Seminars


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


Centre for Statistical Methodology Seminar
Causal Inference & Missing Data and Measurement Error Themes
Friday 30 September 2016, 12:45-2:00pm
LG9, Keppel Street
How should the propensity score be estimated when some confounders are partially observed?
Clemence Leyrat (LSHTM)

Abstract: Propensity score (PS)-based approaches are very popular to estimate causal effects in observational studies at risk of confounding bias. A major issue when estimating the PS is the presence of partially observed covariates. In this talk, we will investigate 3 different strategies to handle missing data for PS analysis: complete case analysis (CC), the missingness pattern approach (MP) and multiple imputation (MI). We will look at the assumptions required for each method and think about when each should be used in practice. For multiple imputation, there are important questions regarding its implementation in the PS context (e.g. should we apply Rubin’s rules to the treatment effect estimates or to the PS estimates themselves? Does the outcome have to be included in the imputation model?). This talk will give an overview of key questions and outline some solutions from our recent research.

Centre for Statistical Methodology Seminar
Multivariate Methods & Survival Analysis Themes
Friday 4 November 2016, 12:45-2:00pm (please note that this seminar was originally scheduled for Tuesday 18 October 2016)
LG24, Keppel Street
Sparse survival models in high-throughput cancer studies
Prof Ernst Wit (University of Groningen)

Abstract: Sparsity is an essential feature of many contemporary data problems. Many health scans collect a lot of genetic information on patients. In certain cases it is reasonable to assume that the underlying process generating the data is itself sparse, in the sense that only a few of the measured variables are involved in the survival process. We propose an explicit method of monotonically decreasing sparsity for survival models. In our approach we generalize a so-called equiangular condition in a generalized linear model. Although the geometry involves the Fisher information in a way that is not obvious in the simple regression setting, the equiangular condition turns out to be equivalent with an intuitive condition imposed on the Rao score test statistics. In certain special cases the method can be tweaked to obtain L1-penalized GLM solution paths, but that’s not the point. The method itself defines sparsity more directly. Although the computation of the solution paths is not trivial, the method compares favorably to other path following algorithms. We show how the method works on a diffuse large-B-cell lymphoma dataset and four high-throughput survival studies of prostate, ovarian, skin and colon cancer, all performed in the last 5 years.

Centre for Statistical Methodology Seminar
Analysis of Clinical Trials Theme
Friday 27 January 2017, 12:45-2:00pm
LG9, Keppel Street
Central statistical monitoring in an academic clinical trials unit
Amy Kirkwood (UCL Cancer Trials Centre)

Abstract: Within our academic clinical trial unit we follow a risk based approach to monitoring, avoiding, when possible, on-site source data verification which is an expensive activity with little evidence that it is worthwhile. Central statistical monitoring (CSM) has been suggested as a cheaper alternative where checks are performed centrally without the need to visit all sites. We developed a suite of R-programs which could perform data checks at either a subject or site level using previously described methods or ones we developed. These aimed to find possible data errors such as outliers, incorrect dates, or anomalous data patterns; digit preference, values too close or too far from the means, unusual correlation structures, extreme variances which may indicate fraud or procedural errors and under-reporting of adverse events. The aim was to produce programs which would be quick and easy to apply and which would produce simple tables or easy-to-read figures. We will summarise the methods and, using examples from trials within our unit, show how they are implemented and that they can be easy to interpret. We found CSM to be a worthwhile alternative to on-site data checking and may be used to limit the number of site visits by targeting only sites which are picked up by the programs. The methods can identify incorrect or unusual data for a trial subject, or centres where the data considered together are too different to other centres and therefore should be reviewed, possibly through an on-site monitoring visit.

Centre for Statistical Methodology Seminar
Friday 24 February 2017, 12:45-2:00pm
LG9, Keppel Street
Linking administrative data for epidemiological research
Katie Harron (LSHTM)