Clinical Trials Data Flashcards
Clinical Trial Basics
Phases of Clinical Trials:
- Phase 1: Tests a new drug or treatment in a small group for the first time to evaluate its safety, determine a safe dosage range, and identify side effects. Typically healthy volunteers (20-100)
- Phase 2: The drug or treatment is given to a larger group to see if it is effective and to further evaluate its safety. 100-300 who have the condition the new treatment is intended to treat
- Phase 3: Conducted on larger populations and compared to standard or equivalent treatments to confirm its effectiveness, monitor side effects, and collect information that will allow it to be used safely. 1,000-3,000
- Phase 4: Post-marketing studies delineate additional information, including the drug’s risks, benefits, and optimal use
Types of Trials:
- Randomised: Participants are randomly assigned to separate groups that compare different treatments
- Placebo-Controlled: The effectiveness of a new treatment is compared with a placebo
- Double-Blind: Neither the participants nor the experimenters know who is receiving a particular treatment, to prevent bias
Endpoints: These are the primary and secondary outcomes used to judge the effectiveness of a treatment
Control Group: The group in a study that receives either no treatment, a standard treatment, or a placebo
Blinding: Method used to prevent bias in research. Single-blind means the participant is unaware of which group they are in, while double-blind means both participants and researchers are unaware
Statistical Concepts
Mean: The average of a set of values. Useful for summarising a set of data with a single number, but it can be influenced by extreme values (outliers)
Median: The middle value in a list of numbers sorted from lowest to highest. Unlike the mean, the median is not affected by outliers and is a better measure of central tendency for skewed distribution
Standard Deviation (SD): A measure of the amount of variation in a set of values or dispersion in a set of values. A low SD indicates that the values tend to be close to the mean, while a high SD indicates that the values are spread out over a wider range. In clinical trials, a high SD might suggest more variability in patient responses to a treatment
P-Value: Measures the probability that an observed difference could have occurred randomly by chance, Typically, a p-value of less than 0.05 is considered statistically significant. However, statistical significance doesn’t always imply clinical significance (there might be potential confounders)
Confidence Intervals (Cl): An estimated range of values that is likely to include a population parameter with a certain degree of confidence. For example, a 95% confidence interval suggests that if the same study were conducted 100 times, the results would fall within that range 95 times. CIs provide context to the mean or effect size, showing the precision of an estimate
Effect size: Indicates the magnitude of a difference and is not dependent on sample size. Larger effect sizes suggest a greater degree of change and are usually more clinically significant
A large effect size or a statistically significant p-value generally suggests that the results are not due to chance
Standard Error of the Mean (SEM): SEM is calculated by dividing the SD of the sample by the square root of the sample size (n). (SEM = SD / √n). A smaller SEM indicates a more accurate estimate of the population mean. It means the sample mean is likely closer to the true population mean. As the sample size increases, the SEM decreases. Larger sample size provide more precise estimates of the population mean. SEM is often used in research studies to quantify the precision of the mean when it is used as an estimate of the population mean.
- Standard deviation (SD) measures the amount of variability or dispersion in a set of data.
- SEM, on the other hand, measures how far the sample mean of the data is likely to be from the true population mean. It’s essentially about the precision of the mean as an estimator
