Spearman Correlation
Spearman correlation is a rank-based measure from -1 to +1 of how consistently two variables, such as disease severity and costs, rise or fall together.
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Spearman correlation is a rank-based measure from -1 to +1 of how consistently two variables, such as disease severity and costs, rise or fall together.
Specificity is the share of people without the target condition whom a diagnostic test correctly calls negative, setting how many false positives arise.
A spline model is a regression model that joins polynomial pieces at knots to fit a flexible curve, such as hospital cost by age or survival over time.
Standard costing sets the expected cost of a health service in advance from standard quantities and prices, then compares actual costs with this benchmark.
Standard deviation is a measure of how widely patient costs, outcomes or other values spread around their mean, in the same units as the data.
The standard error (SE) is the standard deviation of an estimate's sampling distribution, showing how precisely a mean cost or treatment effect is known.
The standard gamble (SG) values a health state for QALYs as the chance of full health, against death, that makes a gamble as good as the state for sure.
The standard normal distribution is a normal distribution with mean 0 and standard deviation 1, used in health economics to read z values as probabilities.
The standardised mean difference (SMD) is a mean difference divided by a standard deviation, used to pool continuous outcomes measured on different scales.
The standardised mortality ratio (SMR) is observed deaths divided by deaths expected at reference rates for a group's age mix, used to gauge excess deaths.
A standardised residual is a regression residual divided by its estimated standard deviation, used to flag outlying patients in cost and outcome models.
The standardised response mean (SRM) is a responsiveness index for health outcome measures, the mean change divided by the standard deviation of change.
State occupancy is the expected time a modelled cohort spends in a health state, found by adding the share of the cohort in that state across model cycles.
A cost, utility, health outcome, or other consequence accrued while a modelled person occupies a specified state for a defined amount of time.
A state space model is a time series model where hidden states, such as disease severity or service demand, evolve over time and are measured with error.
A state transition model is a decision model in which a cohort or simulated patients move between health states over time to estimate costs and QALYs.
Statistical power is the probability a trial gives a significant result when a true effect of set size exists, used to size clinical and economic trials.
Statistical process control (SPC) is a set of statistical methods for monitoring health service data over time to tell routine variation from real change.
Statistical significance is a threshold-based conclusion that an observed result is sufficiently incompatible with a prespecified null hypothesis under a stated statistical model to meet the chosen significance level.
A steady state in a Markov model is the stationary distribution of a cohort across health states, unchanged from cycle to cycle under fixed transitions.