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Journal articles on the topic 'Hazard ratios'

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Published: 10 December 2022
Last updated: 10 March 2023

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1

Sedgwick, P. "Hazards and hazard ratios." BMJ 345, sep07 1 (September 7, 2012): e5980-e5980. http://dx.doi.org/10.1136/bmj.e5980.

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2

Hernán, Miguel A. "The Hazards of Hazard Ratios." Epidemiology 21, no. 1 (January 2010): 13–15. http://dx.doi.org/10.1097/ede.0b013e3181c1ea43.

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3

Sedgwick, P. "Hazard ratios." BMJ 343, sep21 2 (September 21, 2011): d5918. http://dx.doi.org/10.1136/bmj.d5918.

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4

Sedgwick, P., and L. Marston. "Hazard ratios." BMJ 341, aug25 1 (August 25, 2010): c4566. http://dx.doi.org/10.1136/bmj.c4566.

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5

Sedgwick, P. "Derivation of hazard ratios." BMJ 343, no. 02 1 (November 2, 2011): d6994. http://dx.doi.org/10.1136/bmj.d6994.

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6

Bartlett, Jonathan W., Tim P. Morris, Mats J. Stensrud, Rhian M. Daniel, Stijn K. Vansteelandt, and Carl-Fredrik Burman. "The Hazards of Period Specific and Weighted Hazard Ratios." Statistics in Biopharmaceutical Research 12, no. 4 (June 23, 2020): 518–19. http://dx.doi.org/10.1080/19466315.2020.1755722.

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7

Alexander, Brian M., Jonathan D. Schoenfeld, and Lorenzo Trippa. "Hazards of Hazard Ratios — Deviations from Model Assumptions in Immunotherapy." New England Journal of Medicine 378, no. 12 (March 22, 2018): 1158–59. http://dx.doi.org/10.1056/nejmc1716612.

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VanderWeele, Tyler J. "Optimal approximate conversions of odds ratios and hazard ratios to risk ratios." Biometrics 76, no. 3 (January 6, 2020): 746–52. http://dx.doi.org/10.1111/biom.13197.

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9

Padhiar, A., C. Parker, JM Quigley, S. Mealing, and DA Scott. "Time Ratios Or Hazard Ratios: Accelerating Toward A New Approach?" Value in Health 18, no. 7 (November 2015): A686. http://dx.doi.org/10.1016/j.jval.2015.09.2537.

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10

Diao, Guoqing, and Joseph G. Ibrahim. "Quantifying time-varying cause-specific hazard and subdistribution hazard ratios with competing risks data." Clinical Trials 16, no. 4 (June 5, 2019): 363–74. http://dx.doi.org/10.1177/1740774519852708.

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Various non-proportional hazard models have been developed in the literature for competing risks data. The regression coefficients under these models, however, typically cannot be compared directly. We propose new methods to quantify the average of the time-varying cause-specific hazard ratios and subdistribution hazard ratios through two general classes of transformations and weight functions that are chosen to reflect the relative importance of the hazard ratios in different time periods. We further propose an [Formula: see text] -norm type of test statistic that incorporates the test statistics for all possible pairs of the transformation function and weight function under consideration. Extensive simulations are conducted under various settings of the hazards and demonstrate that the proposed test performs well under all settings. An application to a clinical trial in follicular lymphoma is examined in detail.
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Corona, Alberto, Vincenzo De Santis, and Mervyn Singer. "Hazard Ratios and Hazardous Carbapenemase-Producing Enterobacteriaceae." Critical Care Medicine 43, no. 11 (November 2015): e536-e537. http://dx.doi.org/10.1097/ccm.0000000000001233.

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12

Stensrud, Mats J., John M. Aalen, Odd O. Aalen, and Morten Valberg. "Limitations of hazard ratios in clinical trials." European Heart Journal 40, no. 17 (November 29, 2018): 1378–83. http://dx.doi.org/10.1093/eurheartj/ehy770.

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13

Jiang, Honghua, Pandurang M. Kulkarni, Yanping Wang, and Craig H. Mallinckrodt. "Nonparametric covariate adjustment in estimating hazard ratios." Pharmaceutical Statistics 15, no. 1 (November 26, 2015): 46–53. http://dx.doi.org/10.1002/pst.1725.

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14

Prentice, R. "Regression on hazard ratios and cross ratios in multivariate failure time analysis." Biometrika 84, no. 2 (June 1, 1997): 349–63. http://dx.doi.org/10.1093/biomet/84.2.349.

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15

Bolard, P., C. Quantin, J. Esteve, J. Faivre, and M. Abrahamowicz. "Modelling time-dependent hazard ratios in relative survival." Journal of Clinical Epidemiology 54, no. 10 (October 2001): 986–96. http://dx.doi.org/10.1016/s0895-4356(01)00363-8.

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16

Blagoev, Krastan B., Julia Wilkerson, and Tito Fojo. "Hazard ratios in cancer clinical trials—a primer." Nature Reviews Clinical Oncology 9, no. 3 (January 31, 2012): 178–83. http://dx.doi.org/10.1038/nrclinonc.2011.217.

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17

Azuero, Andres. "A note on the magnitude of hazard ratios." Cancer 122, no. 8 (February 16, 2016): 1298–99. http://dx.doi.org/10.1002/cncr.29924.

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18

Sergeant, Gregory, Baki Topal, and Steffen Fieuws. "On Hazard Ratios and Magnitude of Impact on Prognosis." Annals of Surgery 249, no. 3 (March 2009): 545. http://dx.doi.org/10.1097/sla.0b013e31819aae31.

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19

Fidler, Vaclav, Margijske H. G. van Roest, and Koert P. de Jong. "On Hazard Ratios and Magnitude of Impact on Prognosis." Annals of Surgery 249, no. 3 (March 2009): 545–46. http://dx.doi.org/10.1097/sla.0b013e31819abe2f.

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20

Abel, Ulrich R., and Lutz Edler. "A pitfall in the meta-analysis of hazard ratios." Controlled Clinical Trials 9, no. 2 (June 1988): 149–51. http://dx.doi.org/10.1016/0197-2456(88)90035-9.

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21

Eitz, Kerstin A. "Errors in Hazard Ratios, Labels in Figures, and Text." JAMA Oncology 6, no. 12 (December 1, 2020): 1984. http://dx.doi.org/10.1001/jamaoncol.2020.7074.

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22

Lin, Ray S., Ji Lin, Satrajit Roychoudhury, Keaven M. Anderson, Tianle Hu, Bo Huang, Larry F. Leon, et al. "Rejoinder to Letter to the Editor “The Hazards of Period Specific and Weighted Hazard Ratios”." Statistics in Biopharmaceutical Research 12, no. 4 (October 1, 2020): 520–21. http://dx.doi.org/10.1080/19466315.2020.1825522.

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23

Kropko, Jonathan, and Jeffrey J. Harden. "Beyond the Hazard Ratio: Generating Expected Durations from the Cox Proportional Hazards Model." British Journal of Political Science 50, no. 1 (November 27, 2017): 303–20. http://dx.doi.org/10.1017/s000712341700045x.

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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.
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24

Røssell Johansen, Eeva-Liisa, Mette Lise Lousdal, Mette Vinther Skriver, Michael Væth, Ivar Sønbø Kristiansen, and Henrik Støvring. "Predicting Difference in Mean Survival Time from Reported Hazard Ratios for Cancer Patients." Medical Decision Making 39, no. 3 (March 5, 2019): 228–38. http://dx.doi.org/10.1177/0272989x19832879.

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Background. Gain in mean survival time from new cancer treatments is a core component of cost-effectiveness analyses frequently used by payers for reimbursement decisions. Due to limited follow-up time, clinical trials rarely report this measure, whereas they often report hazard ratios comparing treatment groups. Aim. We aimed to explore the empirical relationship between gain in mean survival time and the hazard ratio for cancer patients. Methods. We included all patients in Norway diagnosed from 1965 through 2004 with late-stage cancer at the point of diagnosis and with one of the following cancers: stomach, colon, rectal, pancreas, lung and trachea, kidney excluding renal pelvis, and metastasized breast and prostate. Patients were followed until emigration, death, or June 30, 2016, whichever came first. Observed mean survival times and hazard ratios were obtained in subcohorts defined by patients’ sex, age, cancer type, and time period of diagnosis, which had nearly complete follow-up. Based on theoretical considerations, we fitted a linear relationship between observed differences in mean survival and logarithmic hazard ratios. For validation, we estimated differences in mean survival from hazard ratios of bootstrap samples with artificially induced censoring and compared with fitting a Weibull distribution. Results. The relationship between differences in mean survival time and corresponding logarithmic hazard ratios was linear for each of the included cancers. The predicted differences in mean survival of the empirical approach generally had smaller bias than the Weibull approach. Conclusion. For cancer diagnoses with poor prognosis, differences in mean survival times could be predicted from corresponding hazard ratios. This hazard ratio–based approach outperforms or is similar to fitting Weibull models to data with incomplete follow-up, while making fewer assumptions.
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25

Khosrow-Khavar, Farzin, Nathaniel Bouganim, Kristian B. Filion, Samy Suissa, and Laurent Azoulay. "Cardiotoxicity of Use of Sequential Aromatase Inhibitors in Women With Breast Cancer." American Journal of Epidemiology 189, no. 10 (April 27, 2020): 1086–95. http://dx.doi.org/10.1093/aje/kwaa065.

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Abstract The association between use of aromatase inhibitors (AIs) and cardiovascular outcomes is controversial. While some observational studies have assessed the cardiovascular safety of AIs as upfront treatments, their cardiotoxicity as sequential treatments with tamoxifen remains unknown. Thus, we conducted a population-based cohort study using data from the United Kingdom Clinical Practice Research Datalink linked to the Hospital Episode Statistics and Office for National Statistics databases. We employed a prevalent new-user design to propensity-score match, in a 1:2 ratio, patients switching from tamoxifen to AIs with patients continuing tamoxifen between 1998 and 2016. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals for the study outcomes (myocardial infarction, ischemic stroke, heart failure, and cardiovascular mortality). Overall, 1,962 patients switching to AIs were matched to 3,874 patients continuing tamoxifen. Compared with tamoxifen, AIs were associated with an increased risk of myocardial infarction (hazard ratio (HR) = 2.08, 95% confidence interval (CI): 1.02, 4.27). The hazard ratios were elevated for ischemic stroke (HR = 1.58, 95% CI: 0.85, 2.93) and heart failure (HR = 1.69, 95% CI: 0.79, 3.62) but not cardiovascular mortality (HR = 0.87, 95% CI: 0.49, 1.54), with confidence intervals including the null value. The elevated hazard ratios observed for the cardiovascular outcomes should be corroborated in future large observational studies.
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26

Richardson, David B., Alexander P. Keil, Jessie K. Edwards, Alan C. Kinlaw, and Stephen R. Cole. "Standardizing Discrete-Time Hazard Ratios With a Disease Risk Score." American Journal of Epidemiology 189, no. 10 (April 29, 2020): 1197–203. http://dx.doi.org/10.1093/aje/kwaa061.

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Abstract The disease risk score (DRS) is a summary score that is a function of a potentially large set of covariates. The DRS can be used to control for confounding by the covariates that went into estimation of the DRS and obtain a standardized estimate of an exposure’s effect on disease. However, to date, literature on the DRS has not addressed analyses that focus on estimation of survival or hazard functions, which are common in epidemiologic analyses of cohort data. Here, we propose a method for standardization of hazard ratios using the DRS in longitudinal analyses of the association between a binary exposure and an outcome. This approach to handling a potentially large set of covariates through a model-based approach to standardization may provide a useful tool for cohort analyses of hazard ratios and may be particularly well-suited to settings where an exposure propensity score is difficult to model. Simulations are used in this paper to illustrate the approach, and an empirical example is provided.
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27

Huang, Jinggang, and Craig Friedman. "Modeling multi-period corporate default probability when hazard ratios decay." Journal of Credit Risk 5, no. 1 (March 2009): 3–23. http://dx.doi.org/10.21314/jcr.2009.085.

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28

Vickers, A. D. "SURVIVAL NETWORK META-ANALYSIS: HAZARD RATIOS VERSUS RECONSTRUCTED SURVIVAL DATA." Value in Health 19, no. 3 (May 2016): A90. http://dx.doi.org/10.1016/j.jval.2016.03.1820.

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29

Dong, Manh Cuong, Shaonan Tian, and Cathy W. S. Chen. "Predicting failure risk using financial ratios: Quantile hazard model approach." North American Journal of Economics and Finance 44 (April 2018): 204–20. http://dx.doi.org/10.1016/j.najef.2018.01.005.

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30

Barraclough, Helen, Lorinda Simms, and Ramaswamy Govindan. "Biostatistics Primer: What a Clinician Ought to Know: Hazard Ratios." Journal of Thoracic Oncology 6, no. 6 (June 2011): 978–82. http://dx.doi.org/10.1097/jto.0b013e31821b10ab.

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31

Borate, Uma M., Shin Mineishi, and Luciano J. Costa. "Reply to a note on the magnitude of hazard ratios." Cancer 122, no. 8 (February 16, 2016): 1299–300. http://dx.doi.org/10.1002/cncr.29923.

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32

Schemper, Michael, Samo Wakounig, and Georg Heinze. "The estimation of average hazard ratios by weighted Cox regression." Statistics in Medicine 28, no. 19 (May 26, 2009): 2473–89. http://dx.doi.org/10.1002/sim.3623.

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33

Liu, Rong, Ling Zha, Tomotaka Sobue, Tetsuhisa Kitamura, Junko Ishihara, Ayaka Kotemori, Sayaka Ikeda, Norie Sawada, Motoki Iwasaki, and Shoichiro Tsugane. "Dietary Acrylamide Intake and Risk of Lung Cancer: The Japan Public Health Center Based Prospective Study." Nutrients 12, no. 8 (August 12, 2020): 2417. http://dx.doi.org/10.3390/nu12082417.

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Acrylamide, which forms in heat-treated foods with high carbohydrate content, is a probable human carcinogen. This study aimed to evaluate the association between dietary acrylamide intake and lung cancer using data from the Japan Public Health Center based Prospective Study. Our study included 85,303 participants who completed a food frequency questionnaire. Cox proportional hazards regression models were used to assess hazard ratios and 95% confidence intervals (CIs) after adjusting for confounders. After 14.3 years and 15.4 years of mean follow-up period, 1187 and 485 lung cancer cases were identified in men and women, respectively. The multivariable-adjusted hazard ratios of 10-µg/day increment in acrylamide intake were 1.01 (95% CI, 0.99–1.02) in men and 0.98 (95% CI, 0.95–1.02) in women. Compared with the lowest quartile of acrylamide intake, the hazard ratios for the highest quartile were 1.13 (95% CI, 0.95–1.33; p for trend = 0.12) in men and 1.03 (95% CI, 0.78–1.36; p for trend = 0.86) in women in the multivariable-adjusted model. Moreover, there was also no significant association observed in the stratified analysis for histological subtypes of lung cancer. This study demonstrated that dietary acrylamide intake was not associated with increased lung cancer risk in the Japanese population.
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Roos, Elin, Alessandra Grotta, Fei Yang, Rino Bellocco, Weimin Ye, Hans-Olov Adami, Karin Wirdefeldt, and Ylva Trolle Lagerros. "Body mass index, sitting time, and risk of Parkinson disease." Neurology 90, no. 16 (March 21, 2018): e1413-e1417. http://dx.doi.org/10.1212/wnl.0000000000005328.

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ObjectiveCauses of Parkinson disease are largely unknown, but recent evidence suggests associations with physical activity and anthropometric measures.MethodsWe prospectively analyzed a cohort of 41,638 Swedish men and women by detailed assessment of lifestyle factors at baseline in 1997. Complete follow-up until 2010 was achieved through linkage to population-based registers. We used multivariable Cox proportional hazards models to estimate hazard ratios with 95% confidence intervals (CIs).ResultsWe identified 286 incident cases of Parkinson disease during follow-up. Multivariable adjusted hazard ratios were 1.06 (95% CI 0.76–1.47) for sitting time ≥6 vs <6 hours per day; and 1.13 (95% CI 0.60–2.12) for body mass index ≥30 vs <25 kg/m2. Results did not differ by sex.ConclusionsNo association between prolonged sitting time per day or obesity and risk of Parkinson disease was found.
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Wang, Mo, Ellenor Mittendorfer-Rutz, Thomas E. Dorner, Konstantinos A. Pazarlis, Annina Ropponen, Pia Svedberg, and Magnus Helgesson. "Determinants of work disability following lumbar spine decompression surgery." Scandinavian Journal of Public Health 47, no. 3 (July 5, 2018): 281–92. http://dx.doi.org/10.1177/1403494818785055.

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Aims: Scientific knowledge about risk factors for work disability in terms of long-term sickness absence and disability pension following lumbar spine decompression surgery remains insufficient. This study aimed to investigate the associations between socio-demographic, work-related, and medical characteristics with subsequent long-term sickness absence (>90 days) and disability pension for individuals who underwent lumbar spine decompression surgery. Methods: A prospective cohort study of all individuals aged 19–60 years with diagnosed dorsopathies, who underwent lumbar spine decompression surgery 2008–10 in Sweden ( n=7373) was performed. Univariate and multivariate hazard ratios with 95% confidence intervals regarding long-term sickness absence and disability pension with a 3-year follow-up period were estimated by Cox proportional regression. Results: Low educational level, being a non-European immigrant and preoperative sickness absence were risk factors for both long-term sickness absence and disability pension (hazard ratios: 1.2–3.8). Female sex was a risk factor for long-term sickness absence (hazard ratios: 1.3) whereas age >44 years and being a Nordic immigrant were risk factors for disability pension (hazard ratios: 1.9–2.6). Medical factors as common mental disorders, other mental disorders, prescribed psychiatric medication and somatic comorbidity were risk factors for both long-term sickness absence and disability pension (hazard ratios: 1.2–3.4). A simultaneous lumbar fusion surgery and high preoperative pain severity were risk factors for long-term sickness absence (hazard ratios 1.2–1.8). Conclusions: To prevent long-term work disability after lumbar spine decompression surgery, specific focus is required on older and female patients, those with mental or somatic comorbidities, high levels of preoperative pain or sickness absence, with a simultaneous lumbar fusion surgery, a low educational level or a non-European immigrant background.
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Conforti, Fabio, Laura Pala, Vincenzo Bagnardi, Giuseppe Viale, Tommaso De Pas, Eleonora Pagan, Elisabetta Pennacchioli, et al. "Sex-Based Heterogeneity in Response to Lung Cancer Immunotherapy: A Systematic Review and Meta-Analysis." JNCI: Journal of the National Cancer Institute 111, no. 8 (May 20, 2019): 772–81. http://dx.doi.org/10.1093/jnci/djz094.

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Abstract Background We previously showed that therapy with anti–checkpoints T-lymphocyte-associated protein 4 (anti–CTLA-4) or antiprogrammed cell death protein 1 (anti–PD-1) agents was more effective for men as compared with women. However, because the sex-dimorphism of the immune system is complex, involving multiple elements of immune responses, it is possible that women could derive larger benefit than men from strategies other than therapy with immune checkpoint inhibitors (ICIs) alone. Here we investigated whether women could derive larger benefit than men from the combination of chemotherapy and anti-PD-1 or anti-PD-L1. Methods We performed two meta-analyses. The first included all randomized controlled trials (RCTs) testing anti-PD1 and anti–PD-L1 plus chemotherapy vs chemotherapy to assess different efficacy between men and women. The second included all RCTs of first-line systemic treatment in advanced non-small cell lung cancer testing anti–PD-1/PD-L1 given either alone or combined with chemotherapy to assess the different efficacy of these two immunotherapeutic strategies according to patients’ sex. For each RCT included in the two meta-analyses, first, a trial-specific ratio of hazard ratios (HRs) was calculated from the ratio of the reported hazard ratios in men and in women; second, these trial-specific ratios of hazard ratios were combined across trials using a random-effects model to obtain a pooled hazard ratios ratio. A pooled HRs ratio estimate lower than 1 indicates a greater treatment effect in men, and higher than 1 a greater effect in women. Results Eight RCTs were included in the first meta-analysis. The pooled overall survival hazard ratios (OS-HRs) comparing anti–PD-1/PD-L1 plus chemotherapy vs chemotherapy was 0.76 (95% confidence interval [CI] = 0.66 to 0.87) for men and 0.48 (95% CI = 0.35 to 0.67) for women. The pooled ratio of the overall survival hazard ratios reported in men vs women was 1.56 (95% CI = 1.21 to 2.01), indicating a statistically significant greater effect for women. Six RCTs were included in the second meta-analysis: three tested an anti-PD-1 alone, whereas three RCTs tested anti-PD-1/PD-L1 plus chemotherapy. The pooled overall survival hazard ratios were 0.78 (95% CI = 0.60 to 1.00) in men and 0.97 (95% CI = 0.79 to 1.19) in women for anti–PD-1 alone, compared with 0.76 (95% CI = 0.64 to 0.91) in men and 0.44 (95% CI = 0.25 to 0.76) in women for anti–PD-1/PD-L1 plus chemotherapy. The pooled ratio of overall survival hazard ratios was 0.83 (95% CI = 0.65 to 1.06) for anti–PD-1 alone, indicating a greater effect in men, and 1.70 (95% CI = 1.16 to 2.49) for anti–PD-1/PD-L1 plus chemotherapy, indicating a greater effect in women. Conclusion Women with advanced lung cancer derived a statistically significantly larger benefit from the addition of chemotherapy to anti–PD-1/PD-L1 as compared with men.
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Yen, Ju-Chuan, Hsiu-Li Lin, Chia-An Hsu, Yu-Chuan (Jack) Li, and Min-Huei Hsu. "Atrial Fibrillation and Coronary Artery Disease as Risk Factors of Retinal Artery Occlusion: A Nationwide Population-Based Study." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/374616.

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We use Taiwanese national health insurance research database (NHIRD) to investigate whether thrombolism (carotid artery disease (CAD) as a surrogate) or embolism (atrial fibrillation (AF) as a surrogate) plays roles in later retinal artery occlusion (RAO) development and examine their relative weights. The relative risks of RAO between AF and CAD patients and controls were compared by estimating the crude hazard ratio with logistic regression. Kaplan-Meier analysis was used to calculate the cumulative incidence rates of developing RAO, and a log-rank test was used to analyze the differences between the survival curves. Separate Cox proportional hazard regressions were done to compute the RAO-free rate after adjusting for possible confounding factors such as age and sex. The crude hazard ratios were 7.98 for the AF group and 5.27 for the CAD group, and the adjusted hazard ratios were 8.32 and 5.34 for the AF and CAD groups, respectively. The observation time with RAO-free was shorter for AF compared with CAD group (1490 versus 1819 days). AF and CAD were both risk factors for RAO with different hazard ratios. To tackle both AF and CAD is crucial for curbing RAO.
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38

Schmidt, Sigrun A. J., Henrik Toft Sørensen, Sinéad M. Langan, and Mogens Vestergaard. "Associations of Lifestyle and Anthropometric Factors With the Risk of Herpes Zoster: A Nationwide Population-Based Cohort Study." American Journal of Epidemiology 190, no. 6 (February 11, 2021): 1064–74. http://dx.doi.org/10.1093/aje/kwab027.

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Abstract The role of lifestyle in development of herpes zoster remains unclear. We examined whether smoking status, alcohol consumption, body mass index, or physical activity were associated with zoster risk. We followed a population-based cohort of 101,894 respondents to the 2010 Danish National Health Survey (baseline, May 1, 2010) until zoster diagnosis, death, emigration, or July 1, 2014, whichever occurred first. We computed hazard ratios for zoster associated with each exposure, using Cox regression with age as the time scale and adjusting for potential confounders. Compared with never smokers, hazards for zoster were increased in former smokers (1.17, 95% confidence interval (CI): 1.06, 1.30), but not in current smokers (1.00, 95% CI: 0.89, 1.13). Compared with low-risk alcohol consumption, neither intermediate-risk (0.95, 95% CI: 0.84, 1.07) nor high-risk alcohol consumption (0.99, 95% CI: 0.85, 1.15) was associated with zoster. We also found no increased hazard associated with weekly binge drinking versus not (0.93, 95% CI: 0.77, 1.11). Risk of zoster varied little by body mass index (referent = normal weight) and physical activity levels (referent = light level), with hazard ratios between 0.96 and 1.08. We observed no dose-response association between the exposures and zoster. The examined lifestyle and anthropometric factors thus were not risk factors for zoster.
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39

Knudsen, Signe Schöllhammer, Bodil Hammer Bech, Bent Winding Deleuran, Cecilia Høst Ramlau-Hansen, and Linn Håkonsen Arendt. "Maternal rheumatoid arthritis and systemic lupus erythematosus and risk of cryptorchidism and hypospadias in boys: a Danish nationwide study." Rheumatology 59, no. 8 (November 18, 2019): 1871–77. http://dx.doi.org/10.1093/rheumatology/kez538.

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Abstract Objectives RA and SLE are the most prevalent autoimmune rheumatic diseases affecting young women. Both diseases are characterized by systemic inflammation that may affect placental function and fetal development during pregnancy, and both diseases are associated with adverse pregnancy and child outcomes. We investigated the associations between maternal RA or SLE and the two genital malformations, cryptorchidism and hypospadias. Methods In this nationwide register-based study including all male singleton live births in Denmark from 1995 to 2016, we assessed the occurrence of cryptorchidism and hypospadias according to the prenatal disease-state of the mothers. Using Cox proportional hazards models we calculated adjusted hazard ratios, accounting for varying age at diagnosis. Results Among 690 240 boys, 1026 had a mother with RA and 352 had a mother with SLE. We found adjusted hazard ratios of 1.72 (95% CI: 1.15; 2.57) for cryptorchidism among boys born to mothers with RA and 1.46 (95% CI: 0.69; 3.06) for boys born to mothers with SLE, compared with the general population. As the number of hypospadias cases was low, multivariate analysis was not feasible. The crude hazard ratios were 0.51 (95% CI: 0.16; 1.58) and 1.00 (95% CI: 0.25; 4.03) for RA and SLE, respectively. Conclusion Boys born to mothers with RA had higher risk of cryptorchidism, compared with unexposed boys. Boys born to mothers with SLE showed a similar tendency, however with less precision of the estimate. No conclusion could be reached on the risk of hypospadias, due to the low number of events.
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Nechita, Vlad-Ionuţ, Emil Moiş, Luminiţa Furcea, Mihaela-Ancuţa Nechita, and Florin Graur. "Klatskin Tumor: A Survival Analysis According to Tumor Characteristics and Inflammatory Ratios." Medicina 58, no. 12 (December 5, 2022): 1788. http://dx.doi.org/10.3390/medicina58121788.

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Background and Objectives: The aim was to evaluate the association of inflammatory biomarkers with resectability and overall survival in hilar cholangiocarcinoma. Materials and Methods: We conducted a retrospective cohort study over 72 consecutive surgical cases of Klatskin tumor over an 11-year period. The sample was divided into two groups: 42 surgical resection cases and 30 unresectable tumors. Values of inflammatory ratios were compared according to the resectability. Log-rank test, univariate, and multivariate Cox proportional hazards models were used to evaluate the overall survival. Results: Subjects were between 42–87 years old (average age of 64.91 ± 9.15 years). According to the procedure: 58.33% benefited from resection (with a 30.95% R0 resection rate) and 41.66% had palliative surgery. Elevated NLR (neutrophil to lymphocyte ratio), PLR (platelet to lymphocyte ratio), and SII (systemic immune-inflammation index), and lower LMR (lymphocyte to monocyte ratio) at admission were associated with unresectable tumors (p < 0.01). For the multivariate Cox proportional hazard models, increased absolute values of NLR, PLR, and SII were associated with lower survival; no differences were observed for LMR absolute value. The cut-off value of NLR ≥ 6 was associated with lower survival. The median survival time for all subjects was 442 days, with 774 days for the resection group and 147 days for the group with palliative surgery. Conclusions: In hilar cholangiocarcinoma, inflammatory ratios are associated with tumor resectability. Tumor excision conferred an important advantage in survival. Elevated NLR, PLR, and SII values at admission significantly increased the hazard ratio. LMR had no influence on survival.
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James, W. H. "Offspring sex ratios as an index of pollution hazard in residential environments." Occupational and Environmental Medicine 52, no. 8 (August 1, 1995): 556. http://dx.doi.org/10.1136/oem.52.8.556-a.

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Williams, F. L., O. L. Lloyd, and S. A. Ogston. "Offspring sex ratios as an index of pollution hazard in residential environments." Occupational and Environmental Medicine 52, no. 9 (September 1, 1995): 622. http://dx.doi.org/10.1136/oem.52.9.622.

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Hilsenbeck, Susan G., Peter M. Ravdin, Carl A. de Moor, Gary C. Chamness, C. Kent Osborne, and Gary M. Clark. "Time-dependence of hazard ratios for prognostic factors in primary breast cancer." Breast Cancer Research and Treatment 52, no. 1-3 (November 1998): 227–37. http://dx.doi.org/10.1023/a:1006133418245.

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Nieto-Barajas, Luis E. "Bayesian semiparametric analysis of short- and long-term hazard ratios with covariates." Computational Statistics & Data Analysis 71 (March 2014): 477–90. http://dx.doi.org/10.1016/j.csda.2013.03.012.

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Kuk, Anthony Y. C. "An extended semiparametric model for short-term and long-term hazard ratios." Journal of Statistical Computation and Simulation 89, no. 11 (April 30, 2019): 2138–50. http://dx.doi.org/10.1080/00949655.2019.1610885.

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Falcaro, Milena, Ula Nur, Bernard Rachet, and James R. Carpenter. "Estimating Excess Hazard Ratios and Net Survival When Covariate Data Are Missing." Epidemiology 26, no. 3 (May 2015): 421–28. http://dx.doi.org/10.1097/ede.0000000000000283.

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Austin, Peter C. "The performance of different propensity score methods for estimating marginal hazard ratios." Statistics in Medicine 32, no. 16 (December 12, 2012): 2837–49. http://dx.doi.org/10.1002/sim.5705.

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Zhongzhan, Zhang. "Estimating hazard ratios in nested case-control studies by mantel-haenszel method." Acta Mathematicae Applicatae Sinica 17, no. 4 (October 2001): 457–68. http://dx.doi.org/10.1007/bf02669698.

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Monsarrat, Paul, and Jean-Noel Vergnes. "The progressive substitution of hazard ratios for relative risks in biomedical research." Scientometrics 119, no. 2 (March 16, 2019): 1263–67. http://dx.doi.org/10.1007/s11192-019-03059-2.

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Sutradhar, Rinku, and Peter C. Austin. "Relative rates not relative risks: addressing a widespread misinterpretation of hazard ratios." Annals of Epidemiology 28, no. 1 (January 2018): 54–57. http://dx.doi.org/10.1016/j.annepidem.2017.10.014.

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