Effect Of Hormone Therapy Within Risk Groups Defined By Generalized Competing Event Model: Ancillary Analysis Of NRG Oncology’s RTOG 9408

Mell, Loren K.; Pugh, S.; Jones, Clifford; Nelson, Tyler J.; Morginstin, Mark; Gore, Elizabeth; Zakeri, Kaveh; Bahary, Jean-Paul; Souhami, L.; Michalski, J.; Hartford, Alan C.; Mishra, M.; Roach, Mack; Parliament, Matthew; Choi, Kyung San; Pisansky, T.M.; Husain, Siraj; Malone, Shauna; Horwitz, Edwin M.; Feng, F.Y.

doi:10.1016/j.ijrobp.2020.07.435

Cited by 2 publications

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“…1. Effects of intensive treatment approaches for time periods during which the proportional hazards assumption was found to be valid (for prostate cancer, 3.1-10.4 years, with the time point of 10.4 years chosen based on published literature 25 ; for lung cancer, excluding the first year of follow-up; for head and neck cancer, 2-10 years). Results are presented from unadjusted, multivariable Cox (MVA), and inverse probability treatment weighting (IPTW) models.…”

Section: Discussionmentioning

confidence: 99%

“…ⱡReference values were obtained from randomized trials and meta-analyses ( a surgery vs. active monitoring; b radiotherapy vs. active monitoring; c lobectomy vs. segmentectomy; d locoregional therapy with chemotherapy vs. locoregional therapy alone) [32][33][34]. Effects of intensive treatment approaches for time periods during which the proportional hazards assumption was found to be valid (for prostate cancer, 3.1-10.4 years, with the time point of 10.4 years chosen based on published literature25 ; for lung cancer, excluding the first year of follow-up; for head and neck cancer, 2-10 years). Results are presented from unadjusted, multivariable Cox (MVA), and inverse probability treatment weighting (IPTW) models.…”

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confidence: 99%

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Application of BRACE Method to Address Treatment Selection Bias in Observational Data

Nelson

et al. 2021

Preprint

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Background: Cancer treatments can paradoxically appear to reduce the risk of non-cancer mortality in observational studies, due to residual confounding from treatment selection bias. Here we apply a novel method, Bias Reduction through Analysis of Competing Events (BRACE), to reduce bias in the presence of residual confounding. Methods: We studied 36630 prostate cancer patients, 4069 lung cancer patients, and 7117 head/neck cancer patients, using the Veterans Affairs Informatics and Computing Infrastructure database. We estimated effects of intensive treatment (prostate: prostatectomy vs. radiotherapy; lung: lobectomy vs. sublobar resection or radiotherapy; head/neck: radiotherapy with concurrent cisplatin and/or multiagent induction vs. radiotherapy with or without alternative systemic therapy) on cancer-specific mortality, non-cancer mortality, and overall survival (OS), using both multivariable Cox (MVA) and propensity score (inverse probability treatment weighting (IPTW)) models. Next, we applied the BRACE method to adjust for residual confounding, based on the observed treatment effect on competing event and relative event hazards. Results: For each cohort, intensive treatment was associated with significantly reduced hazards for cancer-specific mortality, non-cancer mortality, and OS. Compared to the results for MVA and IPTW models, hazard ratios (95% confidence intervals) for the effect of intensive treatment on OS were attenuated in each cohort after applying BRACE: (prostate- MVA: 0.75 (0.71, 0.80), IPTW: 0.73 (0.66, 0.75), BRACE: 0.98 (0.95, 1.00); lung- 0.79 (0.68, 0.91), 0.79 (0.66, 0.89), BRACE: 0.81 (0.65, 0.94); head/neck- 0.71 (0.66, 0.76), 0.70 (0.66, 0.76), BRACE: 0.81 (0.76, 0.86)). BRACE estimates were similar to findings from meta-analyses and randomized trials. Conclusions: We found evidence of residual confounding in several observational cohorts after applying standard methods, which were mitigated after applying BRACE. Application of this method could provide more reliable estimates and inferences when residual confounding is identified and represents a novel approach to improving the validity of outcomes research.

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Application of BRACE Method to Address Treatment Selection Bias in Observational Data

Nelson

et al. 2021

Preprint

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Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data

Williamson

Nelson

Thompson

et al. 2022

Clinical Cancer Research

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Purpose: Cancer treatments can paradoxically appear to reduce the risk of noncancer mortality in observational studies, due to residual confounding. Here we introduce a method, Bias Reduction through Analysis of Competing Events (BRACE), to reduce bias in the presence of residual confounding. Experimental Design: BRACE is a novel method for adjusting for bias from residual confounding in proportional hazards models. Using standard simulation methods, we compared BRACE with Cox proportional hazards regression in the presence of an unmeasured confounder. We examined estimator distributions, bias, mean squared error (MSE), and coverage probability. We then estimated treatment effects of high versus low intensity treatments in 36,630 prostate cancer, 4,069 lung cancer, and 7,117 head/neck cancer patients, using the Veterans Affairs database. We analyzed treatment effects on cancer-specific mortality (CSM), noncancer mortality (NCM), and overall survival (OS), using conventional multivariable Cox and propensity score (adjusted using inverse probability weighting) models, versus BRACE-adjusted estimates. Results: In simulations with residual confounding, BRACE uniformly reduced both bias and MSE. In the absence of bias, BRACE introduced bias toward the null, albeit with lower MSE. BRACE markedly improved coverage probability, but with a tendency toward overcorrection for effective but nontoxic treatments. For each clinical cohort, more intensive treatments were associated with significantly reduced hazards for CSM, NCM, and OS. BRACE attenuated OS estimates, yielding results more consistent with findings from randomized trials and meta-analyses. Conclusions: BRACE reduces bias and MSE when residual confounding is present and represents a novel approach to improve treatment effect estimation in nonrandomized studies.

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Effect Of Hormone Therapy Within Risk Groups Defined By Generalized Competing Event Model: Ancillary Analysis Of NRG Oncology’s RTOG 9408

Cited by 2 publications

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Application of BRACE Method to Address Treatment Selection Bias in Observational Data

Application of BRACE Method to Address Treatment Selection Bias in Observational Data

Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data

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