Confidence Intervals for the Risks of Regression Models

2006 ◽  
pp. 755-764
Author(s):  
Imhoi Koo ◽  
Rhee Man Kil
Keyword(s):  
Confidence Intervals ◽  
Regression Models

Treatment Time or Convection Volume in HDF: What Drives the Reduced Mortality Risk?

2015 ◽  
Vol 40 (1) ◽  
pp. 53-58 ◽  
Author(s):  
Camiel L.M. de Roij van Zuijdewijn ◽  
Menso J. Nubé ◽  
Piet M. ter Wee ◽  
Peter J. Blankestijn ◽  
Renée Lévesque ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Mortality Risk ◽  
Survival Benefit ◽  
Regression Models ◽  
Cox Regression ◽  
Treatment Time ◽  
High Volume ◽  
P Values ◽  
Hazard Ratios

Background/Aims: Treatment time is associated with survival in hemodialysis (HD) patients and with convection volume in hemodiafiltration (HDF) patients. High-volume HDF is associated with improved survival. Therefore, we investigated whether this survival benefit is explained by treatment time. Methods: Participants were subdivided into four groups: HD and tertiles of convection volume in HDF. Three Cox regression models were fitted to calculate hazard ratios (HRs) for mortality of HDF subgroups versus HD: (1) crude, (2) adjusted for confounders, (3) model 2 plus mean treatment time. As the only difference between the latter models is treatment time, any change in HRs is due to this variable. Results: 114/700 analyzed individuals were treated with high-volume HDF. HRs of high-volume HDF are 0.61, 0.62 and 0.64 in the three models, respectively (p values <0.05). Confidence intervals of models 2 and 3 overlap. Conclusion: The survival benefit of high-volume HDF over HD is independent of treatment time.

Hollywood Rules

2017 ◽  
pp. 1-9
Author(s):  
Karl Schmedders ◽  
Charlotte Snyder ◽  
Ute Schaedel
Keyword(s):  
Confidence Intervals ◽  
Statistical Methods ◽  
Regression Models ◽  
Functional Form ◽  
Hedge Fund ◽  
Fund Manager ◽  
Wall Street ◽  
Independent Variables ◽  
Point Estimates ◽  
Hedge Fund Manager

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

Confidence Intervals on Regression Models with Censored Data

2013 ◽  
Vol 42 (9) ◽  
pp. 2140-2159 ◽  
Author(s):  
Jesus Orbe ◽  
Vicente Núñez-antón
Keyword(s):  
Confidence Intervals ◽  
Censored Data ◽  
Regression Models

scholarly journals Analysis of Nonlinear Regression Models: A Cautionary Note

Dose-Response ◽  
2005 ◽  
Vol 3 (3) ◽  
pp. dose-response.0 ◽  
Author(s):  
Shyamal D. Peddada ◽  
Joseph K. Haseman
Keyword(s):  
Confidence Intervals ◽  
Regression Models ◽  
Nonlinear Models ◽  
Nonlinear Function ◽  
Standard Errors ◽  
True Parameter ◽  
Nonlinear Regression Models ◽  
Confidence Levels ◽  
Regression Parameters ◽  
Large Sample Theory

Regression models are routinely used in many applied sciences for describing the relationship between a response variable and an independent variable. Statistical inferences on the regression parameters are often performed using the maximum likelihood estimators (MLE). In the case of nonlinear models the standard errors of MLE are often obtained by linearizing the nonlinear function around the true parameter and by appealing to large sample theory. In this article we demonstrate, through computer simulations, that the resulting asymptotic Wald confidence intervals cannot be trusted to achieve the desired confidence levels. Sometimes they could underestimate the true nominal level and are thus liberal. Hence one needs to be cautious in using the usual linearized standard errors of MLE and the associated confidence intervals.

scholarly journals Visualization Strategies for Regression Estimates with Randomization Inference

2019 ◽  
Author(s):  
Marshall A. Taylor
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Regression Models ◽  
Statistical Significance ◽  
Permutation Tests ◽  
P Value ◽  
P Values ◽  
Alpha Level ◽  
Significance Levels ◽  
Nonprobability Sample

Coefficient plots are a popular tool for visualizing regression estimates. The appeal of these plots is that they visualize confidence intervals around the estimates and generally center the plot around zero, meaning that any estimate that crosses zero is statistically non-significant at at least the alpha-level around which the confidence intervals are constructed. For models with statistical significance levels determined via randomization models of inference and for which there is no standard error or confidence intervals for the estimate itself, these plots appear less useful. In this paper, I illustrate a variant of the coefficient plot for regression models with p-values constructed using permutation tests. These visualizations plot each estimate's p-value and its associated confidence interval in relation to a specified alpha-level. These plots can help the analyst interpret and report both the statistical and substantive significance of their models. Illustrations are provided using a nonprobability sample of activists and participants at a 1962 anti-Communism school.

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