Introduction to Observational Epidemiology

The longitudinal cohort study is the gold standard in observational epidemiology.

This study integrates experimental animal, observational epidemiology, and occupational exposure evidence by applying a pathway-based approach.

In the 1990s, there was intense debate over the merits and demerits of metaanalysis in observational epidemiology, with some arguing for abandoning this approach entirely 2 and others expressing reservations based largely on the heterogeneity of study methods.

Although clinical trials are necessary for vaccine approval, observational epidemiology will be required to evaluate the long-term effectiveness, safety, and population impacts of newly approved COVID-19 vaccines under real-world field conditions.

Human nutrition is often labelled a ‘soft’ science, as it makes less use of the experimental approach than is the case in many other disciplines, instead relying extensively on observational epidemiology, with all the limitations that this entails.

Conventional observational epidemiology suffers from unmeasured confounding and reverse causation.

045), according to the Diabetes Control and Complications Trial (DCCT) and its observational Epidemiology of Diabetes Interventions and Complications (EDIC) study, with an average of 27 years of follow-up in both groups (4).

DESIGN Retrospective observational epidemiology study.

We systematically reviewed the observational epidemiology literature to determine the all-cause mortality (ACM) in undernourished patients with acute heart failure or at risk of malnutrition through a meta-analysis of observational studies.

The International Society for Pharmacoepidemiology (ISPE) funded a working group of ISPE members to develop guidance material for the application and reporting of self‐controlled study designs, similar to Standards of Reporting Observational Epidemiology (STROBE).

Given the exploratory nature of much observational research and the lack of preregistration of protocols and analyses, selective reporting bias may be more influential than confounding for most observational epidemiology.

Despite suggestive observational epidemiology and laboratory studies, there is limited experimental evidence regarding the effect of organic diet on human health.

In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple.

Although the approach enjoys some utility in testing the etiological role of discrete biochemical pathways, like folate metabolism, examples like that of alcohol consumption and cardiovascular disease demonstrate that it must be treated with all of the circumspection that should accompany all forms of observational epidemiology.

From animal studies to observational epidemiology, randomized control trials and their synthesis, there is a pipeline of different study designs providing different nature and quality of information.

This panel, named the Clean Air Scientific Advisory Committee, is currently chaired by a statistician who does not support the use of observational epidemiology for environmental regulatory purposes, arguing that such studies can never prove causation because they do not adequately adjust for potential confounders such as weather and demographic and socioeconomic variables (12).

Improvements in data resources and computational power provide important opportunities to ensure the continued relevance and growth of observational epidemiology.

I argue that observational epidemiology could be improved by focusing greater attention on 1) defining questions that make clear whether the inferential goal is descriptive or causal; 2) greater utilization of quantitative bias analysis and alternative research designs that aim to decrease the strength of assumptions needed to estimate causal effects; and 3) promoting, experimenting with, and perhaps institutionalizing both reproducible research standards and replication studies to evaluate the fragility of study findings in epidemiology.