For further information on any CSM events please e-mail:

Centre for Statistical Methodology Seminar
Thursday 24 January 2019, 12:45-2:00pm
LG8, Keppel Street
Heather Battey (Imperial College London)

Title: Large numbers of explanatory variables.

Abstract: The lasso and its variants are powerful methods for regression analysis when there are a small number of study individuals and a large number of potential explanatory variables. There results a single model, while there may be several simple representations equally compatible with the data. I will outline a different approach, whose aim is essentially a confidence set of models. The talk is based on joint work with David Cox.

Centre for Statistical Methodology Seminar
Thursday 28 Feb 2019, 12:45-2:00pm
LG8, Keppel Street
Antonio Gasparrini and Francesco Sera (LSHTM)

Title: An extended mixed-effects model for meta-analysis: statistical framework and the R package mixmeta

Centre for Statistical Methodology Seminar
Friday 22 March 2019, 12:45-2:00pm
LG9, Keppel Street
Matt Fox (Boston University)

Title: Using Quantitative Bias Analysis to Deal with Misclassification in the Results Section, not the Discussion Section

Centre for Statistical Methodology Seminar
Thursday 25 April 2019, 12:45-2:00pm
LG8, Keppel Street
Anders Huitfeldt (London School of Economics)

Title: A new approach to generalizability of clinical trials


VanderWeele (Epidemiologic Methods, 2012) provided two separate definitions of effect heterogeneity, which he referred to as “effect modification in distribution” and “effect modification in measure”. The standard epidemiological approach, which is based on effect modification in measure, is associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators can assume effect homogeneity on either the additive or the multiplicative scale. More recently, Bareinboim and Pearl introduced a new graphical framework for transportability, based on effect heterogeneity in distribution. These graphs are an elegant solution to many of the problems associated with traditional approaches, but they require the investigator to make strong assumptions about the data generating mechanism: In particular, it is not sufficient to control for those variables that are associated with the effect of treatment; investigators using this approach are required to account for all causes of the outcome that differ between the populations.

In light of these limitations, we propose a new definition of effect heterogeneity, based on “counterfactual outcome state transition parameters”, that is, the proportion of those individuals who would not have been a case by the end of follow-up if untreated, who would have responded to treatment by becoming a case; and the proportion of those individuals who would have become a case by the end of follow-up if untreated who would have responded to treatment by not becoming a case. Effects are said to be equal between populations if and only if these proportions are equal between the populations. Although counterfactual outcome state transition parameters are generally not identified from the data without strong monotonicity assumptions, we show that when they stay constant between populations, there are important implications for model specification, meta-analysis, and research generalization.

Centre for Statistical Methodology Seminar
Wednesday 26 June 2019, 14:40-15:40pm
John Snow Lecture Theatre B, Keppel Street
George Davey Smith (University of Bristol)

Title: Post-“Modern Epidemiology”: when methods meet matter.


In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of non-communicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of non-communicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post–modern.

Brief Bio:

George Davey Smith was a member of the noise-terrorism outfit Scum Auxiliary in the early 1980s. Since artistic and commercial success eluded them, he has had to earn his living working as an epidemiologist in the provinces.