scholarly journals Screening at stationary versus mobile units in BreastScreen Norway

2019 ◽  
Vol 27 (1) ◽  
pp. 31-39
Author(s):  
Åsne Holen ◽  
Sofie Sebuødegård ◽  
Gunvor G Waade ◽  
Hildegunn Aase ◽  
Nina-Merete Hopland ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Rural Areas ◽  
Estimating Equation ◽  
Sociodemographic Factors ◽  
Mammographic Screening ◽  
Generalized Estimating Equation ◽  
Mobile Unit ◽  
Interval Breast Cancer ◽  
Screening Unit ◽  
Generalized Estimating

Objective To compare breast characteristics, compression parameters, and early performance measures (rates of recall, screen-detected and interval breast cancer, and histopathologic tumour characteristics) for mammographic screening at a stationary versus mobile screening unit. Methods Results from 92,408 mammographic screening examinations performed as part of BreastScreen Norway during 2008–2017 at either a stationary ( n = 52,620) or mobile ( n = 39,788) unit in Hordaland county were compared using descriptive statistics and generalized estimating equations. A generalized estimating equation for a binary outcome was used to estimate crude and adjusted odds ratios with 95% confidence intervals for the outcomes of interest. Adjusted generalized estimating equation models included age, breast volume, and density grade as covariates. Results Screening at the stationary unit was performed on smaller breasts with higher mammographic density, using lower compression force but higher pressure than at the mobile unit. Using the stationary screening unit as reference, for women screened at the mobile unit, the adjusted odds ratio was: for recall 0.94 (95% CI: 0.87--1.01), screen-detected breast cancer 0.92 (95% CI: 0.78--1.10), and interval breast cancer 1.17 (95% CI: 0.83–1.64). Conclusions The quality of care did not differ for women screened at the stationary versus the mobile unit, but there were differences between the women who attended the two units. Sociodemographic factors should be included in future analyses to fully understand the risk of breast cancer among women residing in urban versus rural areas.

Models for Longitudinal Data: A Generalized Estimating Equation Approach

Biometrics ◽  
1988 ◽  
Vol 44 (4) ◽  
pp. 1049 ◽  
Author(s):  
Scott L. Zeger ◽  
Kung-Yee Liang ◽  
Paul S. Albert
Keyword(s):  
Longitudinal Data ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Equation Approach ◽  
Generalized Estimating

scholarly journals Improved Generalized Estimating Equation Analysis via xtqls for Quasi–Least Squares in Stata

2007 ◽  
Vol 7 (2) ◽  
pp. 147-166 ◽  
Author(s):  
Justine Shults ◽  
Sarah J. Ratcliffe ◽  
Mary Leonard
Keyword(s):  
Least Squares ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Iterative Approach ◽  
Current Estimate ◽  
Cross Sectional ◽  
Equation Analysis ◽  
Correlation Structures ◽  
Correlation Parameters ◽  
Generalized Estimating

Quasi–least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (gee) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of qls that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of qls: (1) qls allows some correlation structures that have not yet been implemented in the framework of gee, (2) qls can be applied as an alternative to gee if the gee estimate is infeasible, and (3) qls uses the same estimating equation for estimation of β as gee; as a result, qls can involve programs already available for gee. In particular, xtqls calls the Stata program xtgee within an iterative approach that alternates between updating estimates of the correlation parameter α and then using xtgee to solve the gee for β at the current estimate of α. The benefit of this approach is that after xtqls, all the usual postregression estimation commands are readily available to the user.

scholarly journals Simultaneous inference for multiple marginal generalized estimating equation models

2019 ◽  
Vol 29 (6) ◽  
pp. 1746-1762 ◽  
Author(s):  
Robin Ristl ◽  
Ludwig Hothorn ◽  
Christian Ritz ◽  
Martin Posch
Keyword(s):  
Estimating Equations ◽  
Estimating Equation ◽  
Small Sample ◽  
Regression Coefficients ◽  
Generalized Estimating Equation ◽  
Type I ◽  
Simultaneous Inference ◽  
Multiple Endpoints ◽  
Repeated Observations ◽  
Generalized Estimating

Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.

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