scholarly journals Multivariate random effect models with complete and incomplete data

2012 ◽  
Vol 109 ◽  
pp. 146-155 ◽  
Author(s):  
James O. Chipperfield ◽  
David G. Steel
Keyword(s):  
Incomplete Data ◽  
Random Effect ◽  
Random Effect Models

scholarly journals LO84: The incidence of fall-related intracranial bleeding in older adults taking anticoagulants, antiplatelets and neither medication: a meta-analysis

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S38-S38
Author(s):  
K. de Wit ◽  
D. Nishijima ◽  
S. Mason ◽  
R. Jeanmonod ◽  
S. Parpia ◽  
...  
Keyword(s):  
Older Adults ◽  
Meta Analysis ◽  
Grey Literature ◽  
Random Effect ◽  
Intracranial Bleeding ◽  
Inclusion Criteria ◽  
Antiplatelet Medication ◽  
Cochrane Database ◽  
Random Effect Models ◽  
Anticoagulant Medication

Introduction: It is unclear whether anticoagulant or antiplatelet medications increase the risk for intracranial bleeding in older adults after a fall. Our aim was to report the incidence of intracranial bleeding among older adults presenting to the emergency department (ED) with a fall, among patients taking anticoagulants, antiplatelet medications, both medications and neither medication. Methods: This was a systematic review and meta-analysis, PROSPERO reference CRD42019122626. Medline, EMBASE (via OVID 1946 - July 2019), Cochrane, Database of Abstracts of Reviews of Effects databases and the grey literature were searched for studies reporting on older adults who were evaluated after a fall. We included prospective studies conducted in the ED where more than 80% of the cohort were 65 years or older and had fallen. We contacted study authors for aggregate data on intracranial bleeding in patients prescribed anticoagulant medication, antiplatelet medication and neither medication. Incidences of intracranial bleeding were pooled using random effect models, and I2 index was used to assess heterogeneity. Results: From 7,240 publication titles, 10 studies met inclusion criteria. The authors of 8 of these 10 studies provided data (on 9,489 patients). All studies scored low or moderate risk of bias. The pooled incidence of intracranial bleeding among patients taking an anticoagulant medication was 5.1% (n = 5,016, 95% Confidence Interval (CI): 4.1 to 6.3%) I2 = 42%, a single antiplatelet 6.4% (n = 2,148, 95% CI: 5.4 to 7.6%) I2 = 75%, both anticoagulant and antiplatelet medications 5.9% (n = 212, 95% CI: 1.3 to 13.5%) I2 = 72%, and neither of these medications 4.8% (n = 1,927, 95% CI: 3.5 to 6.2%) I2 = 50%. A sensitivity analysis restricted to patients who had a head CT in the ED reported incidences of 6.1% (n = 3,561, 95% CI: 3 to 8.3%), 8.4% (n = 1,781, 95% CI: 5.5 to 11.8%), 6.7% (n = 206, 95% CI 1.5 to 15.2%) and 6.6% (n = 1,310, 95% CI: 5.0 to 8.4%) respectively. Conclusion: The incidence of fall-related intracranial bleeding in older ED patients was similar among patients who take anticoagulant medication, antiplatelet medication, both and neither medication, although there was heterogeneity between study findings.

Correlation between Methylenetetrahydrofolate Reductase Polymorphisms and Hepatocellular Carcinoma: A Meta-Analysis

2019 ◽  
Vol 74 (3) ◽  
pp. 251-256 ◽  
Author(s):  
Hailong Su ◽  
Guo Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Polymorphisms ◽  
Methylenetetrahydrofolate Reductase ◽  
Meta Analysis ◽  
Random Effect ◽  
Recessive Model ◽  
Mthfr Gene ◽  
Systematic Research ◽  
The Relationship ◽  
Random Effect Models

Background: The correlation between methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms and hepatocellular carcinoma (HCC) remains controversial. Objectives: We performed this study to better assess the relationship between MTHFR gene polymorphisms and the likelihood of HCC. Methods: A systematic research of PubMed, Medline, and Embase was performed to retrieve relevant articles. ORs and 95% CIs were calculated. Results: A total of 15 studies with 8,378 participants were analyzed. In overall analyses, a significant association with the likelihood of HCC was detected for the rs1801131 polymorphism with fixed-effect models (FEMs) in recessive comparison (p = 0.002, OR 0.62, 95% CI 0.43–0.82). However, no positive results were detected for the rs1801133 polymorphism in any comparison. Further subgroup analyses revealed that the rs1801131 polymorphism was significantly associated with the likelihood of HCC in Asians with both FEMs (recessive model: p < 0.0001, OR 0.42, 95% CI 0.29–0.62; allele model: p = 0.004, OR 1.20, 95% CI 1.06–1.35) and random-effect models (recessive model: p = 0.002, OR 0.47, 95% CI 0.29–0.75). Nevertheless, we failed to detect any significant correlation between the rs1801133 polymorphism and HCC. Conclusions: Our findings indicated that the rs1801131 polymorphism may serve as a genetic biomarker of HCC in Asians.

Abstract 005: A Comparison of Methods for Identifying Hospital Performance Outliers in Cardiac Surgery

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Xiaoting Wu ◽  
Min Zhang ◽  
Richard L Prager ◽  
Donald S Likosky
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Random Effect ◽  
Hospital Performance ◽  
Fixed Effect ◽  
Logistic Regression Models ◽  
Indirect Standardization ◽  
Statistical Approaches ◽  
Random Effect Models ◽  
Direct Standardization

Introduction: A number of statistical approaches have been advocated and implemented to estimate adjusted hospital outcomes for public reporting or reimbursement. Nonetheless, the ability of these methods to identify hospital performance outliers in support of quality improvement has not yet been fully investigated. Methods: We leveraged data from patients undergoing coronary artery bypass grafting surgery between 2012-2015 at 33 hospitals participating in a statewide quality collaborative. We applied 5 different statistical approaches (1: indirect standardization with standard logistic regression models, 2: indirect standardization with fixed effect models, 3: indirect standardization with random effect models, 4: direct standardization with fixed effect models, 5: direct standardization with random effect models) to estimate hospital post-operative pneumonia rates adjusting for patients’ risk. Unlike the standard logistic regression models, both fixed effect and random effect models accounted for hospital effect. We applied each method to each year, and subsequently compared methods in their ability to identify hospital performance outliers. Results: Pneumonia rates ranged from 0 % to 24 %. The standard logistic regression models for 2013-2015 had c-statistics of 0.73-0.75, fixed effect models had c-statistics of 0.81-0.83, and random effect models had c-statistics of 0.80-0.83. Each method differed in its ability to identify performance outliers (Figure 1). In direct standardization, random effect models stabilized the hospital rates by moving the estimated rates toward the average rate, fixed effect models produced larger standard errors of hospital effect (particularly for hospitals with low case volumes). In indirect standardization, the three models showed high agreement on their derived observed: expected ratio (intraclass correlation =0.95). Indirect standardization with fixed effect or random effect models, identified similar hospital performance outliers in each year. Conclusion: The five statistical approaches varied in their ability to identify performance outliers. Given its higher sensitivity to outlier hospitals, indirect standardization methods with fixed or random effect models, may be best suited to support quality improvement activities.

scholarly journals Life Satisfaction Development in the Transition to Adulthood: Differences by Gender and Immigrant Background

2022 ◽  
Author(s):  
Juul H. D. Henkens ◽  
Matthijs Kalmijn ◽  
Helga A. G. de Valk
Keyword(s):  
Life Satisfaction ◽  
Transition To Adulthood ◽  
Random Effect ◽  
Children Of Immigrants ◽  
Longitudinal Survey ◽  
Healthy Development ◽  
Immigrant Background ◽  
Multi Level ◽  
Random Effect Models ◽  
Lower Life Satisfaction

AbstractLife satisfaction is crucial for healthy development into adulthood. However, it is yet largely unknown how life satisfaction develops in the transition to adulthood. This study examined life satisfaction development in this transition and paid special attention to differences between boys, girls, children of immigrants, and nonimmigrants. Unique longitudinal data of seven waves (2010–2018) of the Children of Immigrants Longitudinal Survey Germany were used. Respondents (N = 3757, 54% girls, 78% nonimmigrants, Mage weighted = 14.6, SD = 0.6 at wave 1) were followed between ages 14 and 23 and multi-level random effect models were applied. Life satisfaction developed in a nonlinear way in the transition to adulthood (M-shape), with overall decreases between age 17 and 18 and between age 20 and 23. Girls reported lower life satisfaction levels in adolescence and more unstable trajectories than boys, where girls with immigrant backgrounds represented the least advantageous life satisfaction trajectory. Differences in life satisfaction between groups decreased from age 19 onwards.

scholarly journals Causal variance decompositions for institutional comparisons in healthcare

2019 ◽  
Vol 29 (7) ◽  
pp. 1972-1986
Author(s):  
Bo Chen ◽  
Keith A Lawson ◽  
Antonio Finelli ◽  
Olli Saarela
Keyword(s):  
Random Effect ◽  
Real Data ◽  
Hospital Performance ◽  
Case Mix ◽  
Link Functions ◽  
Variation In Care ◽  
Residual Variation ◽  
Data Application ◽  
Variance Decompositions ◽  
Random Effect Models

There is increasing interest in comparing institutions delivering healthcare in terms of disease-specific quality indicators (QIs) that capture processes or outcomes showing variations in the care provided. Such comparisons can be framed in terms of causal models, where adjusting for patient case-mix is analogous to controlling for confounding, and exposure is being treated in a given hospital, for instance. Our goal here is to help identify good QIs rather than comparing hospitals in terms of an already chosen QI, and so we focus on the presence and magnitude of overall variation in care between the hospitals rather than the pairwise differences between any two hospitals. We consider how the observed variation in care received at patient level can be decomposed into that causally explained by the hospital performance adjusting for the case-mix, the case-mix itself, and residual variation. For this purpose, we derive a three-way variance decomposition, with particular attention to its causal interpretation in terms of potential outcome variables. We propose model-based estimators for the decomposition, accommodating different link functions and either fixed or random effect models. We evaluate their performance in a simulation study and demonstrate their use in a real data application.

scholarly journals Aspirin Exposure and Mortality Risk among Prostate Cancer Patients: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Lai lai Fan ◽  
Cheng Peng Xie ◽  
Yi Ming Wu ◽  
Xi jie Gu ◽  
Ying he Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Frequency Response ◽  
Mortality Risk ◽  
Meta Analysis ◽  
Random Effect ◽  
Response Analysis ◽  
Dose Frequency ◽  
All Cause Mortality ◽  
Cancer Specific Mortality ◽  
Random Effect Models

Background. Prostate cancer (PCa) is the ninth most common cause of cancer death globally. Many studies have investigated aspirin exposure and mortality risk among PCa patients, returning inconsistent results. We conducted a comprehensive meta-analysis to explore the association between aspirin exposure and mortality risk among PCa patients and to investigate potential dose/duration/frequency-response relationships. Methods and Results. Studies published from 1980 to 2018 of PubMed and EMBASE databases were searched. We included 14 studies with 110,000 participants. Multivariate-adjusted odds ratios (ORs) were pooled using random-effect models. Potential dose/duration/frequency-response relationships were evaluated for aspirin exposure and prostate cancer-specific mortality (PCSM) risk. We did not detect an association between the highest aspirin exposure and mortality risk (PCSM of prediagnostic aspirin exposure, OR: 0.96, 95% confidence interval [CI]: 0.87-1. 07, I2 = 0%; PCSM of postdiagnostic aspirin exposure, OR:0.92, 95% CI: 0.77-1.10, I2 = 56.9%; all-cause mortality [ACM] of prediagnostic aspirin exposure, OR: 0.96, 95% CI: 0.88-1.04, I2 = 9.4%; ACM of postdiagnostic aspirin exposure, OR: 0.95, 95% CI: 0.73-1.23, I2 = 88.9%). There was no significant dose/frequency-response association observed for aspirin exposure and PCSM risk. On duration-response analysis, we found that short-term postdiagnostic aspirin exposure (shorter than 2.5 years) increased the risk of PCSM. Conclusions. Our meta-analysis suggests that there is no association between aspirin exposure and PCSM risk. Nor is there an association between the highest aspirin exposure and ACM risk among PCa patients. More studies are needed for a further dose/duration/frequency-response meta-analysis.

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