Dr David McLernon
PhD MPhil BSc
Senior Research Fellow
I have been a Senior Research Fellow in Medical Statistics in the Institute of Applied Health Sciences for 2 years. I was a research fellow at the same institute for the previous 11 years. I have a strong methodological interest in clinical prediction modelling, particularly within the clinical area of reproductive medicine, a field I have worked in for over 10 years. One of the projects I led concerned the dynamic prediction of live birth in couples who have unexplained infertility. Such models help clinicians to counsel couples on their changing prognosis over time and may help aid decisions around when their patients should undergo ART. I led the development of the OPIS predictions models which estimate the chance of having a baby over multiple complete cycles of IVF. I also collaborate with the USA's Society for Assisted Reproductive Technology and together we developed the new SART IVF predictor at sart.org.
I am also a member of an international body called The STRATOS initiative (STRengthening Analytical Thinking for Observational Studies). Specifically, I am a member of the topic group 6 on ‘Evaluating diagnostic tests and prediction models’ (https://www.stratos-initiative.org/groups). I am currently writing a guidance paper for researchers on how to assess the predictive performance of survival prediction models.
I currently supervise 5 PhD students, and teach medical statistics at undergraduate and postgraduate level. I am Associate Editor of Human Reproduction Open and sit on the Research Advisory Committee of Wellbeing of Women.
- PhD Primary Care/Medical Statistics2008 - University of DundeePrediction of liver disease diagnosis and mortality following liver function testing in primary care
- MPhil Medical Statistics2002 - Queen's University BelfastEpidemiology of Childhood Type 1 Diabetes in Northern Ireland: Geographical and Temporal Analyses
- BSc Hons Applied Mathematics, Statistics and Operational Research1999 - Queen's University Belfast
Memberships and Affiliations
- Internal Memberships
- Member of Health and Data Linkage in North East Scotland (HEADLINES) group (June 2013-)
- Member of Grampian Data Safe Haven Governance subgroup (July 2013-)
- Ex-Member of the College Ethics Review Board (May 2010-June 2018)
- Organised and chaired the Foresterhill Statistics Group meetings at the University of Aberdeen (June 2008-May 2011)
- External Memberships
- Secretary of the Royal Statistical Society Highlands Local Group Committee
- Fellow of the Royal Statistical Society
- Associate Editor of Human Reproduction Open journal
- Member of The STRengthening Analytical Thinking for Observational Studies (STRATOS) Initiative Topic Group 6: Evaluating diagnostic tests and prediction models http://www.stratos-initiative.org/group_6
Wellbeing of Women Research Advisory Committee member (2018-)
Predicting cumulative live birth for couples beginning their second complete cycle of in vitro fertilization treatmentHuman reproduction (Oxford, England)Contributions to Journals: Articles
Individual participant data meta-analysis of trials comparing frozen versus fresh embryo transfer strategy (INFORM): a protocol.BMJ Open, vol. 12, e062578Contributions to Journals: Articles
Comparison of perinatal outcomes following frozen or fresh embryo transfer: separate analyses of singleton, twin and sibling live births from a linked national In vitro fertilisation registryFertility and SterilityContributions to Journals: Articles
Prevalence of PErioperAtive CHildhood obesitY in children undergoing general anaesthesia in the UK: a prospective, multicentre, observational cohort studyBritish Journal of Anaesthesia, vol. 127, no. 6, pp. 953-961Contributions to Journals: Articles
Is stroke incidence increased in survivors of adult cancers?: A systematic review and meta-analysisJournal of Cancer SurvivorshipContributions to Journals: Review articles
Methodologically, I am interested in the development of prognostic models, particularly those using advanced survival analysis methods such as accelerated failure time models. I am involved in promoting the application of methods for assessing the performance of prediction models with the STRATOS Initiative Topic Group 6. Prediction modelling is important because they facilitate clinical decision-making, inform patients of their probability of an outcome of interest, and can be used for risk stratification to inform RCT designs.
Much of my work focuses on the application of statistical methods to the discipline of reproductive medicine, a field I have worked in for over 10 years. One of the projects I led concerned the dynamic prediction of live birth in couples who have unexplained infertility. Such models help clinicians to counsel couples on their changing prognosis over time and may help aid decisions around when their patients should undergo ART. I led the development of the OPIS predictions models which estimate the chance of having a baby over multiple complete cycles of IVF. I also collaborate with the USA's Society for Assisted Reproductive Technology and together we developed the new SART IVF predictor at sart.org.
Applied Health SciencesSupervising
Predicting pregnancy outcomes in couples with infertility
Outcome Prediction In Subfertility (OPiS) models
In my CSO Postdoctoral Fellowship (see past research), I developed two clinical prediction models that can calculate the personalised cumulative probability of having a baby over multiple complete cycles of IVF treatment. The pre-treatment model uses couple information from before starting IVF to inform them of their chance of success over the first complete cycle and all subsequent complete cycles up to a maximum of six. The post-treatment model revises this prediction when the couple have attempted their first embryo transfer. This model incorporates treatment information inclduing the number of eggs retrieved and the number of embryos transferred. Please see: https://w3.abdn.ac.uk/clsm/opis. This work involved collaborations with clinical and methodological experts from Leiden University Medical Center and Utrecht Medical Center.
My PhD student is improving the OPiS tool through external validation and the addition of a further prediction model. The existing models are being validated using an updated extract of IVF cycles undertaken between 2010 and 2017 among UK couples. This data is held by the Human Fertilisation and Embryology Authority (HFEA). By updating these models using the latest available data they will provide more accurate predictions in line with current IVF practice. After the first complete cycle, couples who were unsuccessful may wish to try IVF treatment again, and couples who were successful may wish to try for a second baby. We are currently developing a new OPiS model that can be used by couples who want to start a second complete cycle.
Dynamic predictions for couples with unexplained infertility
In recent previous work, we developed a novel prototype dynamic model that can predict natural and treatment related conception in couples with unexplained infertility,9 and converted it into an online tool (https://w3.abdn.ac.uk/clsm/test/tool/opis1). The model predicts from diagnosis and recalculates these chances monthly. This model was based on a relatively small dataset from Aberdeen Fertility Clinic using data from 1998-2011 (1316 couples). This work involved collaborations with clinical and methodological experts from Academic Medical Center in Amsterdam, Leiden University Medical Center and Utrecht Medical Center.
While predictions under expectant management validated well in a Dutch cohort, the precision of predictions following IVF was relatively low due to small numbers of monthly treatments. We propose to refine this model by including the following eight years’ worth of data (~2000 more couples) which will further improve the accuracy and precision of predictions. We will externally validate the improved model in a definitive cohort study using a large sample of new couples with unexplained infertility across the UK. This model will provide a pragmatic approach to personalised management for infertile couples with respect to who and when to treat.
We have also undertaken a usability study of our dynamic prediction tool. We recruited eight female public participants in order to gain a better understanding of user-interface design and interaction. The main feedback was around making the tool more user-friendly e.g. less clinical sounding text, more attractive colouring and interactive elements. The actual science behind the tool was seen as very useful and helpful, especially the personalised predictions and the fact that they are updated monthly. The outcome of this usability study helped assess design priorities and will enable us to design a better user interface for further release. We are currently running a study in collaboration with Cardiff University which aims to determine the feasibility of our prediction tool for the management of couples with unexplained infertility. This study will provide evidence of the need for a tool to help clinicians and patients make decisions on treatment. This will feed into a future study to test our dynamic model on a wider UK population.
As well as fertility-based research, I am involved in studies from different clinical disciplines (e.g. stroke, renal disease, obstetrics and gynaecology) using a variety of statistical methodology (e.g. IPD-MA, advanced survival methods, mixed effects models).
In January 2012 I was awarded a Chief Scientist Office Postdoctoral Fellowship in Health Services and Health of the Public Research supervised by Prof Bhattacharya and Prof Lee. The fellowship aims to develop clinical prediction models that can estimate the probability of pregnancy outcomes in couples attending a fertility clinic. A brief summary follows:
Development and validation of prognostic models for subfertility
Subfertility is defined as the inability to conceive within one year of unprotected vaginal intercourse. Subfertility is a large public health burden, affecting 1 in 5 Scottish couples. Debate exists on what characteristics determine whether and when a subfertile couple should be treated. Treatment for those with a high chance of conceiving naturally may not be cost-effective and result in undue stress. Conversely, those with a low chance of spontaneous conception need immediate treatment. Whilst clinical prediction models exist that estimate the chances of treatment independent pregnancy or treatment dependent pregnancy, none take into account both outcomes. Predicting the chance of pregnancy following IVF treatment is also important since couples with different characteristics e.g. different ages, type of infertility, time spent trying to conceive etc. will have different chances of conception. Some may require IVF sooner while others may need more cycles of IVF treatment. The aims of this study are: 1. To develop and validate prediction models to identify who needs fertility treatment and when (Outcome Prediction In Subfertility (OPIS) Grampian); and 2. To develop clinical prediction models to predict pregnancy outcomes in women considering IVF treatment in the UK (OPIS IVF).
For the OPIS Grampian models, world renowned databases from the Aberdeen Fertility Centre (AFC) and Aberdeen Maternity and Neonatal Databank will be utilised to identify all subfertile couples living in Grampian, who attended the Centre from 1992 to 2011. These databases contain important baseline predictors, treatment information, and outcomes (natural or treatment based pregnancy, live birth). Cox proportional hazards models will be used to estimate the chance of treatment independent pregnancy within different follow-up time periods. A stratified medicine modelling approach will be considered to examine the absolute difference in pregnancy chances between women who undergo treatment and those who undergo expectant management. By calculating this absolute difference for women with the same baseline characteristics, the clinician can decide whether it is beneficial to treat or not. In order to inform clinical decisions regarding when to treat, a dynamic prediction model using landmarking will be used. This involves fitting sequential Cox models predicting treatment independent pregnancy over the following year from incremental time origins from baseline. This enables one to determine the change in the predicted probability of pregnancy the longer treatment is delayed. For OPIS IVF, models predicting treatment related outcomes (including preterm birth) for separate causes of subfertility following IVF will be derived using UK wide data from the Human Fertilisation and Embryology Authority. To adjust for multiple IVF cycles a Poisson regression model will be used considering event time to be the number of cycles until success. This removes the need to account for within-subject correlation because the woman is the unit of observation, rather than the cycle. Predictive ability for all models will be assessed using discrimination and calibration tests. For OPIS Grampian, external validation will be performed using an independent cohort of Dutch subfertile patients. A web-based clinical prediction aid will be created to facilitate clinical management of subfertile couples. Such models will help clinicians and couples make individual decisions about whether fertility treatment would be beneficial and when it should be offered.
I was awarded the inaugural Backett Weir Russell Career Development Fellowship in November 2010, an internal competitive award funded by Professor Roy Weir. This Postdoctoral Fellowship provided the opportunity to publish papers from my PhD and to further investigate my methodological interests (focussing on survival modelling techniques in particular). The award also facilitated the development of my external Postdoctoral Fellowship application to the CSO. Without the kind donation by Professor Weir it would have been unlikely that I would have had the protected time to apply for this funding.
My PhD involved the follow-up of patients who have had their initial liver function tests (LFTs) in primary care, and who had no clinically obvious liver disease. Prediction models using accelerated failure time methods were derived and assessed to predict risk of liver disease, liver mortality and all cause mortality at various time points. This involved large observational datasets and record-linkage procedures.
I have delivered a Café Med talk to the public with my clinical colleague Prof Bhattacharya called ‘Making pregnancy predictable – from the oral pill to fertility apps’. Suttie Centre Café, Foresterhill, 24th Feb 2020
I have delivered several stand-up comedy sets at Bright Club events. The most recent one was entitled ‘What are the odds?’ at the Explorathon festival (Sept 2019).
I have developed collaborations with several key researchers and clinicians from The Netherlands. Prof Ewout Steyerberg is Professor of Medical Decision Making at the Department of Public Health who is world-renowned for his research in prognostic modelling and is author of the textbook ‘Clinical Prediction Models’. Professor te Velde is an Emeritus Professor from the Department of Public Health who has written multiple papers on prognostic modelling in fertility medicine. Prof Ben Mol is Professor of Obstetrics and Gynaecology at the University of Adelaide. With these eminent Professors (including Prof Bhattacharya) I have published an opinion paper on Clinical prediction models to inform individualized decision-making in subfertile couples: a stratified medicine approach.
I am involved in promoting the application of methods for assessing the performance of prediction models with the STRATOS Initiative Topic Group 6. We are mainly a large collaboration of experts in many different areas of biostatistical research e.g. statisticians, methodologists, epidemiologists, with some additional membership categories for applied researchers and clinicians engaged in research with interests and practical experience with statistical methodology.
Current PhD students
Mariam Ratna. Co-supervisor: Prof Bhattacharya. Predicting cumulative pregnancy outcomes in patients having IVF treatment. PhD Assisted Reproduction Unit and Elphinstone Scholarship, University of Aberdeen (May 2017-)
Andrew Mohan. Co-supervisors: Dr Ian Fleming, Dr Kirsten Laws, Dr Leslie Samuel. The effects of hypoxia biomarkers in lung adenocarcinoma. PhD, University of Aberdeen (Oct 2018-)
Christopher Allen. Co-supervisors: Prof Maheshwari, Dr Bhattacharya. Obstetric and perinatal outcomes in pregnancies conceived using donor sperm as compared with those using partner sperm. PhD, University of Aberdeen (Dec 2018-)
Rosalind Mitchell-Hay. Co-supervisors: Prof Murray, Dr Samuel, Dr Ahearn. Developing Imaging Biomarkers in Rectal Cancer. PhD, University of Aberdeen (Sept 2019-)
David Middleton. Co-supervisors: Dr Cooper. Biomarkers for predicting deterioration in Emergency Department Sepsis (BIOMEDS). PhD, University of Aberdeen (Oct 2020-)
Past PhD students
Michelle Tornes. Co-supervisor: Prof Myint. Variations in stroke units care and patient related outcomes. PhD IAHS Studentship, University of Aberdeen (Sept 2015-Feb 2020).
Lina Altayeb. Co-supervisor: Prof Black, Dr Marks. Optimising kidney care: using health informatics to understand medication in kidney patients. PhD, University of Aberdeen (Mar 2017-April 2020).
Mairead Black. Co-supervisors: Dr Bhattacharya, Dr Allen. Model of delivery after caesarean section: An investigation of offspring risks and factors influencing women’s attitudes towards delivery options. PhD and Wellcome Trust Training Fellowship, University of Aberdeen (Mar 2013-Feb 2016).
Funding and Grants
CSO Research Grant £286,516; Nov 19 to Oct 22
“Interactions between cancer and stroke: A national electronic data linkage study (HIPS/19/21)”
Mary Joan MacLeod (PI), Melanie Turner, Peter Murchie, David McLernon (University of Aberdeen), Peter Langhorne (University of Glasgow), Trevor McGoldrick (NHS Grampian)
Medical Research Scotland Vacation Scholarship £2,000; Jul to Aug 19
“Development and validation of a prediction score for 10-year stroke mortality (VAC-1424-2019)”.
David McLernon (PI), Weronika Szlachetka, Phyo Myint (University of Aberdeen)
NHS Endowment Research Grant £12,000; Apr 19 to Mar 20
“Are blood biomarkers associated with stroke severity and clinical outcomes? An electronic data linkage study in NHS Grampian”
Melanie Turner (PI), Mary Joan MacLeod, David McLernon (University of Aberdeen)
Medical Research Scotland Vacation Scholarship £2,000; Jul to Aug 18
“The impact of Heart Failure and Atrial Fibrillation on clinically relevant stroke outcomes”.
David McLernon (PI), Tiberiu Pana, Phyo Myint (University of Aberdeen)
NHS Endowment Research Grant £10,701; Apr 18 to Mar 19
“Perinatal outcomes of singletons born following in-vitro fertilisation: a comparison of different embryo transfer strategies using UK data”
David McLernon (PI), Siladitya Bhattacharya, Dr Abha Maheshwari, Dr Amalraj Raja (University of Aberdeen)
NHS Endowment Research Grant £11,892; Apr 18 to Mar 19
“The frequency and prognostic impact of SNCA mutations in a population-based sample of idiopathic Parkinson’s disease”
Angus Macleod (PI), David McLernon (University of Aberdeen), Jodi Maple-Grødem (Stavanger University Hospital), Carl Counsell (University of Aberdeen)
Tenovus Scotland £11,438; Jan 18 to Dec 18
“Does IVF improve pregnancy rates in unexplained infertile couples?”
David McLernon (PI), Siladitya Bhattacharya (University of Aberdeen), Dr Nan van Geloven (Leiden University Medical Center), Rik van Eekelen (Academic Medical Center)
Medical Research Scotland Vacation Scholarship £1,750; Jul to Aug 17
“Risk factors for ectopic pregnancy in the first complete cycle of in vitro fertilisation”.
David McLernon (PI), Natalie Cameron, Sohinee Bhattacharya (University of Aberdeen)
Stroke Association Fellowship £133,081; Jun 16 to May 19
“Stroke and comorbidity: An electronic data linkage study to investigate the relationship between comorbidity and stroke prevention, management, outcome and recurrence”.
Melanie Turner (Fellow), Mary-Joan Macleod, David McLernon, Peter Langhorne (University of Glasgow)
Chief Scientist Office £222,249 (£1,427); Apr 15 to Mar 17
“A pilot evaluation of an intelligent liver diagnostic pathway: making sense of LFTs for patients, GPs and the NHS in Scotland”.
John Dillon (PI), Peter Donnan, Ellie Dow, Michael Miller, Paul McIntyre, William Bartlett, Ron Neville, Christopher Weatherburn, Sara Marshall (University of Dundee), Kathleen Boyd (University of Glasgow), David McLernon (University of Aberdeen) (Co-applicant)
Chief Scientist Office Postdoctoral Fellowship in Health Services and Health of the Public Research £172,515; Jan 2013 to Dec 2015
“Development and validation of prognostic models for subfertility”
David McLernon (PI), Siladitya Bhattacharya (Supervisor), Amanda Lee (Supervisor) (University of Aberdeen
Backett Weir Russell Career Development Fellowship £138,713; Nov 2010 to Dec 2012
“The use of accelerated failure time models for clinical prediction”
David McLernon, Marion Campbell (Mentor) (University of Aberdeen)
Health Technology Assessment, NHS £249,377; Feb 2005 to Feb 2008
"Development of a decision support tool to facilitate primary care management of patients with abnormal liver function tests without clinically apparent liver disease" (03/38/02)
Donnan PT, Dillon JF, McLernon D, Steinke D, Ryder S, Roderick P, Sullivan F, Rosenberg W.
- Course Co-ordinator and lecturer in the Intermediate Statistics Course for University/external staff on survival analysis
- Lecturer in annual Postgraduate Basic Statistics course
Lecturer in annual Introduction to Medical Statistics to 3rd/4th year medical students
- Co-ordinator and lecturer for the Introduction to SPSS courses for staff
- Lecturer in the online PGCert Applied Statistics course (PU5522)
- Tutor and marker for PU5017
Non-course Teaching Responsibilities
- Deliver Obstetrics & gynaecology medical staff annual lecture on medical statistics – 29th Aug 2019 ‘Clinical prediction models: what are they and how can we spot a useful one?’
- Contribute to the lunchtime student consultancy clinics
- Provide statistical consultancy to staff and PhD students in the School of Medicine & Dentistry
- Marker of exams and assignments for the Applied Statistics module of the Masters in Public Health course (PU5522 and PU5017)
Marker of theses for Intercalated BSc Med Sci degree
- Marker of theses for MSc Public Health course
- Examiner of oral protocol presentations in the Masters in Public Health course
- Invigilator for the medical statistics exam (PU5017)
- Advisor of PhD students
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Incidence of paediatric unplanned day-case admissions in the UK and Ireland: a prospective multicentre observational studyBritish Journal of Anaesthesia, vol. 124, no. 4, pp. 463-472Contributions to Journals: Articles
Influences of rurality on action to diagnose cancer by primary care practitioners– results from a Europe-wide survey in 20 countriesCancer Epidemiology, vol. 65, pp. 101698Contributions to Journals: Articles
A comparison of the beta-geometric model with landmarking for dynamic prediction of time to pregnancyBiometrical Journal, vol. 62, no. 1, pp. 175-190Contributions to Journals: Articles
Making the Most of Your Data: Using an Alternative Statistical Methodology to Multi-level Modeling to Investigate Hospital Effects on Acute Hospital Length of Stay Following Stroke when Number of Hospitals is SmallSage PublicationsBooks and Reports: Other Reports
A systematic review of the quality of clinical prediction models in in vitro fertilisationHuman Reproduction, vol. 35, no. 1, pp. 100-116Contributions to Journals: Articles
Calibration: the Achilles heel of predictive analyticsBMC medicine , vol. 17, 230Contributions to Journals: Articles
The impact of pre-stroke comorbidities on home-time following stroke: a national data linkage studyInternational Journal of Stroke, vol. 14, no. 4 Suppl. , pp. 23Contributions to Journals: Abstracts
Three myths about risk thresholds for prediction modelsBMC medicine , vol. 17, 192Contributions to Journals: Articles
Myocardial infarction after acute ischaemic stroke: incidence, mortality, and risk factorsActa Neurologica Scandinavica, vol. 140, no. 3, pp. 219-228Contributions to Journals: Articles
Hospital-level Variations in Rates of Inpatient Urinary Tract Infections in StrokeFrontiers in Neurology, vol. 10, 827Contributions to Journals: Articles