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Dealing with missing information on covariates for excess mortality hazard regression models - Making the imputation
Luís Antunes1,2, Denisa Mendonça2,3, Maria José Bento1,3
1Grupo de Epidemiologia de Cancro, Centro de Investigação do IPO Porto (CI-IPOP), Instituto Português de Oncologia do Porto (IPO Porto), Porto, Portugal.
Statistical Methods in Medical Research
|September 2, 2021
Summary
The substantive model compatible-fully conditional specification (SMC-FCS) method accurately handles missing data in cancer survival analyses, outperforming standard imputation techniques. This approach reveals socioeconomic disparities in colorectal cancer survival, highlighting higher risks for deprived populations.
Area of Science:
- Epidemiology
- Biostatistics
- Survival Analysis
Background:
- Missing data is prevalent in epidemiological studies, potentially compromising the validity of statistical inference.
- Multiple imputation is a common technique, but model incompatibility can lead to biased results, especially with nonlinear models.
- Existing methods struggle with complex associations and nonlinearities in survival data analysis.
Purpose of the Study:
- To extend the substantive model compatible-fully conditional specification (SMC-FCS) multiple imputation approach for excess hazard regression models.
- To evaluate the performance of the extended SMC-FCS method against standard multiple imputation and complete-case analysis.
- To apply the SMC-FCS method to investigate socioeconomic inequalities in colorectal cancer survival.
Main Methods:
- Extended the SMC-FCS multiple imputation technique to accommodate excess hazard regression models.
- Conducted a simulation study comparing SMC-FCS, standard fully conditional specification (FCS) multiple imputation, and complete-case analysis.
- Applied the SMC-FCS algorithm to population-based colorectal cancer survival data from Portugal.
Main Results:
- The SMC-FCS approach yielded unbiased estimates and appropriate coverage probabilities in simulation scenarios.
- Standard FCS multiple imputation resulted in biased estimates and poor empirical coverage.
- Analysis of colorectal cancer data revealed higher excess hazards for patients from more deprived socioeconomic areas.
Conclusions:
- The extended SMC-FCS method provides a valid and robust approach for handling missing data in excess hazard regression models.
- SMC-FCS effectively addresses model incompatibility issues common in complex survival data analyses.
- The study demonstrates the utility of SMC-FCS in uncovering socioeconomic disparities in cancer survival, informing public health strategies.
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