Barbra A Dickerman1, Xabier García-Albéniz1,2, Roger W Logan1, Spiros Denaxas3,4,5, Miguel A Hernán1,6,7. 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 2. RTI Health Solutions, Barcelona, Spain. 3. Institute of Health Informatics Research, University College London, London, UK. 4. Health Data Research UK (HDR UK) London, University College London, London, UK. 5. Alan Turing Institute, London, UK. 6. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 7. Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.
Abstract
BACKGROUND: Previous case-control studies have reported a strong association between statin use and lower cancer risk. It is unclear whether this association reflects a benefit of statins or is the result of design decisions that cannot be mapped to a (hypothetical) target trial (that would answer the question of interest). METHODS: We outlined the protocol of a target trial to estimate the effect of statins on colorectal cancer incidence among adults with low-density lipoprotein (LDL) cholesterol below 5 mmol/L. We then emulated the target trial using linked electronic health records of 752 469 eligible UK adults (CALIBER 1999-2016) under both a cohort design and a case-control sampling of the cohort. We used pooled logistic regression to estimate intention-to-treat and per-protocol effects of statins on colorectal cancer, with adjustment for baseline and time-varying risk factors via inverse-probability weighting. Finally, we compared our case-control effect estimates with those obtained using previous case-control procedures. RESULTS: Over the 6-year follow-up, 3596 individuals developed colorectal cancer. Estimated intention-to-treat and per-protocol hazard ratios were 1.00 (95% confidence interval [CI]: 0.87, 1.16) and 0.90 (95% CI: 0.71, 1.12), respectively. As expected, adequate case-control sampling yielded the same estimates. By contrast, previous case-control analytical approaches yielded estimates that appeared strongly protective (odds ratio 0.57, 95% CI: 0.36, 0.91, for ≥5 vs. <5 years of statin use). CONCLUSIONS: Our study demonstrates how to explicitly emulate a target trial using case-control data to reduce discrepancies between observational and randomized trial evidence. This approach may inform future case-control analyses for comparative effectiveness research.
RCT Entities:
BACKGROUND: Previous case-control studies have reported a strong association between statin use and lower cancer risk. It is unclear whether this association reflects a benefit of statins or is the result of design decisions that cannot be mapped to a (hypothetical) target trial (that would answer the question of interest). METHODS: We outlined the protocol of a target trial to estimate the effect of statins on colorectal cancer incidence among adults with low-density lipoprotein (LDL) cholesterol below 5 mmol/L. We then emulated the target trial using linked electronic health records of 752 469 eligible UK adults (CALIBER 1999-2016) under both a cohort design and a case-control sampling of the cohort. We used pooled logistic regression to estimate intention-to-treat and per-protocol effects of statins on colorectal cancer, with adjustment for baseline and time-varying risk factors via inverse-probability weighting. Finally, we compared our case-control effect estimates with those obtained using previous case-control procedures. RESULTS: Over the 6-year follow-up, 3596 individuals developed colorectal cancer. Estimated intention-to-treat and per-protocol hazard ratios were 1.00 (95% confidence interval [CI]: 0.87, 1.16) and 0.90 (95% CI: 0.71, 1.12), respectively. As expected, adequate case-control sampling yielded the same estimates. By contrast, previous case-control analytical approaches yielded estimates that appeared strongly protective (odds ratio 0.57, 95% CI: 0.36, 0.91, for ≥5 vs. <5 years of statin use). CONCLUSIONS: Our study demonstrates how to explicitly emulate a target trial using case-control data to reduce discrepancies between observational and randomized trial evidence. This approach may inform future case-control analyses for comparative effectiveness research.
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