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Dr Karla Diaz-Ordaz

PhD

Associate Professor
of Biostatistics

Room
G34

LSHTM
Keppel Street
London
WC1E 7HT
United Kingdom

Tel.
020 7927 2065

My primary methodological research area is causal machine learning motivated by high-dimensional electronic health records and genomics data.

My work on treatment effect heterogeneity and optimal treatment regimes is funded through a Wellcome Trust-Royal Society Sir Henry Dale Fellowship (2020-2025).

I am also co-lead in a collaborative research project "Developping statistical machine learning methods for Clinical Trials" based at  the Alan Turing Institute.

Previously, I held a Wellcome Trust-LSHTM Institutional Support Fellowship (2018-2019), an MRC Career Development Award in Biostatistics (2014-2018) and an NIHR Methods Fellowship (2009-2012).

In 2017, I was a visiting scholar to Prof. Mark van der Laan's Computational Biology and Causality group at the School of Public Health, University of California, Berkeley.

 

I have a PhD in Mathematics from Imperial College London and a MSc in Medical Statistics from the London School of Hygiene and Tropical Medicine.

PhD students:

Oliver Hines (2018- ): MRC London Intercollegiate Doctoral scholarship studying double-robust methods with machine learning in high-dimensional data, with applications to cardio-genetics mediation (joint with Prof Stijn Vansteelandt)

Former PhD students:

Dr Schadrac Agbla (awarded 2019): Instrumental Variable methods for adjusting for nonadherence in cluster randomised trials (joint with Prof Bianca DeStavola). 

Dr Anower Hossain (awarded 2017):  Missing data methodology for cluster randomised trials (joint with Dr Jonathan Bartlett).

 

Affiliations

Department of Medical Statistics
Faculty of Epidemiology and Population Health

Centres

Centre for Statistical Methodology

Teaching

I am the co-organiser of the School's short course Causal Inference in Epidemiology: Recent Methodological Developments 

I am also the teach on the Advanced Statistical Methods (Causal Inference) sub-module in the MSc in Medical Statistics.

I am also a co-organiser of the Machine Learning Module in the MSc in Health Data Science.

Research

My current work involves doubly-robust estimators paired with the machine learning estimation of the nuisance parameters (e.g. Super Learner estimation). Examples of these are Targeted Minimum Loss estimators (TMLE) and g-estimators with machine learning. These methods are very promising to study causal effects using big data. This is in collaboration with Prof Stijn Vansteelandt (U Ghent).

I am also a co-Principal investigator (together with Prof Chris Holmes) on a project scoping the uses of machine learning in clinical trials, at the Alan Turing Institute.

Previous work involved developing methods for estimating causal treatment effect when there is departures from protocol in a randomised trial (i.e. non-compliance and missing data) using Multiple Imputation (collaboration with Prof James Carpenter). I have also worked in  extending methods for cost-effectiveness analysis, accounting for the bivariate nature of the endpoints (with Prof Richard Grieve).

 Some of my methodological work code can be found in my GitHub page.

I am a member of the Centre for Statistical Methodology, and one of the co-ordinators of the missing data and causal inference themes.

Research Area
Clinical trials
Complex interventions
Economic evaluation
Statistical methods
Bayesian Analysis
Electronic health records
Methodology
Discipline
Health economics
Genomics
Economics
Epidemiology
Mathematics
Statistics

Selected Publications

Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England.
Davies NG; Abbott S; Barnard RC; Jarvis CI; Kucharski AJ; Munday JD; Pearson CAB; Russell TW; Tully DC; Washburne AD
2021
Science (New York, N.Y.)
Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7.
Davies NG; Jarvis CI; CMMID COVID-19 Working Group; Edmunds WJ; Jewell NP; Diaz-Ordaz K; Keogh RH
2021
Nature
Importance of patient bed pathways and length of stay differences in predicting COVID-19 bed occupancy in England
Leclerc QJ; Fuller NM; Keogh RH; Diaz-Ordaz K; Sekula R; Semple MG; Atkins KE; Procter SR; Knight GM
2021
Invited Commentary: Treatment drop-in: making the case for causal prediction.
Sperrin M; Diaz-Ordaz K; Pajouheshnia R
2021
American journal of epidemiology
The effect of a one-year vigorous physical activity intervention on fitness, cognitive performance and mental health in young adolescents: the Fit to Study cluster randomised controlled trial.
Wassenaar TM; Wheatley CM; Beale N; Nichols T; Salvan P; Meaney A; Atherton K; Diaz-Ordaz K; Dawes H; Johansen-Berg H
2021
The international journal of behavioral nutrition and physical activity
Predicting COVID-19 related death using the OpenSAFELY platform
Williamson EJ; Tazare J; Bhaskaran K; McDonald HI; Walker AJ; Tomlinson L; Wing K; Bacon S; Bates C; Curtis HJ
2021
medRxiv preprint
Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.
Clift AK; Coupland CAC; Keogh RH; Diaz-Ordaz K; Williamson E; Harrison EM; Hayward A; Hemingway H; Horby P; Mehta N
2020
BMJ-BRITISH MEDICAL JOURNAL
How to estimate the association between change in a risk factor and a
health outcome?
Katsoulis M; Lai AG; Kipourou D-K; Sofat R; Gomes M; Banerjee A; Denaxas S; Lumbers TR; Tsilidis K; Hemingway H
2020
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