Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

Abstract

The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer’s disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

Keywords:

• epilepsy
• epidemiology
• neurostatistics
• causal inference

Acknowledgments

The authors acknowledge Miguel Hernan, MD, PhD, MPH, MSc of the Harvard T Chan School of Public Health, Departments of Epidemiology and Biostatistics for statistical consultation. LMVRM is the recipient of a 2015–2016 Clinical Research Fellowship sponsored by the American Brain Foundation. MBW receives grant funding from NIH (NIH-NINDS 1K23NS090900). JH receives grant funding from NIH (1R01 CA164023-04, 2P01AG032952-06A1, R01 HD075121-04, R01 MH104560-02).

Author contributions

All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.