Seminars


For further information on any CSM events please e-mail: csm@lshtm.ac.uk.


Centre for Statistical Methodology Seminar
Survival Analysis and Statistical Computing Themes
Friday 31 March 2017, 12:45-2:00pm
LG9, Keppel Street
Multistate survival analysis in Stata
Michael Crowther (University of Leicester)

Abstract: Multi-state models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain a much richer understanding of patient trajectories and how risk factors impact over the entire disease pathway. In this talk, I will introduce some new Stata commands for the analysis of multi-state survival data. This includes msset, a data preparation tool that converts a dataset from wide (one observation per subject, multiple time and status variables) to long (one observation for each transition for which a subject is at risk for). I develop a new estimation command, stms, that allows the user to fit different parametric distributions for different transitions, simultaneously, while allowing for sharing of covariate effects across transitions. Finally, predictms calculates transition probabilities, and many other useful measures of absolute risk, following the fit of any model using streg, stms, or stcox, using either a simulation approach or the Aalen–Johansen estimator. Importantly, predictms also allows different parametric distributions to be specified for different transitions, passed as model objects. I illustrate the software using a dataset of patients with primary breast cancer.

Centre for Statistical Methodology Seminar
Friday 28 April 2017, 12:45-2:00pm
LG9, Keppel Street
Statistical approaches to antibody data analysis for populations on the path of malaria elimination
Nuno Sepulveda (LSHTM)

Abstract: Antibody data is currently being considered as an important epidemiological tool to analyse populations on the path of malaria elimination. The basic idea is to estimate the serological classification of the individuals (seronegative or seropositive) in a sample. One then estimates the number of seropositive individuals in order to gain information about the process of malaria elimination. In absence of a training set where the serological status of the individuals is known in advance, the serological classification of the individuals is typically performed using a Two-Gaussian mixture model. Since malaria elimination implies a decreasing seropositive population over time, it is urgent to identify powerful but easily applicable statistical strategies for this epidemiological setting. With this in mind, I will review the concepts of bimodality, data transformation, model validation and interpretation, which can be instrumental in correctly identifying the seropositive population. All these concepts are illustrated with two data sets from Aneityum (Vanuatu) and Chabahar (Iran), where malaria elimination programmes are currently running.

LSHTM Inaugural Lecture
Thursday 4 May 2017, 5:15pm
John Snow Lecture Theatre, Keppel Street
Missing data: sense & sensitivity
Prof James Carpenter (LSHTM)

Centre for Statistical Methodology Seminar
Friday 26 May 2017, 12:45-2:00pm
LG9, Keppel Street
Title TBC
Suzie Cro (Imperial College London)

26th Bradford Hill Memorial Lecture
Monday 12 June 2017, 5:00pm
John Snow Lecture Theatre, Keppel Street
Improving health by improving trials: from outcomes to recruitment and back again
Prof Paula Williamson (University of Liverpool)
Register here: www.lshtm.ac.uk/bhill2017

Centre for Statistical Methodology Seminar
Friday 30 June 2017, 12:45-2:00pm
LG9, Keppel Street
Title TBC
Rebecca Walwyn (University of Leeds)