Seminar 3: Matching
Matching methods aim to balance pre-intervention characteristics between comparison groups, to minimise the bias in the estimation of treatment effectiveness that is due to observed confounders. The major advantages of matching are: conceptual simplicity, that it can be customised according to the causal question of interest, and that it can be conducted without seeing the outcome data in question.
This seminar will outline the key concepts and requisite assumptions in undertaking matching. The seminar will cover propensity score matching together with more flexible, data adaptive matching methods specifically designed to achieve covariate balance. I will discuss sensitivity analyses to the assumption of 鈥榥o unobserved confounding鈥. I will introduce latest developments in matching methods including new approaches for combing matching with instrumental variable estimation.
The seminar will draw on a raft of examples from clinical and economic evaluation, health services and health systems research.
Stay tuned for more details on the seminars in the rest of this series:
- Tuesday 22nd May 12.45-14.00 (Jerry Morris B, Tavistock place): , Antonio Gasparrini
- Thursday 7th June 12.45-14.00 (LG7, Keppel street): , Aur茅lia L茅pine
Past event:
- Seminar 1: 鈥楿sing difference-in-differences in health systems research鈥. The approach is a quasi-experimental method widely used to evaluate the impact of health policies and interventions. This seminar will introduce the method and discuss a number of applications in the field of health systems research with a view to highlighting both the pros and cons of the method. Speaker: Tim Powell-Jackson. You can and
- Seminar 2: 鈥楽tatistical issues in the application of the regression discontinuity design for causal inference from clinical administrative databases鈥. Electronic Health Records (EHRs) are increasingly popular in health care and public health research because they represent a cheap and often very rich source of information about clinical practice. Of course, observational data obtained through EHRs have also potential severe limitations, such as the presence of confounding and missing data. Thus, it is important that suitable methods or designs are used in order to obtain relevant causal estimates of the effects of interventions or policies. The Design (RDD) is a quasi-experimental design, originated in the 1960s in the field of econometrics and it has received some attention in biostatistics, in the recent year. The basic idea is that, when interventions are applied according to some external guideline (associated with a continuous assignment variable) so that individuals 鈥渏ust above鈥 a given threshold experience the intervention and those 鈥渏ust below鈥 do not. Close enough to the threshold, individuals can be reasonably considered as exchangeable and thus the analysis of the RDD mimics that of experimental settings. In this talk, I will address some of the issues associated with the application of the RDD using EHRs, particularly under a Bayesian framework. Speaker:
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