Clinical Measures, Inferential Statistics, Reliability, and Validity

These flashcards cover key terms and definitions related to clinical measures, inferential statistics, and the concepts of reliability and validity.

Relative Risk (RR)

The ratio of the rate of the outcome in the exposed group to the rate of the outcome in the unexposed group.

Absolute Risk Reduction (ARR)

The difference in the rate of the outcome between the exposed and unexposed groups.

Number Needed to Treat (NNT)

The number of individuals that need to be treated with an intervention to prevent one adverse outcome.

Odds Ratio (OR)

The ratio of the odds of exposure for case subjects to the odds of exposure for control subjects.

Specificity

The true negative rate; how well a test identifies those without the disease.

Population vs. Sample

Population refers to all elements being studied, while a sample is a portion selected for study.

Standard Error of the Mean (SEM)

An estimate of the amount of error when a sample mean is used to estimate the population mean.

Levels of Confidence (LOC)

A percentage indicating the probability that a statement about a population mean is correct.

Hypothesis Testing

A method used to determine statistical significance by comparing null and alternate hypotheses.

Type I Error

Rejecting a true null hypothesis, also known as a false positive.

Type II Error

Failing to reject a false null hypothesis, also known as a false negative.

Power of the Test (1 - β)

The probability of correctly rejecting a false null hypothesis.

Degrees of Freedom

Defines the shape of the sampling distribution, increasing with a larger sample size.

Reliability

The repeatability of scores resulting from a testing procedure; consistency of measurement.

Validity

The degree to which evidence supports the interpretations of test scores for proposed uses.

Standard Error of Measurement (SEM)

The measure of the reliability of measurement reflecting how much observed scores fluctuate due to measurement errors.

Relative Risk (RR) Formula and Interpretation

Formula: RR = \frac{\text{Risk in exposed group}}{\text{Risk in unexposed group}} = \frac{a/(a+b)}{c/(c+d)} (using a 2x2 table) Interpretation: An RR > 1 indicates increased risk in the exposed group; an RR < 1 indicates decreased risk; an RR = 1 indicates no difference in risk.

Absolute Risk Reduction (ARR) Formula and Interpretation

Formula: ARR = \text{Risk in unexposed group} - \text{Risk in exposed group} = \frac{c}{c+d} - \frac{a}{a+b} Interpretation: The absolute difference in risk between groups, indicating how much the intervention reduces the risk.

Number Needed to Treat (NNT) Formula and Interpretation

Formula: NNT = \frac{1}{\text{ARR}} Interpretation: The number of patients one needs to treat with an intervention to prevent one adverse event. A lower NNT is generally considered better.

Odds Ratio (OR)

The ratio of the odds of exposure for case subjects to the odds of exposure for control subjects.

Odds Ratio (OR) Formula and Interpretation

Formula: OR = \frac{\text{Odds of exposure in cases}}{\text{Odds of exposure in controls}} = \frac{a/c}{b/d} = \frac{ad}{bc} (using a 2x2 table) Interpretation: An OR > 1 indicates increased odds of exposure in cases; an OR < 1 indicates decreased odds; an OR = 1 indicates no difference in odds. Commonly used in case-control studies.

Sensitivity

The true positive rate; how well a test detects those with the disease.

Sensitivity Formula and Interpretation

Formula: Sensitivity = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Negatives (FN)}} Interpretation: The probability that a test result will be positive when the disease is truly present.

Specificity Formula and Interpretation

Formula: Specificity = \frac{\text{True Negatives (TN)}}{\text{True Negatives (TN)} + \text{False Positives (FP)}} Interpretation: The probability that a test result will be negative when the disease is truly absent.

Sampling Error

The amount of error in the estimate of a population parameter based on a sample statistic.

Standard Error of the Mean (SEM) Formula and Interpretation

Formula: SEM = \frac{\sigma}{\sqrt{n}} where \sigma is the population standard deviation and n is the sample size. If \sigma is unknown, the sample standard deviation s can be used: SEM = \frac{s}{\sqrt{n}} Interpretation: Measures the precision of the sample mean as an estimate of the population mean; a smaller SEM indicates a more precise estimate.

Hypothesis Testing Interpretation

A statistical procedure for making decisions about population parameters based on sample data. It involves setting up a null hypothesis (H0) and an alternative hypothesis (Ha) and using data to determine if there is enough evidence to reject H_0.

Type I Error Interpretation

Occurs when a true null hypothesis is incorrectly rejected. The probability of committing a Type I error is denoted by alpha (\alpha), which is often set at 0.05.

Type II Error Interpretation

Occurs when a false null hypothesis is incorrectly accepted (not rejected). The probability of committing a Type II error is denoted by beta (\beta).

Power of the Test Formula and Interpretation

Formula: Power = 1 - \beta Interpretation: The probability of correctly rejecting a false null hypothesis. A higher power indicates a greater chance of detecting a true effect if one exists, typically desired to be 0.80 or higher.

Degrees of Freedom Interpretation

The number of independent values or pieces of information that can vary in an analysis without breaking any constraints. It varies depending on the statistical test being performed (e.g., n-1 for a sample variance).

Reliability Interpretation

The consistency or stability of a measurement taken under similar conditions. High reliability means that a measurement tool produces consistent results over time or across different observers.

Validity Interpretation

The extent to which a test actually measures what it claims to measure. It refers to the appropriateness, meaningfulness, and usefulness of the specific inferences made from test scores.

Test-Criterion Validity

The relationship between test scores and external variables considered direct measures.

Test-Criterion Validity Interpretation

How well a test predicts an outcome (the criterion). It assesses the test's effectiveness in predicting performance or behavior in another situation or at a later time.