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Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure-response relationships for large-molecule oncology drugs.

ABSTRACT: A Cox proportional hazard (CoxPH) model is conventionally used to assess exposure-response (E-R), but its performance to uncover the ground truth when only one dose level of data is available has not been systematically evaluated. We established a simulation workflow to generate realistic E-R datasets to assess the performance of the CoxPH model in recovering the E-R ground truth in various scenarios, considering two potential reasons for the confounded E-R relationship. We found that at high doses, when the pharmacological effects are largely saturated, missing important confounders is the major reason for inferring false-positive E-R relationships. At low doses, when a positive E-R slope is the ground truth, either missing important confounders or mis-specifying the interactions can lead to inaccurate estimates of the E-R slope. This work constructed a simulation workflow generally applicable to clinical datasets to generate clinically relevant simulations and provide an in-depth interpretation on the E-R relationships with confounders inferred by the conventional CoxPH model.

SUBMITTER: Poon V

PROVIDER: S-EPMC9662202 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure-response relationships for large-molecule oncology drugs.

Poon Victor V Lu Dan D

CPT: pharmacometrics & systems pharmacology 20220916 11

A Cox proportional hazard (CoxPH) model is conventionally used to assess exposure-response (E-R), but its performance to uncover the ground truth when only one dose level of data is available has not been systematically evaluated. We established a simulation workflow to generate realistic E-R datasets to assess the performance of the CoxPH model in recovering the E-R ground truth in various scenarios, considering two potential reasons for the confounded E-R relationship. We found that at high do ...[more]

PMID: 35988264

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Project description:PurposeSignet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required.MethodsBaseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to assess the prediction accuracy. Decision curve analysis (DCA) was used to assess the clinical applicability of the proposed model. For treatment modalities, Kaplan-Meier curves were adopted to analyze condition-specific effects.ResultsWe obtained 731 patients diagnosed with lung signet ring cell carcinoma (LSRCC) in the SEER database and randomized the patients into a training group (551) and a validation group (220) with a ratio of 7:3. Eight factors including age, primary site, T, N, and M.Stage, surgery, chemotherapy, and radiation were included in the nomogram analysis. Results suggested that treatment methods (like surgery, chemotherapy, and radiation) and T-Stage factors had significant prognostic effects. The results of ROC curves, calibration curves, and DCA in the training and validation groups demonstrated that the nomogram we constructed could precisely predict survival and prognosis in LSRCC patients. Through deep verification, we found the constructed model had a high C-index, indicating that the model had a strong predictive power. Further, we found that all surgical interventions had good effects on OS and cancer-specific survival (CSS). The survival curves showed a relatively favorable prognosis for T0 patients overall, regardless of the treatment modality.ConclusionsOur nomogram is demonstrated to be clinically beneficial for the prognosis of LSRCC patients. The surgical intervention was successful regardless of the tumor stage, and the Cox proportional hazard (CPH) model had better performance than the machine learning model in terms of effectiveness.

Dataset Information

Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure-response relationships for large-molecule oncology drugs.

Publications

Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure-response relationships for large-molecule oncology drugs.

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