scholarly journals Regression dilution bias: Tools for correction methods and sample size calculation

2012 ◽  
Vol 117 (3) ◽  
pp. 279-283 ◽  
Author(s):  
Lars Berglund
Keyword(s):  
Sample Size ◽  
Sample Size Calculation

The most frequently asked question to a statistician: The sample size

2017 ◽  
Vol 23 (5) ◽  
pp. 644-646 ◽  
Author(s):  
Maria Pia Sormani
Keyword(s):  
Clinical Study ◽  
Sample Size ◽  
Study Design ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Ethical Implications ◽  
Number Of Patients ◽  
Reducing Costs ◽  
Correct Size

The calculation of the sample size needed for a clinical study is the challenge most frequently put to statisticians, and it is one of the most relevant issues in the study design. The correct size of the study sample optimizes the number of patients needed to get the result, that is, to detect the minimum treatment effect that is clinically relevant. Minimizing the sample size of a study has the advantage of reducing costs, enhancing feasibility, and also has ethical implications. In this brief report, I will explore the main concepts on which the sample size calculation is based.

Sample size calculation for clinical trials in which entry criteria and outcomes are counts of events

1994 ◽  
Vol 13 (8) ◽  
pp. 859-870 ◽  
Author(s):  
Robert P. McMahon ◽  
Michael Proschan ◽  
Nancy L. Geller ◽  
Peter H. Stone ◽  
George Sopko
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Calculation

scholarly journals Prevalence of Ectopic Eruption, Impaction, Retention and Agenesis of the Permanent Second Molar

2007 ◽  
Vol 77 (5) ◽  
pp. 773-778 ◽  
Author(s):  
Lars Bondemark ◽  
Jola Tsiopa
Keyword(s):  
Early Diagnosis ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Optimal Time ◽  
Second Molar ◽  
Ectopic Eruption ◽  
Delayed Eruption ◽  
Dental Records ◽  
The Times ◽  
The City

Abstract Objective: To elucidate the prevalence of ectopic eruption, impaction, and primary and secondary retention as well as agenesis of the permanent second molar (M2) among adolescents. Materials and Methods: After a sample size calculation, dental records, including radiographs, of 1543 patients (722 girls and 821 boys), from three clinics in the city of Malmoe, Sweden, were retrospectively analyzed. Series of annual records and radiographs were examined for all patients from 10 to 16 years of age and were carried out during 2004–2006. The prevalence of ectopic eruption, impaction, and primary and secondary retention as well as agenesis of M2s was registered in a standardized manner and according to preset definitions. In addition, the times of emergence of the M2s were recorded. Results: The prevalence of ectopic eruption of M2 was 1.5%, the prevalence of primary retention was 0.6%, and the prevalence of impaction was 0.2%. This means that the overall prevalence of eruption disturbances was 2.3%. In addition, the prevalence of agenesis was 0.8%. The prevalence of ectopic eruption was significantly higher in the mandible. Those patients with eruption disturbances and agenesis of M2 showed significantly delayed eruption of their other M2s compared to the individuals without any eruption disturbances. Conclusions: The prevalence of eruption disturbances was higher than reported earlier, and, even if the disturbances do not occur frequently, it is important to develop an early diagnosis in order to start the treatment at the optimal time.

Impact of repeated measures of joint space width on the sample size calculation: An application to hip osteoarthritis

2015 ◽  
Vol 82 (3) ◽  
pp. 172-176 ◽  
Author(s):  
Paul Ornetti ◽  
Laure Gossec ◽  
Davy Laroche ◽  
Christophe Combescure ◽  
Maxime Dougados ◽  
...  
Keyword(s):  
Sample Size ◽  
Repeated Measures ◽  
Sample Size Calculation ◽  
Joint Space Width ◽  
Hip Osteoarthritis ◽  
Joint Space ◽  
Space Width

A unified approach to power and sample size determination for log-rank tests under proportional and nonproportional hazards

2021 ◽  
pp. 096228022098857
Author(s):  
Yongqiang Tang
Keyword(s):  
Sample Size ◽  
Proportional Hazards ◽  
Sample Size Calculation ◽  
Unified Approach ◽  
Rank Tests ◽  
Trial Duration ◽  
Nonproportional Hazards ◽  
Equivalence Trials ◽  
Superiority Trials ◽  
Log Rank Tests

Log-rank tests have been widely used to compare two survival curves in biomedical research. We describe a unified approach to power and sample size calculation for the unweighted and weighted log-rank tests in superiority, noninferiority and equivalence trials. It is suitable for both time-driven and event-driven trials. A numerical algorithm is suggested. It allows flexible specification of the patient accrual distribution, baseline hazards, and proportional or nonproportional hazards patterns, and enables efficient sample size calculation when there are a range of choices for the patient accrual pattern and trial duration. A confidence interval method is proposed for the trial duration of an event-driven trial. We point out potential issues with several popular sample size formulae. Under proportional hazards, the power of a survival trial is commonly believed to be determined by the number of observed events. The belief is roughly valid for noninferiority and equivalence trials with similar survival and censoring distributions between two groups, and for superiority trials with balanced group sizes. In unbalanced superiority trials, the power depends also on other factors such as data maturity. Surprisingly, the log-rank test usually yields slightly higher power than the Wald test from the Cox model under proportional hazards in simulations. We consider various nonproportional hazards patterns induced by delayed effects, cure fractions, and/or treatment switching. Explicit power formulae are derived for the combination test that takes the maximum of two or more weighted log-rank tests to handle uncertain nonproportional hazards patterns. Numerical examples are presented for illustration.

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