Author Archives: Joanna Diong

Calculating sample size using precision for planning

Most sample size calculations for independent or paired samples are performed based on power to detect an effect of a certain size, assuming there’s no effect. Instead, Cumming and Calin-Jageman recommend that readers plan studies to detect precise effects. The 95% confidence interval (CI) indicates precision about effects. Therefore, it is possible to plan studies to detect narrow 95% CIs

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R: How to reshape data from wide to long format, and back again

Many studies take repeated observations on subjects. For example, clinical trials record outcomes from subjects before and after treatments, and laboratory studies might record physiological outcomes from the same subjects over time. In a dataframe, when observations from each subject are written on one row and repeated observations are stored as different column variables, we say the data are in

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The likelihood ratio test: relevance and application

Suppose you conduct a study to compare an outcome between two independent groups of people, but you realised later that the groups were unexpectedly different at baseline. This difference might affect how you interpret the findings. For example, you measured muscle stiffness in people with stroke and in healthy people. At the end of the study, you realised that on

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Calculating sample size for a paired t-test

Suppose you are planning to conduct a repeated-measures study, where outcomes are measured from the same subject at more than one point in time and the average within-subject effect is calculated using a paired t-test or linear regression. How might you calculate how many subjects need to be tested in order to find an effect? Similar to calculating sample size

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Calculating sample size for a 2 independent sample t-test

Scientists often plan for studies by calculating how many subjects or units need to be tested in order to find an effect. That is, they plan for a study using statistical power according to principles of hypothesis testing. Sample size calculations are usually required in ethics applications and grant proposals to justify the study. We previously learned how to calculate

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Indirect evidence of reporting bias in a survey of medical research studies

Reporting bias (ie. bias arising when dissemination of research findings is influenced by the results) is thought to be common in biomedical and medical research. However, exactly how common it is has been difficult to quantify. Albarqouni and colleagues examined how commonly reporting bias occurs by examining the distribution of p values in medical research studies, and compared these distributions

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