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Statistical inference in matched case-control studies of recurrent events
Yin Bun Cheung1,2,3, Xiangmei Ma2, K F Lam2,4
1Signature Programme in Health Services & Systems Research, Duke-NUS Medical School, Singapore 169857.
International Journal of Epidemiology
|March 4, 2020
Summary
A new method for analyzing recurrent events in case-control studies provides accurate confidence intervals, unlike standard conditional logistic regression methods. This approach is suitable for complex data structures in recurrent event analysis.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Concurrent sampling design is used for case-control studies with recurrent events, involving time matching.
- Standard conditional logistic regression (CLR) analysis overlooks dependencies in recurrent events.
- Existing CLR methods for clustered data do not accommodate the concurrent sampling design's complex data structure.
Purpose of the Study:
- To propose a novel statistical method for analyzing recurrent events in case-control studies using concurrent sampling.
- To evaluate the proposed method's performance against existing conditional logistic regression approaches.
Main Methods:
- Breaking matches and applying unconditional logistic regression with time adjustments.
- Utilizing robust standard errors for clustered observations within individuals.
- Conducting extensive simulations and analyzing childhood pneumonia data.
Main Results:
- The proposed method and CLR yielded similar point estimates and low bias.
- The proposed method achieved target confidence interval coverage probability.
- CLR methods failed to achieve target coverage except in low disease incidence scenarios.
Conclusions:
- The proposed method is effective for analyzing case-control studies with recurrent events.
- This method offers improved confidence interval accuracy compared to standard CLR for recurrent event data.
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