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Fenton D, Diaz A, Chen H, Armstrong M, Auger S, Bujnowski D, Pedroza A, Polster S, Das P, Horowitz P, Roxbury C. Determining the association of insurance coverage and survival outcomes in patients with olfactory neuroblastoma utilizing machine learning. Int Forum Allergy Rhinol 2023; 13:2082-2085. [PMID: 37132277 DOI: 10.1002/alr.23172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/04/2023]
Abstract
KEY POINTS We use machine learning to examine health insurance and mortality in olfactory neuroblastoma. Private insurance significantly improved survival even after adjusting for confounders. The regression model also found no statistical difference between Medicare and no insurance.
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Affiliation(s)
- David Fenton
- Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Ashley Diaz
- Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Haobin Chen
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Mikhayla Armstrong
- Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Samuel Auger
- Department of Surgery, Section of Otolaryngology, University of Chicago, Chicago, Illinois, USA
| | - Daniel Bujnowski
- Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
| | - Alena Pedroza
- Univerisity of Chicago, Division of the Biological Sciences, Chicago, Illinois, USA
| | - Sean Polster
- Department of Surgery, Section of Neurological Surgery, University of Chicago, Chicago, Illinois, USA
| | - Paramita Das
- Department of Surgery, Section of Neurological Surgery, University of Chicago, Chicago, Illinois, USA
| | - Peleg Horowitz
- Department of Surgery, Section of Neurological Surgery, University of Chicago, Chicago, Illinois, USA
| | - Christopher Roxbury
- Department of Surgery, Section of Otolaryngology, University of Chicago, Chicago, Illinois, USA
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Chen S, Deng Y, Zhai Y, Zhan Y, Li X, Wang J, Bai H. Clinical Analysis and Prognostic Prediction Model for Patients with Uterine Leiomyosarcoma at FIGO Stage I. Int J Womens Health 2023; 15:545-557. [PMID: 37069963 PMCID: PMC10105571 DOI: 10.2147/ijwh.s406381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/06/2023] [Indexed: 04/19/2023] Open
Abstract
Purpose To reveal the clinical status and construct a predictive prognostic model for patients with uterine leiomyosarcoma (uLMS) at International Federation of Gynecology and Obstetrics (FIGO) stage I. Patients and Methods The medical records of patients with stage I uLMS during the study period were retrospectively reviewed. Multiple imputation, Martingale residuals and restricted cubic spline were used for data processing. Univariate and multivariate analyses were used to determine independent prognostic factors. The Schoenfeld individual test was used to verify the proportional hazards (PH) assumption. The predictive ability of the nomogram was validated internally. Results Ultimately, 102 patients were included. The median age at diagnosis was 51 years old. During the medium follow-up time of 68 months, 55 (53.9%) patients developed recurrence. The median recurrence interval was 32 months. The most common metastatic site was the lung (27 cases). Eventually, 38 (37.3%) patients died of uLMS. The 3-year and 5-year overall survival rates were 66.0% and 52.0%, respectively. Age at diagnosis >49 years, larger tumor size, MI>10/10HPF, presence of LVSI and Ki-67 labeling index (LI) >25% (P=0.0467, 0.0077, 0.0475, 0.0294, and 0.0427, respectively) were independent prognostic factors. The PH assumption remained inviolate. The concordance index was 0.847, the area under the time-dependent receiver operating characteristic curve surpassed 0.7, and the calibration curve showed gratifying consistency. Conclusion Age at diagnosis, tumor size, MI, LVSI, and Ki-67 LI were identified as independent prognostic factors for stage I uLMS. This prognostic nomogram would provide personalized assessment with superior predictive performance.
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Affiliation(s)
- Shuya Chen
- Department of Gynecological Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China
| | - Yunhan Deng
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yan Zhai
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Yang Zhan
- Department of Pathology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China
| | - Xue Li
- Department of Pathology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
| | - Jiandong Wang
- Department of Gynecological Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China
- Correspondence: Jiandong Wang, Department of Gynecological Oncology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Qihelou Street, Dongcheng District, Beijing, 100006, People’s Republic of China, Tel +0086-13910566755, Email
| | - Huimin Bai
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, People’s Republic of China
- Huimin Bai, Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, No. 8, North Road of Workers Stadium, Chaoyang District, Beijing, 100020, People’s Republic of China, Tel +0086-01085231765, Email
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3
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Cao Y, Yu J. Adjusting for unmeasured confounding in survival causal effect using validation data. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Genomic context and TP53 allele frequency define clinical outcomes in TP53-mutated myelodysplastic syndromes. Blood Adv 2021; 4:482-495. [PMID: 32027746 DOI: 10.1182/bloodadvances.2019001101] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 12/16/2019] [Indexed: 12/26/2022] Open
Abstract
TP53 mutations are associated with adverse outcomes and shorter response to hypomethylating agents (HMAs) in myelodysplastic syndrome (MDS). Limited data have evaluated the impact of the type, number, and patterns of TP53 mutations in response outcomes and prognosis of MDS. We evaluated the clinicopathologic characteristics, outcomes, and response to therapy of 261 patients with MDS and TP53 mutations. Median age was 68 years (range, 18-80 years). A total of 217 patients (83%) had a complex karyotype. TP53 mutations were detected at a median variant allele frequency (VAF) of 0.39 (range, 0.01-0.94). TP53 deletion was associated with lower overall response rate (ORR) (odds ratio, 0.3; P = .021), and lower TP53 VAF correlated with higher ORR to HMAs. Increase in TP53 VAF at the time of transformation was observed in 13 patients (61%), and previously undetectable mutations were observed in 15 patients (65%). TP53 VAF was associated with worse prognosis (hazard ratio, 1.02 per 1% VAF increase; 95% confidence interval, 1.01-1.03; P < .001). Integration of TP53 VAF and karyotypic complexity identified prognostic subgroups within TP53-mutant MDS. We developed a multivariable model for overall survival that included the revised International Prognostic Scoring System (IPSS-R) categories and TP53 VAF. Total score for each patient was calculated as follows: VAF TP53 + 13 × IPSS-R blast score + 16 × IPSS-R cytogenetic score + 28 × IPSS-R hemoglobin score + 46 × IPSS-R platelet score. Use of this model identified 4 prognostic subgroups with median survival times of not reached, 42.2, 21.9, and 9.2 months. These data suggest that outcomes of patients with TP53-mutated MDS are heterogeneous and that transformation may be driven not only by TP53 but also by other factors.
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Han K, Lumley T, Shepherd BE, Shaw PA. Two-phase analysis and study design for survival models with error-prone exposures. Stat Methods Med Res 2020; 30:962280220978500. [PMID: 33327876 PMCID: PMC8715910 DOI: 10.1177/0962280220978500] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
Increasingly, medical research is dependent on data collected for non-research purposes, such as electronic health records data. Health records data and other large databases can be prone to measurement error in key exposures, and unadjusted analyses of error-prone data can bias study results. Validating a subset of records is a cost-effective way of gaining information on the error structure, which in turn can be used to adjust analyses for this error and improve inference. We extend the mean score method for the two-phase analysis of discrete-time survival models, which uses the unvalidated covariates as auxiliary variables that act as surrogates for the unobserved true exposures. This method relies on a two-phase sampling design and an estimation approach that preserves the consistency of complete case regression parameter estimates in the validated subset, with increased precision leveraged from the auxiliary data. Furthermore, we develop optimal sampling strategies which minimize the variance of the mean score estimator for a target exposure under a fixed cost constraint. We consider the setting where an internal pilot is necessary for the optimal design so that the phase two sample is split into a pilot and an adaptive optimal sample. Through simulations and data example, we evaluate efficiency gains of the mean score estimator using the derived optimal validation design compared to balanced and simple random sampling for the phase two sample. We also empirically explore efficiency gains that the proposed discrete optimal design can provide for the Cox proportional hazards model in the setting of a continuous-time survival outcome.
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Affiliation(s)
- Kyunghee Han
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Pennsylvania, PA, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Bryan E Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Pamela A Shaw
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Pennsylvania, PA, USA
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Zhang X, Stamey JD, Mathur MB. Assessing the impact of unmeasured confounders for credible and reliable real-world evidence. Pharmacoepidemiol Drug Saf 2020; 29:1219-1227. [PMID: 32929830 DOI: 10.1002/pds.5117] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 08/17/2020] [Accepted: 08/20/2020] [Indexed: 12/23/2022]
Abstract
PURPOSE We review statistical methods for assessing the possible impact of bias due to unmeasured confounding in real world data analysis and provide detailed recommendations for choosing among the methods. METHODS By updating an earlier systematic review, we summarize modern statistical best practices for evaluating and correcting for potential bias due to unmeasured confounding in estimating causal treatment effect from non-interventional studies. RESULTS We suggest a hierarchical structure for assessing unmeasured confounding. First, for initial sensitivity analyses, we strongly recommend applying a recently developed method, the E-value, that is straightforward to apply and does not require prior knowledge or assumptions about the unmeasured confounder(s). When some such knowledge is available, the E-value could be supplemented by the rule-out or array method at this step. If these initial analyses suggest results may not be robust to unmeasured confounding, subsequent analyses could be conducted using more specialized statistical methods, which we categorize based on whether they require access to external data on the suspected unmeasured confounder(s), internal data, or no data. Other factors for choosing the subsequent sensitivity analysis methods are also introduced and discussed, including the types of unmeasured confounders and whether the subsequent sensitivity analysis is intended to provide a corrected causal treatment effect. CONCLUSION Various analytical methods have been proposed to address unmeasured confounding, but little research has discussed a structured approach to select appropriate methods in practice. In providing practical suggestions for choosing appropriate initial and, potentially, more specialized subsequent sensitivity analyses, we hope to facilitate the widespread reporting of such sensitivity analyses in non-interventional studies. The suggested approach also has the potential to inform pre-specification of sensitivity analyses before executing the analysis, and therefore increase the transparency and limit selective study reporting.
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Affiliation(s)
- Xiang Zhang
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
| | - James D Stamey
- Department of Statistics, Baylor University, Waco, Texas, USA
| | - Maya B Mathur
- Quantitative Sciences Unit, Stanford University, Stanford, California, USA
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Murad H, Dankner R, Berlin A, Olmer L, Freedman LS. Imputing missing time-dependent covariate values for the discrete time Cox model. Stat Methods Med Res 2019; 29:2074-2086. [PMID: 31680633 DOI: 10.1177/0962280219881168] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We describe a procedure for imputing missing values of time-dependent covariates in a discrete time Cox model using the chained equations method. The procedure multiply imputes the missing values for each time-period in a time-sequential manner, using covariates from the current and previous time-periods as well as the survival outcome. The form of the outcome variable used in the imputation model depends on the functional form of the time-dependent covariate(s) and differs from the case of Cox regression with only baseline covariates. This time-sequential approach provides an approximation to a fully conditional approach. We illustrate the procedure with data on diabetics, evaluating the association of their glucose control with the risk of selected cancers. Using simulations we show that the suggested estimator performed well (in terms of bias and coverage) for completely missing at random, missing at random and moderate non-missing-at-random patterns. However, for very strong non-missing-at-random patterns, the estimator was seriously biased and the coverage was too low. The procedure can be implemented using multiple imputation with the Fully conditional Specification (FCS) method (MI procedure in SAS with FCS statement or similar packages in other software, e.g. MICE in R). For use with event times on a continuous scale, the events would need to be grouped into time-intervals.
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Affiliation(s)
- Havi Murad
- Biostatistics and Biomathematics Unit, Gertner Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Rachel Dankner
- Cardiovascular Epidemiology Unit, Gertner Institute, Sheba Medical Center, Tel-Hashomer, Israel.,Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, School of Public Health, Tel-Aviv University, Tel-Aviv, Israel
| | - Alla Berlin
- Cardiovascular Epidemiology Unit, Gertner Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Liraz Olmer
- Biostatistics and Biomathematics Unit, Gertner Institute, Sheba Medical Center, Tel-Hashomer, Israel
| | - Laurence S Freedman
- Biostatistics and Biomathematics Unit, Gertner Institute, Sheba Medical Center, Tel-Hashomer, Israel
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Kuenzig ME, Sadatsafavi M, Aviña-Zubieta JA, Burne RM, Abrahamowicz M, Beauchamp ME, Kaplan GG, Benchimol EI. Asthma is not associated with the need for surgery in Crohn's disease when controlling for smoking status: a population-based cohort study. Clin Epidemiol 2018; 10:831-840. [PMID: 30038523 PMCID: PMC6049604 DOI: 10.2147/clep.s156772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Purpose Growing evidence suggests asthma and Crohn’s disease commonly cooccur. However, the impact of asthma on the prognosis of Crohn’s disease is unknown. The aim of our study was to assess the effect of asthma on the need for intestinal resection in patients with Crohn’s disease while adjusting for smoking status, imputed from a smaller, secondary data set. Patients and methods Using health administrative data from a universally funded healthcare plan in Alberta, Canada, we conducted a cohort study to assess the effect of asthma on the need for surgery in patients with Crohn’s disease diagnosed between 2002 and 2008 (N=2,113). Validated algorithms were used to identify incident cases of Crohn’s disease, cooccurring asthma, and intestinal resection. The association between asthma and intestinal resection was estimated using multivariable Cox proportional hazards regression. Smoking status was imputed using a novel method using martingale residuals, derived from a data set of 485 patients enrolled in the Alberta Inflammatory Bowel Disease Consortium (2007 to 2014) who completed environmental questionnaires. All analyses were adjusted for age, sex, rural/urban status, and mean neighborhood income quintile. Results Asthma did not increase the risk of surgery in the health administrative data when not adjusting for smoking status (HR 1.03, 95% CI 0.81 to 1.29). The association remained nonsignificant after imputing smoking status in the health administrative data (HR 1.03, 95% CI 0.81 to 1.29). Conclusion Although asthma is associated with an increased risk of Crohn’s disease, co-occurring asthma is not associated with the risk of surgery in these patients. This null association persisted after adjusting for smoking status. This study described a novel method to adjust for confounding (smoking status) in time-to-event analyses, even when the confounding variable is unmeasured in health administrative data.
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Affiliation(s)
- M Ellen Kuenzig
- Children's Hospital of Eastern Ontario (CHEO) Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada, .,Health Information Technology Program, CHEO Research Institute, Ottawa, ON, Canada, .,Institute for Clinical Evaluative Sciences, Ottawa, ON, Canada,
| | - Mohsen Sadatsafavi
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
| | - J Antonio Aviña-Zubieta
- Arthritis Research Canada, Division of Rheumatology, University of British Columbia, Vancouver, BC, Canada
| | - Rebecca M Burne
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Centre for Health Outcomes Research (CORE), Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Marie-Eve Beauchamp
- Centre for Health Outcomes Research (CORE), Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Gilaad G Kaplan
- Department of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Eric I Benchimol
- Children's Hospital of Eastern Ontario (CHEO) Inflammatory Bowel Disease Centre, Division of Gastroenterology, Hepatology and Nutrition, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada, .,Health Information Technology Program, CHEO Research Institute, Ottawa, ON, Canada, .,Institute for Clinical Evaluative Sciences, Ottawa, ON, Canada, .,Department of Pediatrics, University of Ottawa; Ottawa, ON, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
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Jackson JW, Schmid I, Stuart EA. Propensity Scores in Pharmacoepidemiology: Beyond the Horizon. CURR EPIDEMIOL REP 2017; 4:271-280. [PMID: 29456922 DOI: 10.1007/s40471-017-0131-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Purpose of review Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding. In this review, we examine the application of propensity score methods in pharmacoepidemiology with particular attention to these and other issues, with an eye towards standards of practice, recent methodological advances, and opportunities for future progress. Recent findings Propensity score methods have matured in ways that can advance comparative effectiveness and safety research in pharmacoepidemiology. These include natural extensions for categorical treatments, matching algorithms that can optimize sample size given design constraints, weighting estimators that asymptotically target matched and overlap samples, and the incorporation of machine learning to aid in covariate selection and model building. Summary These recent and encouraging advances should be further evaluated through simulation and empirical studies, but nonetheless represent a bright path ahead for the observational study of treatment benefits and harms.
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Affiliation(s)
- John W Jackson
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.,Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Ian Schmid
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | - Elizabeth A Stuart
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205.,Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
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Burne RM, Abrahamowicz M. Adjustment for time-dependent unmeasured confounders in marginal structural Cox models using validation sample data. Stat Methods Med Res 2017; 28:357-371. [DOI: 10.1177/0962280217726800] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Large databases used in observational studies of drug safety often lack information on important confounders. The resulting unmeasured confounding bias may be avoided by using additional confounder information, frequently available in smaller clinical “validation samples”. Yet, no existing method that uses such validation samples is able to deal with unmeasured time-varying variables acting as both confounders and possible mediators of the treatment effect. We propose and compare alternative methods which control for confounders measured only in a validation sample within marginal structural Cox models. Each method corrects the time-varying inverse probability of treatment weights for all subject-by-time observations using either regression calibration of the propensity score, or multiple imputation of unmeasured confounders. Two proposed methods rely on martingale residuals from a Cox model that includes only confounders fully measured in the large database, to correct inverse probability of treatment weight for imputed values of unmeasured confounders. Simulation demonstrates that martingale residual-based methods systematically reduce confounding bias over naïve methods, with multiple imputation including the martingale residual yielding, on average, the best overall accuracy. We apply martingale residual-based imputation to re-assess the potential risk of drug-induced hypoglycemia in diabetic patients, where an important laboratory test is repeatedly measured only in a small sub-cohort.
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Affiliation(s)
- Rebecca M Burne
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Canada
| | - Michal Abrahamowicz
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Canada
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