The large odds ratio and the wide confidence interval, what are the potential reasons for these findings? Is this result reliable?

You may face a specific problem associated with your dataset and close to perfect separation of the dependent variable based on an independent variable (separation or quasi-separation problem). I invite you to read this intersting post on stack exchange (Very wide confidence intervals for odds ratios - Cross Validated (stackexchange.com)) as well as paper from Irala which gives interesting insights based on a specific example.

To interpret correctly this OR you could use adjusted prediction of your model using for example ggeffect R package.

(ggeffects: Marginal Means And Adjusted Predictions Of Regression Models • ggeffects (strengejacke.github.io)).

I hope this helps!

Irala, J. D., Fernández-Crehuet Navajas, R., & Serrano del Castillo, A. (1997). Abnormally wide confidence intervals in logistic regression: interpretation of statistical program results. Revista Panamericana de Salud Pública, 2, 268-271.

Pandia Vadivu Pandian

otential Reasons for Large Odds Ratio and Wide Confidence Interval:

Small Sample Size:Small studies or studies with small sample sizes often result in larger ORs and wider CIs. This is because smaller samples have greater variability and uncertainty in their estimates of effect size.

Rare Outcome:If the outcome of interest is rare in the population being studied, the OR tends to be larger because the odds of exposure among cases (or non-cases) can be dramatically different. However, this can also lead to wider CIs due to the smaller number of events.

Confounding Variables:Uncontrolled confounding variables can inflate ORs. If an important confounder is not accounted for in the analysis, it can exaggerate the association between exposure and outcome, resulting in a larger OR.

Measurement Error:Errors in measurement of exposure or outcome can lead to biased estimates of ORs. This may result in larger ORs if measurement error is differential (i.e., differentially affecting exposed versus unexposed groups).

Selection Bias:Biased selection of study participants can distort the association between exposure and outcome, leading to larger ORs. For example, if participants are not representative of the target population, the OR may not generalize well.

Publication Bias:Studies with statistically significant results (often larger ORs) are more likely to be published, leading to an overestimation of the true effect size in the literature.

Assessing the Reliability of the Result:

Consider the Study Design:A well-designed study with appropriate control for confounding, adequate sample size, and reliable measurement methods enhances the reliability of the OR estimate.

Evaluate Precision:Despite a wide CI, if the point estimate of the OR is clinically or scientifically meaningful and consistent with prior research, it may still be reliable.

Examine Consistency:Check if the findings are consistent across different studies and populations. Consistency strengthens the reliability of the observed association.

Sensitivity Analysis:Conduct sensitivity analyses to explore the impact of potential biases or methodological choices on the OR estimate. This can provide insights into the robustness of the findings.

Clinical or Public Health Relevance:Consider the clinical or public health implications of the findings. A large OR may still be relevant if it has practical significance in decision-making or intervention planning.

Conclusion:

While a large odds ratio and wide confidence interval can indicate significant associations, careful interpretation is crucial. Assessing the reliability involves understanding potential sources of bias, evaluating study quality, and considering the context of the findings. Larger studies with rigorous methods and consistent results across different settings enhance confidence in the reliability of the observed association.

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