Summary:
This article covered several of the basic concepts that underlie the majority of statistical analyses reported in the clinical research literature: variability, hypothesis testing, CIs and sample size.
Variability :
Is the source of uncertainty in clinical studies.
Measures of variability:
- Standard deviation (SD) : is the distribution of the sample data around the sample mean. Small SD little variability.
- Standard error (SE): measures the variability of sample means if the study were repeated many times.
Researchers use two approaches to quantify this uncertainty:
hypothesis testing with P values, and estimation with confidence intervals
- Hypothesis testing: test relationship between exposure and outcome. Null, alternative hypothesis.
- P-value: measures the probability that the results are due to chance. P< α: reject null hypothesis.
- Confidence intervals (CIs): to estimate the upper and lower limits of the variability in the sample data, measures significance, and association of Relative Risk (RR). 95 percent CI does not include 1significant results
- Relative Risk (RR): measure the strength of the association, 1=no association, RR>1: positive association, RR<1: protective factor
- Power: is the probability that the study will be able to detect a difference/association
Clinical significance:
