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MeSH Review:

Proportional Hazards Models

Wadsworth, M. et al., Ellis, R.J. et al., Ikeda, K. et al., Ikeda, K. et al., Erikssen, G. et al., et al.

Barbare, Bouché, Bonnetain, Raoul, Rougier, Abergel, Boige, Denis, Blanchi, Pariente, Milan, Bedenne, Sabin, Mocroft, Bofill, Janossy, Lee, Johnson, Phillips, Yang, Yoshimura, Suzuma, Tamaki, Umemura, Nakamura, Nakamura, Wang, Mori, Sakurai, Kakudo, Ikeda, Arase, Saitoh, Kobayashi, Someya, Hosaka, Akuta, Suzuki, Suzuki, Sezaki, Kumada, Tanaka, Harada, Wadsworth, Butterworth, Marmot, Ecob, Hardy, Osborne, Bardou, Hopp, Chamness, Hilsenbeck, Fuqua, Wong, Allred, Clark, Schiff, Ellis, Deutsch, Heaton, Marcotte, McCutchan, Nelson, Abramson, Thal, Atkinson, Wallace, Grant, Balkau, Shipley, Jarrett, Pyörälä, Pyörälä, Forhan, Eschwège, Fornage, Lee, Doris, Bray, Heiss, Zeldin, Boerwinkle, Foulkes, Brunet, Stefansson, Straume, Chappuis, Bégin, Hamel, Goffin, Wong, Trudel, Kapusta, Porter, Akslen, Nordstrom, Oh, Sacks, L'Italien, Scheer, Chu, Klausner, Katz, Schwarcz, Schwartz, Prysak, Murphy, Lonardo, Pass, Schwartz, Brooks, Khaw, Bingham, Welch, Luben, Wareham, Oakes, Day, Berger, Sachs, Amering, Holzinger, Bankier, Katschnig,

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Disease relevance of Proportional Hazards Models

We used a Cox proportional hazards model to see whether use of antiretroviral therapy was associated with acquiring an STD after AIDS, after adjustment for sex, age, race, HIV-1 risk category, and CD4 count at AIDS diagnosis [1].
Intent-to-treat Cox models were fit to determine the relation between the use of acyclovir (modeled as a time-dependent covariate) and disease progression, controlling for baseline and time-dependent clinical and laboratory prognostic variables [2].
Propensity scores were used to control for baseline heart disease (HD) and other known predictors of chemotherapy, and Cox proportional hazards models were used to estimate the risk of cardiomyopathy (CM), congestive heart failure (CHF), and HD after chemotherapy [3].
Cox proportional hazard model identified that alpha-fetoprotein levels (p = 0.00001), age (p = 0.00067), positive hepatitis C virus antibodies (p = 0.00135), total alcohol intake (p = 0.00455) and indocyanine green retention rate (p = 0.04491) were independently associated with the appearance rates of hepatocellular carcinoma [4].
In a parsimonious multivariable proportional hazards model, protein levels of cyclin E, p27(Kip1), p53, and the presence of glomeruloid microvascular proliferation all independently predicted outcome after breast cancer [5].

Psychiatry related information on Proportional Hazards Models

Single and joint associations of physical activity, body mass index (BMI), and blood glucose levels with risk of type 2 diabetes were examined using Cox proportional hazards models [6].
There was some attenuation of this association on cumulative adjustment for a wide range of biologic and life-style coronary disease risk factors, including HDL cholesterol, though it remained significant in the fully adjusted proportional hazards model (RR = 1.6, 95% CI 1.1-2.3) [7].
The Cox proportional hazards model adjusted for age, sex, race, education, serum cholesterol, systolic blood pressure, body mass index, cigarette smoking, and alcohol consumption [8].
METHODS: The data of 73 out-patients with panic disorder who had completed at least 6 weeks of a randomized trial of 24 weeks of either paroxetine only or paroxetine combined with cognitive group-therapy were analyzed in a Cox proportional hazards model [9].

High impact information on Proportional Hazards Models

In a proportional-hazards model, the time-dependent systemic exposure to methotrexate, but not to teniposide or cytarabine, was significantly related to the risk of early relapse in children with B-lineage leukemia [10].
The survival advantage for prednisolone persisted after stratification according to center and the presence of encephalopathy, and after adjustment for prognostic factors in a proportional-hazards model [11].
The effect of AIB1 on DFS adjusted for other prognostic factors was assessed by multivariable analysis using the Cox proportional hazards model [12].
We examined time to occurrence of first new histopathologically confirmed BCC and SCC during the follow-up period in relation to history of radiation therapy (for reasons other than NMSC) using a proportional hazards model [13].
Multivariate analysis using the Cox proportional hazards model found independent prognostic significance for p53 mutations (hazards ratio [HR] = 1.84; P = .018) and advanced disease stage (HR = 2.20; P = .003) [14].

Chemical compound and disease context of Proportional Hazards Models

Using the Cox proportional hazards model, the factors associated with decreased 4-yr survival were the presence of significant RAS, reduced ejection fraction, elevated serum creatinine, and symptoms of congestive heart failure [15].
METHODS: Data for 491 ICD patients enrolled in the Antiarrhythmics Versus Implantable Defibrillators Study were used to create a hazards model relating baseline factors to time to first recurrent arrhythmia [16].
Sex-specific Cox proportional hazards models showed that for men, duration of IDDM, HDL cholesterol, fibrinogen, hypertension, and smoking were all significantly associated with the onset of CAD [17].
After adjusting for concurrently measured predictors of survival (CD4 lymphocyte counts, Centers for Disease Control and Prevention HIV disease classification, hemoglobin concentration, and serum beta 2-microglobulin) in proportional hazards models, mortality for all NP abnormal subjects remained elevated (RR, 1.8; 95% CI, 1.2-2.8; P < .01) [18].
In the resulting proportional hazards models, the foscarnet area under the concentration-time curve approached statistical significance (P = 0.11) as a predictor of decreased risk of retinitis progression [19].

Biological context of Proportional Hazards Models

In the Cox proportional hazards model, the variant IL-6 genotype was associated with an increased recurrence risk (hazard ratio [HR], 4.60; 95% CI, 1.24 to 17.09) in patients receiving mBCG [20].
A multivariate Cox proportional hazards model was constructed to examine the effect of histology (FLC vs. HCC) on the risk of mortality [21].
The U-shaped relation was confirmed with Cox's proportional hazards model, controlling for age, gender, systolic blood pressure, hemoglobin, serum cholesterol, blood glucose, and cardiovascular complications at entry [22].
A proportional hazard model identified alpha-fetoprotein (>/=20ng/ml, hazard ratio 2.30, 95% confidence interval 1.55-3.42), age (>/=55 years, 2.02, 95% CI 1.32-3.08), gender (male, 1.58, 95% CI 1.05-2.38), and platelet count (<100,000counts/mm(3), 1.54, 95% CI 1.04-2.28) as independently associated with carcinogenesis [23].
The Cox proportional hazards model identified both greater than average steroid dosage and positive recipient CMV serology as significant risk factors for CMV infection (P = .014 and .048, respectively) [24].

Anatomical context of Proportional Hazards Models

A linear correlation between CARL and survival was found above a threshold estrogen receptor concentration of 15 fmol/mg cytosol protein using a correlation of the Cox proportional hazards model (P < 0.02) [25].
Multivariate analysis using the stepwise Cox proportional hazard model showed that lymph node metastasis was related to poor survival rates; in addition, patients with loss of Fhit expression still tended to have poor survival (P = 0.0563) [26].
Multivariate Cox proportional hazards models revealed that both the total T lymphocyte count and CD8 T lymphocyte count provided important prognostic information for survival [27].
However, a Cox proportional-hazards model selected the number of coronary arteries narrowed (RR 1.43, p < 0.0001), estrogen use (RR 0.38, p = 0.001), left main coronary stenosis (RR 1.83, p = 0.001), and diabetes mellitus (RR 1.57, p = 0.003) as the significant independent predictors of survival [28].
After correcting for differences in age, plasma cholesterol, systolic blood pressure, erythrocyte sedimentation rate and smoking habits (Cox proportional hazards model), an increase in Hct by 2 SDS was associated with an increase in CVD mortality by a factor ranging between 2.9 at 10, and 1.9 at 16 years (P < 0.05) [29].

Associations of Proportional Hazards Models with chemical compounds

According to Cox's proportional hazard model, total cancer incidence and total cancer mortality were negatively correlated to serum cholesterol level (P less than .001) [30].
We used the Cox proportional hazard model to determine the effect of ascorbic acid and other risk factors on mortality [31].
In multivariate Cox models that controlled for possible confounders, a nonmonotonic increase in mortality hazard ratios was associated with ascending quintile of homocysteine level: 1.0, 1.4, 1.3, 1.5, and 2.0 (P < 0.001 for trend) [32].
Events occurring during the 30 days following the date of influenza diagnosis or oseltamivir dispensing were examined using Cox proportional hazards models [33].
In a Cox proportional hazards model we found a significant beneficial effect of tamoxifen on survival in patients belonging to Okuda I or II stages [34].

Gene context of Proportional Hazards Models

With the use of Cox proportional hazard model and VEGF levels as a continuous variable, high VEGF levels correlated with shorter survival of patients in chronic CML (P =.008) [35].
In a univariate Cox proportional hazards model, higher levels of IL-1Ralpha, IL-8, VEGFR1, and VEGFR2 were predictive of poor survival [36].
In each racial group, Cox proportional hazard models were constructed to assess the relationship between incident ischemic stroke and EPHX2 polymorphisms [37].
Logistic regression was used to identify factors associated with ER positivity and Cox proportional hazards models were used to measure survival differences by ER status [38].
Univariate Kaplan-Meier and multivariate Cox proportional hazards models were used to determine the prognostic significance of expression of MUC1 and MUC2 in these CRCs [39].

Analytical, diagnostic and therapeutic context of Proportional Hazards Models

METHODS: Cox's proportional hazard models were used on data from the National Birth Cohort Study (the MRC National Survey of Health and Development), which was begun in 1946 and had self-reported physician-diagnosed diabetes with age at onset ranging from 31 to 53 years (n=78 cases, and n=47 cases in the multivariate analysis) as the outcome [40].
As the protocol for the oral glucose tolerance test and methods for measuring glucose differed between studies, mortality was analyzed according to the percentiles of the 2-h and fasting glucose distributions, using the Cox's proportional hazards model [41].
Multivariate analysis using Cox's proportional hazard model showed PCNA index, tumour stage, and mean AgNOR score to be independent predictors of survival, while tumour grade and mitotic index were found to be dependent variables [42].
Regression analysis using Cox's proportional-hazards model showed that the three parameters, Child score, aminopyrine breath test results and H/L ratio were significantly related to survival [43].
Furthermore, phosphorylated tyrosine-containing proteins expression level statistically contributed to disease-free survival in Cox's proportional hazard model [44].

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