Flexible Two-Stage Designs: An Overview

2001 ◽  
Vol 40 (02) ◽  
pp. 117-121 ◽  
Author(s):  
W. Brannath ◽  
M. Posch ◽  
P. Bauer
Keyword(s):  
Confidence Intervals ◽  
Sequential Designs ◽  
Two Stage ◽  
P Values ◽  
Interim Analyses ◽  
Flexible Approach ◽  
Error Functions ◽  
Unbiased Estimates ◽  
Basic Ideas

AbstractIn this overview we introduce the basic ideas behind a new flexible approach in sequential designs. The different concepts based on two-stage combination tests and conditional error functions are brought together. We sketch the construction of p-values, confidence intervals, and median unbiased estimates. Finally, recursive combination tests are introduced which extend the flexibility to the choice of the number of interim analyses.

Inference for econometric modeling in antidumping, countervailing duty and safeguard investigations

2009 ◽  
Vol 8 (4) ◽  
pp. 545-557 ◽  
Author(s):  
JAMES J. FETZER
Keyword(s):  
Confidence Intervals ◽  
Econometric Models ◽  
Econometric Modeling ◽  
P Values ◽  
Flexible Approach ◽  
Fixed Level ◽  
Point Estimates ◽  
Level Of Confidence ◽  
Significance Levels

AbstractThis paper examines how to make inferences from econometric models prepared for antidumping, countervailing duty, and safeguard investigations. Analysis of these models has typically entailed drawing inferences from point estimates that are significantly different from zero at a fixed level of confidence. This paper suggests a more flexible approach of drawing inferences using confidence intervals at various significance levels and reporting p-values for the relevant test of injury. Use of confidence intervals and p-values to identify insights and data patterns would have more impact on USITC trade remedy determinations than definitive conclusions about injury based on whether estimates are statistically significant.

scholarly journals A Frequentist Alternative to Significance Testing, p-Values, and Confidence Intervals

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
David Trafimow
Keyword(s):  
Present Article ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
A Priori ◽  
Significance Testing ◽  
Population Parameters ◽  
Null Hypothesis Significance Testing ◽  
P Values ◽  
Statistical Procedures ◽  
Major Section

There has been much debate about null hypothesis significance testing, p-values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

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.

CONCEPTUAL KNOWLEDGE OF CONFIDENCE INTERVALS IN PSYCHOLOGY UNDERGRADUATE AND GRADUATE STUDENTS

2019 ◽  
Vol 18 (1) ◽  
pp. 46-62
Author(s):  
NOELLE M. CROOKS ◽  
ANNA N. BARTEL ◽  
MARTHA W. ALIBALI
Keyword(s):  
Graduate Students ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Conceptual Knowledge ◽  
Statistics Education ◽  
Psychology Students ◽  
Sample Mean ◽  
P Values ◽  
Research Journal ◽  
Graduate Psychology Students

In recent years, there have been calls for researchers to report and interpret confidence intervals (CIs) rather than relying solely on p-values. Such reforms, however, may be hindered by a general lack of understanding of CIs and how to interpret them. In this study, we assessed conceptual knowledge of CIs in undergraduate and graduate psychology students. CIs were difficult and prone to misconceptions for both groups. Connecting CIs to estimation and sample mean concepts was associated with greater conceptual knowledge of CIs. Connecting CIs to null hypothesis  significance testing, however, was not associated with conceptual knowledge of CIs. It may therefore be beneficial to focus on estimation and sample mean concepts in instruction about CIs. First published May 2019 at Statistics Education Research Journal Archives

NO EVIDENCE, OR NOT ENOUGH EVIDENCE?

PEDIATRICS ◽  
1996 ◽  
Vol 98 (6) ◽  
pp. A22-A22
Author(s):  
Student
Keyword(s):  
Confidence Intervals ◽  
P Value ◽  
P Values ◽  
Considerable Uncertainty ◽  
Absence Of Evidence

When we are told that "there's no evidence that A causes B," we should first ask whether absence of evidence means simply that there is no information at all. If there are data, we should look for quantification of the association rather than just a P value. Where risks are small, P values may well mislead: confidence intervals are likely to be wide, indicating considerable uncertainty.

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