This report is a description of an NCRM collaborative event-based project

Best, Nicky

Dearden, Lorraine

Geneletti, S

Goldstein, Harvey

Rasbash, Jon

Richardson, Sylvia

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NCRM Collaborative fund project: Adaptingeconometric causal effect estimators to the pub-lic health arenaFinal reportApplicants: Sara Geneletti, Nicky Best, Sylvia Richardson (BIASII), Lorraine Dearden (Admin), Harvey Goldstein, Jon Rasbash(LEMMA 2)Day OneWe organised a one day seminar entitled “Mixing econometrics and epidemi-ology”, this was free to attend but required registration. The seminar daywas well attended with over 20 registrants. The schedule of talks was asfollows:1. 11:00 - 11:15 Welcome Coffee2. 11:15-11:30 Sara Geneletti (Imperial College) Introduction to themeeting3. 11:30-12:30 Lorraine Dearden (Institute of Education) Evaluation meth-ods in Economics and their possible application to Epidemi-ology4. 12:30-13:30 lunch break5. 13:30-14:05 Philip Dawid (Cambridge) Beware of the DAG!6. 14:05-14:40 Gianluca Baio (University College London/University ofMilano Bicocca) Mixing Econometrics and Epidemiology: theperfect job for Health Economics?7. 14:40-15:15 Frank Windmeijer (University of Bristol) SMM estima-tion with binary outcomes8. 15:15-15:30 coffee break9. 15:30-16:05 Nuala Sheehan (University of Leicester) Mendelian ran-domisation and causal inference in epidemiology10. 16:05-16:35 general discussion1Day twoOn the second day, speakers from the seminar day were invited to participatein a brainstorming session. The aim of this session was to consider situationswhere econometric estimators discussed during the seminar day could beapplied to the Public Health arena. We briefly considered two types of es-timators, regression discontinuity designs and instrumental variables beforegoing on to discuss how we would tackle an example public health problem.Regression discontinuity designsRDDs take advantage of ”sharp” threshold and compare individuals whoare on either side of this threshold. An example of such a threshold areprimary school admissions in the UK which depend on the date of birthof a child rather than the child’s age. This can tell us about the effectof age on scholastic achievement as it allows us to compare children whoeffectively have the same age, but some of whom are born just before thethreshold and therefore start school a year before those who are born justafter. Lorraine Dearden from Admin has worked extensively on this issue. Asimilar discontinuity can be seen in many Public Health contexts. A simpleexample are the thresholds used by GPs and clinicians to determine whetherto prescribe drugs to their patients. For example, an individual will beprescribed ACE inhibitors if their Systolic and Diastolic blood pressures areabove 140 and 90 respectively, if they fall below these thresholds they will not.To understand whether the ACE inhibitors are effective and also whetherthe threshold should change it is useful to compare blood pressure changesin individuals who have blood pressures just below the thresholds and whotherefore do not receive medication to those who have blood pressures justabove the thresholds and therefore receive medication. The idea is that thesetwo groups are not likely to be that different before they receive medicationand the sharp thresholds allow us to compare the effect of medication almostas in a randomised trial – limited to those who have blood pressures aroundthe thresholds. RDDs have not yet been absorbed into the Public Healthand Epidemiologic literature and there is scope for collaborations and furtherresearch in this area. BIAS II are collaborating with their visiting fellowsfrom ISER who are using RDDs to investigate fertility outcomes.Instrumental Variables and Mendelian RandomizationThis is the focus of work by Nuala Sheehan and Frank Windmeijer and wediscussed the pros and cons of applying instrumental variable methods from2econometrics to epidemiology using Mendelian randomization. The advan-tage of using a genetic instrument is that it is believable without too muchfurther justification – this is not always the case in econometrics where it isusually much harder to justify the validity and “randomness” of an instru-ment. One of the major problems with the approach is that contrary to thegeneral econometric context, the genetic instrument is generally binary. Thismeans that most econometric techniques which are developed for the contin-uous case and linear models cease to work in this case. Another problem isthat the genetic instruments tend to be weak. We discussed whether perhapsmore than one gene could be used and a stronger instrument would then bea set of genes. Finally, the gene could be affecting the outcome indirectly(see DAG in Figure 1). Although there has been research showing that mostof the genetic instruments that have been used are not pairwise associatedwith outcomes of interest, there is no guarantee that jointly genes might notaffect outcomes. One potential new avenue of research that emerged fromthis critique was to attempt simulation studies to understand how some genescould be marginally independent of the outcomes of interest but jointly de-pendent. We also discussed some more fundamental problems with applyingeconometric estimators which take into account human behaviour such ascompliance to the context of genetic instruments as the concept of compli-ance is difficult to interpret when individuals are complying not to certainbehaviours (e.g. participating or not in an adult education programme) butto certain epidemiologic exposures such as BMI.G X YU?Figure 1: DAG representing the use of the genetic instrument. The dashedline violates one of the IV assumptionsFinal commentsBoth days were successful and resulted in some in a number of possible av-enues of future research and potential collaborations. In particular, RDDappear to offer an exciting and as yet relatively unexplored avenue of re-search in Epidemiology and Public Health. Our discussion on the use ofIV estimators with a genetic instrument revealed that whilst IV estimators3are undoubtedly useful, they might not be as effective as they have been ineconometrics.4

Adapting econometric causal effect estimators to the public health arena

https://eprints.ncrm.ac.uk/1668/1/Geneletti_report.pdf

Adapting econometric causal effect estimators to the public health arena

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