(Intermediate / Advanced)
Dr. Ula Nur, London School of Hygiene & Tropical Medicine (LSHTM) (View profile)
Dr. Jonathan Bartlett, LSHTM
Dr. Bianca De Stavola, LSHTM (View profile)
SAS-133-UK
Cass Business School, City University London Stata  Stata 13    Timberlake Consultants | Statistics | Econometrics | Forecasting
Overview Agenda Prerequisites Testimonials Prices Registration Terms & Conditions  

Overview

Timberlake Consultants invite you to attend the 2014 London Stata Autumn School to be held at Cass Business School, London, UK between 1-5 September 2014.

The Stata Autumn School comprises a series of five, 1-day courses delivered by experienced biostatisticians: Dr. Jonathan Bartlett, Dr. Ula Nur and Dr. Bianca De Stavola, all of the London School of Hygiene and Tropical Medicine (LSHTM). Each course will include an initial introduction to the topic followed by hands-on examples. There will be plenty of time dedicated to interpretation of the results, discussion of assumptions and comparison of approaches.

This is a great opportunity for students, academics and professionals to expand their statistical skills and learn how they can apply statistics from biostatisticians at the forefront of their specialist fields.

Timberlake Consultants are the official Stata 13 distributors to the UK, Ireland, Middle East, North Africa, Spain, Portugal, Poland and Brazil.


 

Back to top 

Agenda

Course 1: Dealing with Missing Data

Date: Monday 1 September 2014
Delivered by: Dr. Jonathan Bartlett & Dr. Ula Nur, London School of Hygiene & Tropical Medicine

We begin by illustrating with a simple dataset the adverse consequences missing data can have on inferences. Next we give an intuitive explanation of Rubin’s classification scheme for missingness mechanisms (MCAR, MAR, MNAR), and explore how missingness mechanisms can be described and investigated using Stata. We then move on to a brief discussion of the deficiencies in several commonly used ad-hoc approaches to handling missing data before we introduce the method of multiple imputation (MI), a principled approach for handling missing data under the MAR assumption. Both joint model and chained equations imputation will be described, and we will apply these to data using Stata 12’s new MI commands. We briefly introduce an alternative approach to handling missing data, that of inverse probability weighting, and illustrate how this is readily performed in Stata, and conclude by emphasising the important role of sensitivity analyses when analysing partially observed datasets.

  • Session 1: Impacts of missing data, classifying missingness mechanisms and ad-hoc methods.
  • Session 2: An introduction to multiple imputation (MI) (single variable).
  • Session 3: Multiple imputation for multiple variables (chained equations).
  • Session 4: Inverse probability weighting and sensitivity analyses after MI.

Course 2: Analysis of Survey Data

Date: Tuesday 2 September 2014
Delivered by: Dr. Ula Nur, LSHTM

We will describe the general statistical techniques that apply to almost all forms of survey data. We will start by introducing the most common sampling designs used in collecting survey data, such as simple random, cluster and stratified sampling designs. Their main features, such as sampling weights, clustering and stratification will be reviewed. The course will cover one, two and multiple stage survey designs, and the three variance estimators implemented in Stata’s survey estimation commands. We will introduce the svyset command which declares the data to be complex survey data, specifies the variables that identify the survey design characteristics, and the default method for variance estimation. The course will also cover the estimation approaches using the “svy:” prefix, which are implemented in the Stata survey family of commands. Lastly we will cover post-stratification, a method for adjusting sampling weights to account for underrepresented groups in the population, and the analysis of strata with one sampling unit and those with certainty units. Examples and exercises will use datasets from the Stata Survey Documentation.

  • Session 1: Sampling design characteristics.
  • Session 2: Special types of sampling units.
  • Session 3: Poststratification and Regression with survey data.
  • Session 4: Variance estimation.

Course 3: Survival Analysis

Date: Wednesday 3 September 2014
Delivered by: Dr. Ula Nur, LHSTM

We will discuss various approaches to the analysis of time-to-event data. These are data that arise from following up individuals until a particular event is observed, or until their follow-up is interrupted (i.e. time is censored). Hence, in this setting, the outcome of interest consists of two pieces of information: the time which the subject spends in the study and what happens at the end of this time. We will first introduce the powerful stset command which declares the key survival variables. We will then discuss censoring mechanisms, and the estimation and comparison of survival curves using the Kaplan-Meier, the life-table (actuarial) method, and the log-rank test. The course will also cover estimation of the cumulative hazard function using the Nelson-Aalen estimator. The two most commonly used multivariable models for survival analysis - Cox and Poisson models - will be introduced and compared using real data examples taken from clinical and epidemiological studies.

  • Session 1: Introduction to survival analysis.
  • Session 2: Kaplan-Meier survival curves and the log-rank test.
  • Session 3: The Cox proportional hazards model.
  • Session 4: Checking the proportional hazards assumption.

Course 4: Analysis of Hierarchical Data

Date: Thursday 4 September 2014
Delivered by: Dr. Bianca De Stavola, LHSTM

We will introduce the course with examples of settings where the usual assumption of independent units of analysis does not hold. If this dependency is ignored, any subsequent inferences are potentially invalid. Dependency therefore must be dealt with. We will discuss alternative approaches to achieve this, with the focus on methods that explicitly specify the nature of the dependency, i.e. mixed effects models. The alternative generalised estimating equations approach will also be briefly described. The focus will be on continuous outcomes when there are only two levels of aggregation. As mixed effects models are a development of ANOVA and the linear regression model this is where we will start. We will then introduce random intercept models, with and without covariates, using the xtmixed command, and then more general random coefficient models. Throughout we will discuss assumptions and ways to assess the appropriateness of the fitted model. An example of modelling individual measurement repeated over time (“longitudinal data” in biostatistics) will conclude the day.

  • Session 1: Impact of dependency, choice of strategies and revision of linear regression.
  • Session 2: The random intercept model.
  • Session 3: The random coefficient model.
  • Session 4: Models for longitudinal data.

Course 5: Structural Equation Modelling

Date: Friday 5 September, LSHTM
Delivered by: Dr. Bianca De Stavola, LHSTM

In this course we will give a brief introduction to generalisations of standard regression models that deal with multivariate outcomes, i.e. structural equation models (SEM). These models include path analysis and factor analysis. We will start by introducing path analysis, i.e. the joint analysis of an outcome and any of its predictors which we also wish to model. The predictors that are also modelled are denoted “intermediate” outcomes, while the main outcome of interest is termed “distal”. We will discuss and fit path analytical models using the command sem on data taken from life course epidemiology. We will also introduce the concepts of direct and indirect effects as used in the SEM literature, highlighting their limitations. Factor analysis will be introduced as an approach to deal with measurement error in a variable of interest when there are error-prone observations available. Such models can again be fitted using sem. Examples will be used, and assumptions and generalizations discussed. More general SEMs where a path model includes latent variables will conclude the day.

  • Session 1: From univariate to multivariate models: notation and diagrams.
  • Session 2: Path regression.
  • Session 3: Confirmatory Factor Analysis.
  • Session 4: Linear structural equation modelling.

Learning Ratios

  • Course 1: 50% theory and 50% practical.
  • Course 2: 45% theory and 55% practical.
  • Course 3: 45% theory and 55% practical.
  • Course 4: 50% theory and 50% practical.
  • Course 5: 50% theory and 50% practical.

Pre-course Readings

  • Course 1:
    • Sterne, J., et al., (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls, 338: b2393, BMJ.
    • Schafer, J. L., (1999). Multiple imputation: a primer, 8: 3-15, SMMR.
  • Course 2:
  • Course 3:
    • StataCorp, (2013). Survival Analysis and Epidemiological Tables, Stata Press.
    • Kleinbaum, D., and Klein, M., (2011). Survival Analysis, a Self-Learning Text, Springer.
  • Course 4:
  • Course 5:
    • StataCorp, (2013). Structural Equation Modeling, Stata Press. (Stata 13 or Stata 12 release).
    • Kleinbaum, D., and Klein, M., (2011). Survival Analysis, a Self-Learning Text, Springer.

Suggested Readings

  • Course 1:
    • Van Buuren, S., (2007). Multiple imputation of discrete and continuous data by fully conditional specification, 16: 219-242, SMMR.
    • Carpenter, J. R., & Kenward, M. G., (2012). Multiple imputation and its application, Wiley.
  • Course 2:
    • Levy, P., and Lemeshow, S., (1999). Sampling of Populations, Wiley.
  • Course 3:
  • Course 4:
  • Course 5:
    • Introductory: Kline, R. B., (2004). Principles and Practice of Structural Equation Modeling, 2nd Ed., New York: Guildford.
    • Advanced: Skrondal, A., and Rabe-Hesketh, S., (2004). Generalized Latent Variable Modeling, Boca Raton, Fla: Chapman.

 

Back to top 

Prerequisites

Course 1: Dealing with Missing Data

Participants should have a working knowledge of STATA, and in particular be familiar with regression models, such as linear and logistic regression, and their interpretation. No knowledge regarding missing data techniques will be assumed.

Course 2: Analysis of Survey Data

Knowledge of Stata is not required, but attendees are assumed to have some statistical knowledge, such as what is typically covered in an introductory statistics course. Participants should be computer literate, able to manage files and familiar with Microsoft Windows.

Course 3: Survival Analysis

Knowledge of Stata is not required, but attendees are assumed to have some statistical knowledge, such as what is typically covered in an introductory statistics course. Participants should be computer literate, able to manage files and familiar with Microsoft Windows.

Course 4: Analysis of Hierarchical Data

Basic knowledge of Stata required and familiarity with linear regression models and the regress command in Stata.

Course 5: Structural Equation Modelling

Basic knowledge of Stata required and familiarity with linear regression models and the regress command in Stata.

 

Back to top 

Testimonials

Coming soon.

 

Back to top 

Prices

Cost (per participant):

Registration type Price (inc. VAT)
Non-academic (any 1-day) £480.00 Purchase Training Button
Non-academic (any 2-days) £900.00 Purchase Training Button
Non-academic (any 3-days) £1,140.00 Purchase Training Button
Non-academic (any 4-days) £1,320.00 Purchase Training Button
Non-academic (all 5-days) £1,440.00 Purchase Training Button
Academics & Students (any 1-day) £180.00 Purchase Training Button
Academics & Students (any 2-days) £336.00 Purchase Training Button
Academics & Students (any 3-days) £480.00 Purchase Training Button
Academics & Students (any 4-days) £600.00 Purchase Training Button
Academics & Students (all 5-days) £660.00 Purchase Training Button
Click here for Pricing FAQs »
  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, laptops can be hired for a fee of £10.00 (ex. VAT) per day).
  • If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.

The number of delegates is restricted. Please register early to guarantee your place.

 

Back to top 

Registration

We welcome delegates to find out more and register for the course by contacting our sales and training team either by email: training@timberlake.co.uk, phone: +44 (0) 20 8697 3377 or by filling out an online registration form.

 

Back to top 

Terms & Conditions

For full Training Courses Terms & Conditions please click here.

Payment of course fees required prior to the course start date.

Registration closes 5-calendar days prior to the start of the course.

  • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
  • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
  • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.



 

Back to top 

Return to:  Training Calendar  |  Home



Last modified: 2014-03-27 15:04:47
Training | Headlines

   
Software Consultancy Training News Shop Support
Website terms & conditions  Privacy Policy   Contact Us   Sitemap 
Timberlake Consultants Limited
B3 Broomsleigh Business Park
Worsley Bridge Road
London SE26 5BN
United Kingdom

Telephone: +44 (0) 20 8697 3377
Fax:+44 (0) 20 8697 3388
Email: info@timberlake.co.uk

© Copyright 2014 Timberlake Consultants Limited. All rights reserved.

Follow us:
Newsletter registration
Join