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Methodical considerations on adjusting for Charlson Comorbidity Index in epidemiological studies
Sören Möller1,2, Mette Bliddal3,4, Katrine Hass Rubin3,4
1Open Patient data Explorative Network, Odense University Hospital, Odense, Denmark. moeller@health.sdu.dk.
Insights
Adjusting for comorbidities in epidemiological studies using the Charlson Comorbidity Index (CCI) requires careful strategy selection. Adjusting for individual comorbidities is best for large samples, while categorized CCI is preferable for smaller studies to minimize bias.
Area of Science:
- Epidemiology
- Biostatistics
- Health Services Research
Background:
- Comorbidities present a significant confounding challenge in epidemiological research.
- The Charlson Comorbidity Index (CCI) is frequently used to adjust for comorbidities in regression analyses.
- Optimal strategies for incorporating CCI into statistical models remain unclear.
Purpose of the Study:
- To compare different Charlson Comorbidity Index (CCI) adjustment strategies in Cox regression.
- To evaluate the effectiveness of various CCI adjustment methods in mitigating confounding and bias.
- To identify the most appropriate CCI adjustment strategy based on sample size and study design.
Main Methods:
- A simulation study was conducted using Cox regression models.
- Different methods of adjusting for the Charlson Comorbidity Index (CCI) were simulated.
- The impact of each adjustment strategy on confounding and bias was assessed.
Main Results:
- Adjusting for each comorbidity as a separate dichotomous covariate proved most effective in mitigating bias in larger sample sizes.
- For smaller studies where individual covariate adjustment is impractical, using the CCI categorized into multiple levels was found to be a preferable strategy.
- Suboptimal CCI adjustment strategies can introduce a small, observable bias, typically within a few percent.
Conclusions:
- The selection of a Charlson Comorbidity Index (CCI) adjustment strategy significantly influences bias mitigation in epidemiological studies.
- Careful consideration of the chosen adjustment method is crucial for achieving desired bias reduction.
- Researchers should be deliberate in their choice of CCI adjustment to ensure robust and reliable study findings.
Abstract:
Confounding by comorbidities is of concern in many epidemiological studies. To take this into account a common strategy is to calculate each participant's Charlson Comorbidity Index (CCI) and use this for adjustment in regression analyses. Various CCI adjustment strategies are possible, and it is unclear, which is preferable. In this simulation study, we compared common adjustment strategies in Cox regression analyses to determine to which degree they mitigate confounding and conservative bias caused by missing adjustment for independent predictors. We found that adjustment for each comorbidity as separate dichotomous covariate is the preferable adjustment strategy in samples of sufficient size as this mitigates both bias sources to the largest degree. If this is impractical in smaller studies adjustment for CCI split into multiple categories is preferable. In conclusion, the choice of CCI adjustment strategy impacts mitigation of bias in this simulation study, and suboptimal adjustment strategies can cause an observable bias, although of quite limited magnitude of only a few percent in this simulation example. Researcher should be careful when deciding on the adjustment strategies applied to ensure that the desired mitigation of bias sources is achieved.
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