Question

In a randomized, double-blind clinical trial, 156 subjects were given an antidepressant medication to help them stop smoking; a second group of 153 subjects were given a placebo. Insomnia was more common in the antidepressant group than in the placebo group; Fisher's exact test of the insomnia data gave a \(P\) -value of \(0.008 .^{24}\) Interpret this \(P\) -value in the context of the clinical trial.

Short answer

The P-value of 0.008 indicates there is a statistically significant difference in insomnia rates between the antidepressant and placebo groups, and it is unlikely that this result is due to chance.

Step-by-step solution

Understanding the P-value

The P-value is a measure of the probability of an observed difference in results occurring by chance, assuming there is no actual difference between the groups (null hypothesis). In this clinical trial context, it refers to the probability of the difference in insomnia incidences between the antidepressant group and the placebo group occurring if, in reality, the drug does not affect insomnia rates.

Interpreting the P-value in the trial's context

Since the P-value is 0.008, which is less than the common significance level of 0.05, we conclude that the observed difference in insomnia incidences is very unlikely to have occurred due to random chance. This suggests that there is a statistically significant difference in insomnia rates between the antidepressant group and the placebo group, and the antidepressant medication is likely related to an increase in insomnia.

Key concepts

Fisher's exact test

The Fisher's exact test is a statistical significance test used to determine if there are nonrandom associations between two categorical variables. In the context of the clinical trial mentioned in the exercise, this test was used to assess whether the occurrence of insomnia was associated with the group assignment (antidepressant or placebo). Unlike other tests that work well for large sample sizes, Fisher's exact test is especially useful for smaller sample sizes because it doesn't rely on approximations of the sampling distribution. It calculates the exact probability (P-value) of obtaining the observed data, or something more extreme, assuming that the null hypothesis is true. This precise calculation makes it ideal for use in clinical trials, where every individual's response is critical to the study's outcomes.

For instance, it would calculate the probability of seeing the observed difference or a more extreme difference in insomnia rates between the antidepressant and placebo group, with the assumption that there was actually no difference in the true insomnia rates.

Statistical significance

Statistical significance is a determination about the non-randomness of the observed effect. In the context of the clinical trial for an antidepressant, statistical significance would mean that the observed differences in insomnia occurrences between the medication group and the placebo group are unlikely to have occurred by chance. The P-value is a key component in assessing this statistical significance; it quantifies the probability that the observed differences could occur under random chance alone. A commonly accepted threshold for 'statistical significance' is a P-value of less than 0.05. If the P-value is below this threshold, the result is considered to be statistically significant—it's an indication that there is less than a 5% probability that the observed effects are due to random fluctuation. In the given exercise, the P-value was 0.008, well under 0.05, suggesting that the observed difference in insomnia was statistically significant and may be attributable to the medication.

Null hypothesis

The null hypothesis is a baseline assumption that there is no effect or no difference between groups in an experiment. It represents a statement of no association that researchers want to test against the alternative hypothesis, which proposes that there is an effect or a difference. In randomized clinical trials like the one described for insomnia studies, the null hypothesis would state that the incidence of insomnia in subjects receiving the antidepressant is the same as those receiving the placebo. When conducting statistical tests, such as Fisher's exact test, the P-value helps determine whether there's enough evidence to reject the null hypothesis. In this case, the very small P-value (0.008) indicates that there is sufficient evidence to reject the null hypothesis, leading researchers to believe that the antidepressant may indeed have an impact on insomnia incidence.

Randomized clinical trial

A randomized clinical trial is a research study that randomly assigns participants to different groups to test the efficacy and safety of a treatment or intervention. This random assignment helps to eliminate selection bias and is meant to ensure that the groups are comparable at the start of the trial. In the context of the exercise, the study is a double-blind trial, which means that neither the participants nor the researchers know which participants are receiving the active medication or the placebo. This design minimizes the influence of preconceived notions or expectations on the outcome, making the results more reliable. The goal of such trials is to provide the highest level of scientific evidence on the effectiveness of new treatments, like assessing whether an antidepressant can aid in smoking cessation without causing adverse effects like insomnia.

Insomnia in clinical studies

In clinical studies, insomnia often serves as an adverse event of interest, particularly when evaluating medications that may affect the central nervous system, like antidepressants. The accurate assessment of insomnia requires careful monitoring, as it is a condition that can significantly impact patients' quality of life. Studies need to determine whether insomnia is caused by the medication, an underlying health condition, or other external factors. In the exercise provided, the incidence of insomnia was compared between groups using an antidepressant medication and a placebo. Clinical researchers aim to understand not only the primary effects of a medication on the condition it is meant to treat but also any secondary effects it may have, such as sleeping difficulties. These insights are critical for healthcare professionals to balance the benefits of the medication against any potential negative side effects.