Mustafa Raoof1, Sidra Haye2, Philip H G Ituarte1, Yuman Fong1. 1. Department of Surgery, City of Hope National Medical Center, Duarte, CA. 2. Department of Economics, University of California Irvine, Irvine, CA.
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.
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.
Authors: Mustafa Raoof; Zeljka Jutric; Sidra Haye; Philip H G Ituarte; Beiqun Zhao; Gagandeep Singh; Laleh Melstrom; Susanne G Warner; Bryan Clary; Yuman Fong Journal: Cancer Med Date: 2020-07-20 Impact factor: 4.452
Authors: Lucas Ernani; Eduardo de Souza Martins Fernandes; Rodrigo Bronze de Martino; Fabricio Ferreira Coelho; Felipe Pedreira Tavares de Mello; Ronaldo Andrade; Leandro Savattone Pimentel; Luciana Bertocco de Paiva Haddad; Paulo Herman; Wellington Andraus; Luiz Augusto Carneiro D'Albuquerque Journal: Arq Bras Cir Dig Date: 2022-01-31
Authors: Nina D Arhin; Chan Shen; Christina E Bailey; Lea K Matsuoka; Alexander T Hawkins; Andreana N Holowatyj; Kristen K Ciombor; Michael B Hopkins; Timothy M Geiger; Audrey E Kam; Marc T Roth; Cody M Lebeck Lee; Michael Lapelusa; Arvind Dasari; Cathy Eng Journal: Cancer Med Date: 2021-06-16 Impact factor: 4.452