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Prevalence
Analyse 2-stage prevalence data
Bayesian estimation of true prevalence from survey testing with one test
Bayesian estimation of true prevalence from survey testing with two tests
Compare 2 prevalence estimates
Estimated true prevalence with an imperfect test
Pooled prevalence for fixed pool size and tests with known sensitivity and specificity
Pooled prevalence for fixed pool size and tests with uncertain sensitivity and specificity
Pooled prevalence for fixed pool size and perfect tests
Pooled prevalence for variable pool size and perfect tests
Pooled prevalence using a Gibbs sampler
Sample size calculation for fixed pool size and perfect tests
Sample size calculation for fixed pool size and uncertain sensitivity and specificity
Sample size for apparent or sero-prevalence
Sample size to estimate true prevalence
Simulate sampling for fixed pool size and assumed known test sensitivity and specificity
Simulate sampling for fixed pool size and assumed perfect test
Simulate sampling for fixed pool size and uncertain test sensitivity and specificity
Simulate sampling for variable pool sizes
Simulated true prevalence with an imperfect test
Freedom
1-Stage Freedom analysis
Analyse results
Sample Size
Representative 1-stage
Back-calculate design prevalence
Confidence of freedom for multiple time periods
Confidence of freedom for a single time period
Population sensitivity - constant unit sensitivity
Population sensitivity - pooled sampling
Population sensitivity - varying unit sensitivity
Sample size - perfect test specificity
Sample size - pooled sampling in a large population
Sample size for target confidence of freedom
Representative 2-stage
Analyse 2-stage survey - actual data
Analyse 2-stage survey - fixed sample size
Least-cost sample sizes from sampling frame
Least-cost sample sizes - no sampling frame
Sample sizes - specified cluster sensitivity
Stochastic analysis - 2-stage freedom data
Risk based 1-stage
Sample Size - single level
Sample Size - single level - different sensitivity
Sensitivity - single level
Sensitivity - single level - different sensitivity
Studies
Bioequivalence analysis
Confidence limits
Difference between 2 means
Difference between 2 proportions
Mean value
Median Value
Proportion
Probability Distributions
Beta distributions for given α and β parameters
Chi-squared distributions
F distributions
Multiple Beta distributions
Normal distributions
Pert distributions for given minimum, mode and maximum values
Summarise Binomial distribution
Single Beta distribution from mode and 5/95 percentiles
Single Beta distribution from count data
T distributions
Significance
1-sample t-test on summary data
1-sample test for mean or median compared to population estimate
2-sample t-test on summary data
Chi-squared test from cross-tabulation of raw data
Chi-squared test for homogeneity of a sample
Chi-squared test for r x c tables
Chi-squared test for trend
Mantel-Haenszel for stratified 2x2 tables
McNemar's test for paired data
Statistical analysis of numeric data
Summary statistics for a 2 by 2 table
T-test or Wilcoxon signed rank test on paired data
Z-test for a single sample proportion
Z-test to compare 2 sample proportions
Summarise
2 by 2 table
Continuous data grouped by category
Continuous data single column
Diagnostics
2 tests in parallel or series
Compare two tests
Estimated true prevalence and predictive values from survey testing
Likelihood ratios and probability of infection in a tested individual
Positive and negative predictive values for a test
Probabilities of numbers of false positives
Probability of infection in a test-negative sample
Repeatability analysis for test with continuous outcome
ROC analysis for test with continuous outcome
Test evaluation against a gold standard
Sampling
Geographic Coordinates
Owner List
Random Number List
With Sampling Frame
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