Hierarchical logistic regression models for clustered binary outcomes in studies of IVF-ET

2000 ◽  
Vol 73 (3) ◽  
pp. 575-581 ◽  
Author(s):  
Joseph W. Hogan ◽  
Andrew S. Blazar
2007 ◽  
Vol 10 (2) ◽  
pp. 406-415 ◽  
Author(s):  
Babak Khoshnood ◽  
Béatrice Blondel

AbstractThe aim of the study was to assess, using population-based data, trends and regional variations in multiple births during the period of increasing use and changes in practice patterns for infertility treatments. National data for 24,554,977 births (live births and stillbirths) were used, including 569,423 twins during the period 1972 to 2003, and 14,599 triplets for 1984 to 2003. Statistical analyses included age-adjusted hierarchical logistic regression models for twin births and separate analyses for triple, same-sex, and different-sex twin births. Due to confidentiality considerations, the only variable available for adjustment was maternal age. Regionallevel variations were estimated using median odds ratios based on random-intercept hierarchical logistic regression models. Overall, twin births increased from 18.1 per 1000 births (95% confidence interval [CI] 17.9–18.2) in 1972 to 1975 to 29.9 per 1000 (95% CI 29.7–30.1) in 2000 to 2003. Twin births increased progressively across all regions, whereas triple births reached a peak in the early 1990s and decreased thereafter. Trends for both twin and triple births varied significantly across regions. Both trends and regional variations were greater for different-sex as compared with same-sex twin births. Regional variations in the proportion of multiple births increased in the case of twin births and decreased for triple births. Differences in multiple births at the regional level in France were comparable to country-level differences observed across several western and northern European countries. Regional differences in multiple births need to be monitored and used to inform policies aimed at regulating the use of infertility treatments.


1993 ◽  
Vol 88 (423) ◽  
pp. 1163
Author(s):  
Thomas R. Belin ◽  
Gregg J. Diffendal ◽  
Steve Mack ◽  
Donald B. Rubin ◽  
Joseph L. Schafer ◽  
...  

Open Heart ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. e000781 ◽  
Author(s):  
Masaki Kodaira ◽  
Toshiki Kuno ◽  
Yohei Numasawa ◽  
Takahiro Ohki ◽  
Iwao Nakamura ◽  
...  

ObjectiveWe aimed to determine the relationship between the prevalence of in-hospital complications and annual institutional patient volume in a population of patients undergoing percutaneous coronary intervention (PCI).MethodsClinical data of patients receiving PCI between January 2010 and June 2015 were collected from 14 academic institutions in the Tokyo area and subsequently used for analysis. We employed multivariate hierarchical logistic regression models to determine the effect of institutional volume on several in-hospital outcomes, including in-hospital mortality and procedure-related complications.ResultsA total of 14 437 PCI cases were included and categorised as receiving intervention from either lower-volume (<200 procedures/year, n=6 hospitals) or higher-volume (≥200 procedures/year, n=8 hospitals) institutions. Clinical characteristics differed significantly between the two patient groups. Specifically, patients treated in higher-volume hospitals presented with increased comorbidities and complex coronary lesions. Unadjusted mortality and complication rate in lower-volume and higher-volume hospitals were 1.3% and 1.2% (p=0.0614) and 6.2% and 8.1% (p=0.001), respectively. However, multivariate hierarchical logistic regression models adjusting for differences in the patient characteristics demonstrated that institutional volume was not associated with adverse clinical outcomes.ConclusionsIn conclusion, we observed no significant association between annual institutional volume and in-hospital outcomes within the contemporary PCI multicentre registry.Trial registration numberUMIN R000005598.


1993 ◽  
Vol 88 (423) ◽  
pp. 1149-1159 ◽  
Author(s):  
Thomas R. Belin ◽  
Gregg J. Diffendal ◽  
Steve Mack ◽  
Donald B. Rubin ◽  
Joseph L. Schafer ◽  
...  

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