Liver Resection Improves Survival in Colorectal Cancer Patients: Causal-effects From Population-level Instrumental Variable Analysis


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Abstract

Objective:The aim of this study was to estimate population-level causal effects of liver resection on survival of patients with colorectal cancer liver metastases (CRC-LM).Background:A randomized trial to prove that liver resection improves survival in patients with CRC-LM is neither feasible nor ethical. Here, we test this assertion using instrumental variable (IV) analysis that allows for causal-inference by controlling for observed and unobserved confounding effects.Methods:We abstracted data on patients with synchronous CRC-LM using the California Cancer Registry from 2000 to 2012 and linked the records to the Office of Statewide Health Planning and Development Inpatient Database. We used 2 instruments: resection rates in a patient's neighborhood (within 50-mile radius)—NALR rate; and Medical Service Study Area resection rates—MALR rate. IV analysis was performed using the 2SLS method.Results:A total of 24,828 patients were diagnosed with stage-IV colorectal cancer of which 16,382 (70%) had synchronous CRC-LM. Liver resection was performed in 1635 (9.8%) patients. NALR rates ranged from 8% (lowest-quintile) to 11% (highest-quintile), whereas MALR rates ranged from 3% (lowest quintile) to 19% (highest quintile). There was a strong association between instruments and probability of liver resection (F-statistic at median cut-off: NALR 24.8; MALR 266.8; P < 0.001). IV analysis using both instruments revealed a 23.6 month gain in survival (robust SE 4.4, P < 0.001) with liver resection for patients whose treatment choices were influenced by the rates of resection in their geographic area (marginal patients), after accounting for measured and unmeasured confounders.Conclusion:Less than 10% of patients with CRC-LM had liver resection. Significant geographic variation in resection rates is attributable to community biases. Liver resection leads to extensive survival benefit, accounting for measured and unmeasured confounders.

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