R Tutorial : Basic statistical analysis

Want to learn more? Take the full course at https://learn.datacamp.com/courses/designing-and-analyzing-clinical-trials-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Here we will go through some basic statistical analyses for these endpoints. When a trial is conducted there is a target population, typically patients with a disease. We cannot include all those patients so a sample is taken to test the study hypotheses and make inferences back to the target population. Consider a placebo-controlled trial evaluating the effects of a drug on bone mineral density. Our endpoint is the change from baseline at one year. We would state for our null hypothesis that the mean changes in the drug and placebo groups are equal; no treatment difference. The alternative hypothesis could be that the group means are not equal. In this case, we allow for the possibility that the mean change in the active drug group could be higher or lower than in the placebo group; a two-sided test. In a one-sided test, our alternative hypothesis could be that the increase in the drug group is greater than in the placebo. We then conduct an appropriate statistical test on the sample data to provide evidence against the null hypothesis. We estimate the treatment effect and its confidence interval, which is the range of values for this estimate that has a specified probability of containing the true population treatment effect. A test statistic is compared to distribution in order to determine the p-value, that is the probability of observing our data or something more extreme if the null hypothesis is true. If this p-value is less than a pre-specified significance level then we reject the null hypothesis. Commonly, this is set to 0.05, i.e. we allow a 5% chance of incorrectly rejecting the null hypothesis when it is in fact true. In our example, bone mineral density change is a continuous measure. If we find that the chan