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Arthritis Care &Research, EarlyView.We report the national burden of dermatopolymyositis mortality over the past quarter century using the US national vital statistics data. Age‐standardized mortality rates for dermatopolymyositis decreased at an annual rate of 3.8% each year, which was higher than the annual percent decrease for deaths from all other causes.
Elizabeth Matz, Ram R. Singh
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Arthritis Care &Research, EarlyView.Objective Lupus low disease activity state (LLDAS) is a validated treatment target in systemic lupus erythematosus (SLE), but limited studies have explored the role of LLDAS in lupus nephritis (LN). This study aims to investigate the frequency and predictors of LLDAS attainment and its benefit on LN relapse and renal function preservation in patients ...
Chak Kwan Cheung +6 more
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Arthritis Care &Research, EarlyView.Objective In complex diseases, it is challenging to assess a patient's disease state, trajectory, treatment exposures, and risk of multiple outcomes simultaneously, efficiently, and at the point of care. Methods We developed an interactive patient‐level data visualization and analysis tool (VAT) that automates illustration of the trajectory of a ...
Ji Soo Kim +18 more
wiley +1 more source
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Multiple Regression: Regression with Multiple Predictors
2020At the end of this chapter \(\ldots \) \(\ldots \) you will understand how a factorial design leads to higher sensitivity in the analysis than two separate experiments. \(\ldots \) you will be able to calculate a two-way ANOVA by hand if necessary.
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International Journal of Injury Control and Safety Promotion, 2018
Simple linear regression models study the relationship between a single continuous dependent variable Y and one independent variable X (Bangdiwala, 2018).
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Simple linear regression models study the relationship between a single continuous dependent variable Y and one independent variable X (Bangdiwala, 2018).
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Multiple Regression—Regression Diagnostics
2004In Chapter 9 we show how to set up and produce an initial analysis of a regression model with several predictors. In the present chapter we discuss ways to investigate whether the model assumptions are met and, when the assumptions are not met, ways to revise the model to better conform with the assumptions. We also examine ways to assess the effect on
Richard M. Heiberger, Burt Holland
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2007
This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit.
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This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit.
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1983
In this chapter we will apply the matrix algebra of the previous chapter to multiple regression. We assume that the reader has had at least a beginning exposure to the idea of multiple regression. In multiple regression we explore the relationship of a single dependent variable to a set of p independent variables.
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In this chapter we will apply the matrix algebra of the previous chapter to multiple regression. We assume that the reader has had at least a beginning exposure to the idea of multiple regression. In multiple regression we explore the relationship of a single dependent variable to a set of p independent variables.
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