scholarly journals Additive Hazard Regression Models: An Application to the Natural History of Human Papillomavirus

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xianhong Xie ◽  
Howard D. Strickler ◽  
Xiaonan Xue

There are several statistical methods for time-to-event analysis, among which is the Cox proportional hazards model that is most commonly used. However, when the absolute change in risk, instead of the risk ratio, is of primary interest or when the proportional hazard assumption for the Cox proportional hazards model is violated, an additive hazard regression model may be more appropriate. In this paper, we give an overview of this approach and then apply a semiparametric as well as a nonparametric additive model to a data set from a study of the natural history of human papillomavirus (HPV) in HIV-positive and HIV-negative women. The results from the semiparametric model indicated on average an additional 14 oncogenic HPV infections per 100 woman-years related to CD4 count < 200 relative to HIV-negative women, and those from the nonparametric additive model showed an additional 40 oncogenic HPV infections per 100 women over 5 years of followup, while the estimated hazard ratio in the Cox model was 3.82. Although the Cox model can provide a better understanding of the exposure disease association, the additive model is often more useful for public health planning and intervention.

2010 ◽  
Vol 18 (2) ◽  
pp. 189-205 ◽  
Author(s):  
Luke Keele

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test.


2021 ◽  
Author(s):  
Casper Wilstrup ◽  
Chris Cave

Abstract Background: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.Results: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion: Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.


2003 ◽  
Vol 21 (24) ◽  
pp. 4560-4567 ◽  
Author(s):  
Andrew L. Feldman ◽  
Steven K. Libutti ◽  
James F. Pingpank ◽  
David L. Bartlett ◽  
Tatiana H. Beresnev ◽  
...  

Purpose: Malignant mesothelioma (MM) arising in the peritoneal cavity is a rare neoplasm characterized by peritoneal progression and for which there are limited therapeutic options. We evaluated the peritoneal progression-free and overall survival (PFS and OS, respectively) for patients with peritoneal MM after surgical resection and regional chemotherapy. Patients and Methods: Forty-nine patients (28 males, 21 females; median age, 47 years; range, 16 to 76 years) with MM underwent laparotomy, tumor resection, continuous hyperthermic peritoneal perfusion with cisplatin (median dose 250 mg/m2), and a single postoperative intraperitoneal dwell of fluorouracil and paclitaxel (n = 35) on protocols approved by the Institutional Review Board. Standard techniques for actuarial analyses of potential prognostic variables (Kaplan-Meier method with two-tailed log-rank test and Cox proportional hazards model) were performed. Results: At a median potential follow-up of 28.3 months, median actuarial PFS is 17 months and actuarial OS is 92 months. Factors associated with improved PFS and OS by the Cox proportional hazards model were a history of previous debulking surgery, absence of deep tissue invasion, minimal residual disease after surgical resection (OS only), and age younger than 60 years (OS only). Conclusion: Surgical resection and regional chemotherapy for MM results in durable PFS and OS. Favorable outcome is associated with age, tumor biology (selection of patients with a history of previous debulking), lack of invasive tumor growth, and minimal residual disease after tumor resection.


2016 ◽  
Vol 35 (1) ◽  
Author(s):  
Ileana Baldi ◽  
Giovannino Ciccone ◽  
Antonio Ponti ◽  
Stefano Rosso ◽  
Roberto Zanetti ◽  
...  

Semiparametric hazard function regression models are among the well studied risk models in survival analysis. The Cox proportional hazards model has been a popular choice in modelling data from epidemiological settings. The Cox-Aalen model is one of the tools for handling the problem of non-proportional effects in the Cox model. We show an application on Piedmont cancer registry data. We initially fit standard Cox model and with the help of the score process we detect the violation of the proportionality assumption. Covariates and risk factors that, on the basis of clinical reasoning, best model baseline hazard are then moved into the additive part of the Cox-Aalen model. Multiplicative effects results are consistent with those of the Cox model whereas only the Cox-Aalen model fully represents the timevarying effect of tumour size.


2021 ◽  
Vol 12 ◽  
Author(s):  
Fahimeh Ramezani Tehrani ◽  
Ali Sheidaei ◽  
Faezeh Firouzi ◽  
Maryam Tohidi ◽  
Fereidoun Azizi ◽  
...  

ObjectivesThere are controversial studies investigating whether multiple anti-Mullerian hormone (AMH) measurements can improve the individualized prediction of age at menopause in the general population. This study aimed to reexplore the additive role of the AMH decline rate in single AMH measurement for improving the prediction of age at physiological menopause, based on two common statistical models for analysis of time-to-event data, including time-dependent Cox regression and Cox proportional-hazards regression models.MethodsA total of 901 eligible women, aged 18–50 years, were recruited from the Tehran Lipid and Glucose Study (TLGS) population and followed up every 3 years for 18 years. The serum AMH level was measured at the time of recruitment and twice after recruitment within 6-year intervals using the Gen II AMH assay. The added value of repeated AMH measurements for the prediction of age at menopause was explored using two different statistical approaches. In the first approach, a time-dependent Cox model was plotted, with all three AMH measurements as time-varying predictors and the baseline age and logarithm of annual AMH decline as time-invariant predictors. In the second approach, a Cox proportional-hazards model was fitted to the baseline data, and improvement of the complex model, which included repeated AMH measurements and the logarithm of the AMH annual decline rate, was assessed using the C-statistic.ResultsThe time-dependent Cox model showed that each unit increase in the AMH level could reduce the risk of menopause by 87%. The Cox proportional-hazards model also improved the prediction of age at menopause by 3%, according to the C-statistic. The subgroup analysis for the prediction of early menopause revealed that the risk of early menopause increased by 10.8 with each unit increase in the AMH annual decline rate.ConclusionThis study confirmed that multiple AMH measurements could improve the individual predictions of the risk of at physiological menopause compared to single AMH measurements. Different alternative statistical approaches can also offer the same interpretations if the essential assumptions are met.


2017 ◽  
Vol 50 (1) ◽  
pp. 303-320 ◽  
Author(s):  
Jonathan Kropko ◽  
Jeffrey J. Harden

The Cox proportional hazards model is a commonly used method for duration analysis in political science. Typical quantities of interest used to communicate results come from the hazard function (for example, hazard ratios or percentage changes in the hazard rate). These quantities are substantively vague, difficult for many audiences to understand and incongruent with researchers’ substantive focus on duration. We propose methods for computing expected durations and marginal changes in duration for a specified change in a covariate from the Cox model. These duration-based quantities closely match researchers’ theoretical interests and are easily understood by most readers. We demonstrate the substantive improvements in interpretation of Cox model results afforded by the methods with reanalyses of articles from three subfields of political science.


2021 ◽  
Author(s):  
Casper Wilstup ◽  
Chris Cave

AbstractHeart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone.We used a newly invented symbolic regression method called the QLat-tice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations.Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.


2014 ◽  
Vol 1049-1050 ◽  
pp. 1613-1616
Author(s):  
Yong Li

The Cox model is commonly used to model survival data as a function of covariates. In this paper we compare the three methods to estimate the variance of the parameters in Cox model and presents the simulation result.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Unnop Jaisamrarn ◽  
Monchai Santipap ◽  
Somsook Santibenchakul

AbstractWe assessed the discontinuation rate and the reason for discontinuation of common contraceptives used by reproductive-aged Thai women. We recruited 1880 women aged 18–45 years from the Family Planning Clinic of the Chulalongkorn Hospital in Bangkok. The participants were followed at three, six and twelve months. A Cox proportional hazards model was used to determine personal risks of discontinuing contraceptives. The incidence rate for discontinuation of combined oral contraceptive pills (COCs), depot medroxyprogesterone acetate (DMPA), copper intrauterine device (IUD), and contraceptive implant(s) were 21.3, 9.2, 4.4, and 2.3/100 person-years, respectively. Most of the women who discontinued (185/222) discontinued contraceptives due to side effects. Compared to contraceptive implant users, the adjusted hazard ratios (aHRs) [95% confidence intervals (CIs)] of discontinuing COCs, DMPA, and the copper IUD were 9.6 (4.3–21.8), 4.2 (1.8–10.0), and 2.2 (0.8–5.9), respectively. Lower income, higher parity, history of miscarriage, and history of abortion were independent predictors of contraceptive discontinuation in a multivariable model.


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
N Taniguchi ◽  
Y Miyasaka ◽  
Y Suwa ◽  
S Harada ◽  
E Nakai ◽  
...  

Abstract Background Heart failure is an important consequence in patients with atrial fibrillation (AF) which is associated with worse prognosis. The H2ARDD score, calculated from 5 clinical risk factors, was reported as a predictor of heart failure events in patients with AF. However, this score has not been externally validated. Purpose The purpose of this study was to evaluate and validate the usefulness of the H2ARDD score for the prediction of heart failure events in AF patients. Methods We used prospective data of patients with AF followed up from 2007 to 2017 in our institute. Patients with active cancer were excluded according to the previous report. H2ARDD score was calculated as follows; history of heart disease=2 points, anemia=1 point, renal dysfunction=1 point, diabetes =1 point, diuretic use=1 point (range from 0 to 6 points). Outcome of interest was defined as heart failure events including new-onset heart failure and death with heart failure. Heart failure was ascertained based on the Framingham criteria. Univariable and multivariable Cox-proportional hazards model were used to assess the risk of heart failure events. Heart failure events-free survival was estimated with Kaplan-Meier methods, and the predictive accuracy of the H2ARDD score for the prediction of heart failure events was measured by the area under the receiver operating characteristic (ROC) curve. Results Of 562 AF patients, 522 (age 69±10 year–old, 64.9%men) met study criteria. Patients who had a history of heart disease was 185 (35%), diabetes mellitus was 135 (26%), anemia was 54 (10%), renal dysfunction was 221 (43%), and diuretic use was 193 (37%). The mean H2ARDD score was 1.88±1.57. Of all study patients, 84 (16.2%) developed heart failure events during a mean follow–up of 54±42 months. Patients who developed heart failure events in 1 year was 24 (4.6%). In multivariable Cox–proportional hazards model, H2ARDD score was shown as an significant predictor for heart failure events (hazard ratio: 1.56, 95% confidence interval: 1.36 - 1.79, P&lt;0.0001), independently of age (per 10 years, hazard ratio: 1.35, 95% confidence interval: 1.03 – 1.78, P&lt;0.05). In the Kaplan–Meier analyses stratified by H2ARDD score (0–1, 2–3, 4–6), patients who had a higher H2ARDD sore had significantly worse heart failure event-free survival (log-rank P&lt;0.0001) (Figure 1). The area under the ROC curve for the prediction of heart failure events in 1-year was 0.812 (95% confidence interval: 0.737 – 0.887, P&lt;0.0001), and the best cut-off value was ≥4 points (sensitivity: 67%, specificity: 83%) (Figure 2). Conclusion H2ARDD score was demonstrated as a significant independent predictor for the prediction of heart failure events, with high predictive accuracy. H2ARDD score may be useful for heart failure risk stratification of AF patients. FUNDunding Acknowledgement Type of funding sources: None. Figure 1 Figure 2


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