Power and sample size calculations for generalized regression models with covariate measurement error

2003 ◽  
Vol 22 (7) ◽  
pp. 1069-1082 ◽  
Author(s):  
Tor D. Tosteson ◽  
Jeffrey S. Buzas ◽  
Eugene Demidenko ◽  
Margaret Karagas
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Regression Models ◽  
Covariate Measurement Error ◽  
Sample Size Calculations ◽  
Generalized Regression

scholarly journals Modifying Spearman’s Attenuation Equation to Yield Partial Corrections for Measurement Error—With Application to Sample Size Calculations

2017 ◽  
Vol 78 (1) ◽  
pp. 70-79 ◽  
Author(s):  
W. Alan Nicewander
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Measurement Errors ◽  
Statistical Tests ◽  
Classical Test Theory ◽  
Test Theory ◽  
Sample Size Calculations ◽  
Correction For Attenuation ◽  
Partial Correction

Spearman’s correction for attenuation (measurement error) corrects a correlation coefficient for measurement errors in either-or-both of two variables, and follows from the assumptions of classical test theory. Spearman’s equation removes all measurement error from a correlation coefficient which translates into “increasing the reliability of either-or-both of two variables to 1.0.” In this inquiry, Spearman’s correction is modified to allow partial removal of measurement error from either-or-both of two variables being correlated. The practical utility of this partial correction is demonstrated in its use to explore increasing the power of statistical tests by increasing sample size versus increasing the reliability of the dependent variable for an experiment. Other applied uses are mentioned.

scholarly journals Virtual reality-based relaxation for enhancement of perioperative well-being and quality of life: protocol for a randomised pilot trial

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e044193
Author(s):  
Matthias Christian Schrempf ◽  
Julian Quirin Petzold ◽  
Hugo Vachon ◽  
Morten Aagaard Petersen ◽  
Johanna Gutschon ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Virtual Reality ◽  
Sample Size ◽  
Stress Reduction ◽  
Pilot Trial ◽  
Well Being ◽  
Coping Resources ◽  
Patients With Cancer ◽  
Sample Size Calculations

IntroductionPatients with cancer undergoing surgery often suffer from reduced quality of life and various forms of distress. Untreated distress can negatively affect coping resources as well as surgical and oncological outcomes. A virtual reality-based stress reduction intervention may increase quality of life and well-being and reduce distress in the perioperative phase for patients with cancer. This pilot trial aims to explore the feasibility of the proposed intervention, assess patient acceptability and obtain estimates of effect to provide data for sample size calculations.Methods and analysisPatients with colorectal cancer and liver metastasis undergoing elective surgery will be recruited for this single-centre, randomised pilot trial with a three-arm design. A total of 54 participants will be randomised at 1:1:1 ratio to one of two intervention groups or a control receiving standard treatment. Those randomised to an intervention group will either receive perioperative virtual reality-based stress reduction exercises twice daily or listen to classical music twice daily. Primary feasibility outcomes are number and proportions of participants recruited, screened, consented and randomised. Furthermore, adherence to the intervention, compliance with the completion of the quality of life questionnaires and feasibility of implementing the trial procedures will be assessed. Secondary clinical outcomes are measurements of the effectiveness of the interventions to inform sample size calculations.Ethics and disseminationThe study protocol, the patient information and the informed consent form have been approved by the ethics committee of the Ludwigs-Maximilians-University, Munich, Germany (Reference Number: 19–915). Study findings will be submitted for publication in peer-reviewed journals.Trial registration numberDRKS00020909.

Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation

2021 ◽  
Vol 40 (9) ◽  
pp. 2101-2112
Author(s):  
Mitchell M. Conover ◽  
Kenneth J. Rothman ◽  
Til Stürmer ◽  
Alan R. Ellis ◽  
Charles Poole ◽  
...  
Keyword(s):  
Measurement Error ◽  
Propensity Score ◽  
Inverse Probability ◽  
Covariate Measurement Error
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