Simultaneous estimation of discrete outcome and continuous dependent variable equations: A bivariate random effects modeling approach with unrestricted instruments

2017 ◽  
Vol 16 ◽  
pp. 23-34 ◽  
Author(s):  
Md Tawfiq Sarwar ◽  
Grigorios Fountas ◽  
Panagiotis Ch. Anastasopoulos
2008 ◽  
Vol 139 (2_suppl) ◽  
pp. P33-P34
Author(s):  
Jeremy T. Reed ◽  
Shankar K. Sridhara ◽  
Scott E Brietzke

Objective Review and assess the current published literature regarding clinical outcomes of suction electrocautery adenoidectomy (ECA) in pediatric patients. Methods The MEDLINE database was systematically reviewed for articles reporting on the use of ECA. Inclusion criteria included English language, sample size greater than 5, and presentation of extractable data regarding pediatric outcomes with ECA. Random effects modeling was used to estimate summary outcomes. Results 9 studies met the inclusion criteria. There were 2 level 1b studies, 2 level 3b studies, and 5 level 4 studies. The mean sample size was 276 patients with a grand mean age of 6.0 years. Random effects modeling of summary estimates of intra-operative hemorrhage (4.1 cc vs. 24.0 cc 95% CI of difference = 16.5–23.1, p<0.001) and operative time (10.0 minutes vs. 11.9 minutes 95% CI of difference=0.82–2.90, p<0.001) favored ECA vs. traditional curette adenoidectomy. Subjective success was reported in 95.0% (95% CI=92.7–97.3%, p<0.001) of ECA patients with a grand mean of 5.8 months of postoperative follow-up and a grand mean lost to follow-up rate of 23.2%. Adenoid regrowth was evaluated objectively (endoscopy or X-ray) in only 116 of 2,132 (5.4%) total patients with an observed regrowth rate of 2.8% (95% CI=0–5.5%, p=0.052) with 846 total person years of follow-up. Conclusions The preponderance of evidence favors ECA versus curette adenoidectomy in terms of decreased intraoperative hemorrhage and decreased operative time. Long-term outcomes data for ECA are scarce, despite the fact that the procedure is likely performed hundreds of times each day, but suggest a low regrowth rate.


Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1369-1377
Author(s):  
Cornelis J. Potgieter ◽  
Rubin Wei ◽  
Victor Kipnis ◽  
Laurence S. Freedman ◽  
Raymond J. Carroll

2000 ◽  
Vol 30 (1) ◽  
pp. 27-80 ◽  
Author(s):  
Alan Agresti ◽  
James G. Booth ◽  
James P. Hobert ◽  
Brian Caffo

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