A general approach for sample size and statistical power calculations assessing the effects of interventions using a mixture model in the presence of detection limits

2006 ◽  
Vol 27 (5) ◽  
pp. 483-491 ◽  
Author(s):  
Lei Nie ◽  
Haitao Chu ◽  
Stephen R. Cole
Keyword(s):  
Sample Size ◽  
Mixture Model ◽  
Statistical Power ◽  
Detection Limits ◽  
Power Calculations

scholarly journals Considerations of sample size and power calculations given a range of analytical scenarios

2021 ◽  
Author(s):  
Alice Carter ◽  
Kate Tilling ◽  
Marcus Robert Munafo
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Poor Quality ◽  
Research Quality ◽  
Design Stage ◽  
Design Consideration ◽  
Power Calculations ◽  
Critical Design ◽  
Study Planning ◽  
Research Questions

The sample size of a study is a key design and planning consideration. However, sample size and power calculations are often either poorly reported or not reported at all, which suggests they may not form a routine part of study planning. Inadequate understanding of sample size and statistical power can result in poor quality studies. Journals increasingly require a justification of sample size, for example through the use of reporting checklists. However, for meaningful improvements in research quality to be made, researchers need to consider sample size and power at the design stage of a study, rather than at the publication stage. Here we briefly illustrate sample size and statistical power in the context of different research questions and how they should be viewed as a critical design consideration.

scholarly journals A systematic review of sample size and power in leading neuroscience journals

2017 ◽  
Author(s):  
Alice Carter ◽  
Kate Tilling ◽  
Marcus R Munafò
Keyword(s):  
Systematic Review ◽  
Sample Size ◽  
Statistical Power ◽  
Reproducible Research ◽  
Sample Sizes ◽  
Significance Level ◽  
Power Calculations ◽  
Research Findings ◽  
Formal Power ◽  
Nature Neuroscience

AbstractAdequate sample size is key to reproducible research findings: low statistical power can increase the probability that a statistically significant result is a false positive. Journals are increasingly adopting methods to tackle issues of reproducibility, such as by introducing reporting checklists. We conducted a systematic review comparing articles submitted to Nature Neuroscience in the 3 months prior to checklists (n=36) that were subsequently published with articles submitted to Nature Neuroscience in the 3 months immediately after checklists (n=45), along with a comparison journal Neuroscience in this same 3-month period (n=123). We found that although the proportion of studies commenting on sample sizes increased after checklists (22% vs 53%), the proportion reporting formal power calculations decreased (14% vs 9%). Using sample size calculations for 80% power and a significance level of 5%, we found little evidence that sample sizes were adequate to achieve this level of statistical power, even for large effect sizes. Our analysis suggests that reporting checklists may not improve the use and reporting of formal power calculations.

Statistical Power in Experimental Audit Studies

2015 ◽  
Vol 45 (2) ◽  
pp. 260-303 ◽  
Author(s):  
Mike Vuolo ◽  
Christopher Uggen ◽  
Sarah Lageson
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
A Priori ◽  
Present Sample ◽  
Power Calculations ◽  
Dichotomous Outcome ◽  
The Social ◽  
Key Factor ◽  
Dichotomous Outcomes ◽  
And Control

Given their capacity to identify causal relationships, experimental audit studies have grown increasingly popular in the social sciences. Typically, investigators send fictitious auditors who differ by a key factor (e.g., race) to particular experimental units (e.g., employers) and then compare treatment and control groups on a dichotomous outcome (e.g., hiring). In such scenarios, an important design consideration is the power to detect a certain magnitude difference between the groups. But power calculations are not straightforward in standard matched tests for dichotomous outcomes. Given the paired nature of the data, the number of pairs in the concordant cells (when neither or both auditor receives a positive response) contributes to the power, which is lower as the sum of the discordant proportions approaches one. Because these quantities are difficult to determine a priori, researchers must exercise particular care in experimental design. We here present sample size and power calculations for McNemar’s test using empirical data from an audit study on misdemeanor arrest records and employability. We then provide formulas and examples for cases involving more than two treatments (Cochran’s Q test) and nominal outcomes (Stuart–Maxwell test). We conclude with concrete recommendations concerning power and sample size for researchers designing and presenting matched audit studies.

P1-188: Enrichment strategies for secondary prevention trials: The effect of restrictive inclusion criteria on statistical power and sample size

2008 ◽  
Vol 4 ◽  
pp. T263-T264
Author(s):  
Steven D. Edland ◽  
Linda K. McEvoy ◽  
Dominic Holland ◽  
John C. Roddey ◽  
Christine Fennema-Notestine ◽  
...  
Keyword(s):  
Secondary Prevention ◽  
Sample Size ◽  
Statistical Power ◽  
Inclusion Criteria ◽  
Prevention Trials

Statistical Power Analysis can Improve Fisheries Research and Management

1990 ◽  
Vol 47 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Randall M. Peterman
Keyword(s):  
Sample Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Small Sample Size ◽  
Industrial Effluent ◽  
Small Sample ◽  
Detectable Effect ◽  
Type Ii ◽  
Sampling Variability ◽  
Statistical Power Analysis

Ninety-eight percent of recently surveyed papers in fisheries and aquatic sciences that did not reject some null hypothesis (H0) failed to report β, the probability of making a type II error (not rejecting H0 when it should have been), or statistical power (1 – β). However, 52% of those papers drew conclusions as if H0 were true. A false H0 could have been missed because of a low-power experiment, caused by small sample size or large sampling variability. Costs of type II errors can be large (for example, for cases that fail to detect harmful effects of some industrial effluent or a significant effect of fishing on stock depletion). Past statistical power analyses show that abundance estimation techniques usually have high β and that only large effects are detectable. I review relationships among β, power, detectable effect size, sample size, and sampling variability. I show how statistical power analysis can help interpret past results and improve designs of future experiments, impact assessments, and management regulations. I make recommendations for researchers and decision makers, including routine application of power analysis, more cautious management, and reversal of the burden of proof to put it on industry, not management agencies.

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