An R version of Wes Johnson and Chun-Lung Su's Betabuster
The library epiR: summary information
Concordance correlation coefficient
Summary measures for count data presented in a 2 by 2 table
Lip cancer in Scotland 1975 - 1980
Number of parameters to be inferred and number of informative priors required for a Bayesian latent class model
Write matrix to an ASCII raster file
R to WinBUGS data conversion
Bohning's test for overdispersion of Poisson data
Estimated dissemination ratio
Extract unique covariate patterns from a data set
Convert British National Grid georeferences to easting and northing coordinates
Decimal degrees and degrees, minutes and seconds conversion
Mixed-effects meta-analysis of binary outcomes using the DerSimonian and Laird method
Descriptive statistics
Confidence intervals for means, proportions, incidence, and standardised mortality ratios
Estimate the precision of a [structured] heterogeneity term
Empirical Bayes estimates of observed event counts
Covariate pattern residuals from a logistic regression model
Directly adjusted incidence rate estimates
Lactation to date and standard lactation milk yields
Event instantaneous hazard based on Kaplan-Meier survival estimates
Rates of use of epidural anaesthesia in trials of caregiver support
Laryngeal and lung cancer cases in Lancashire 1974 - 1983
Indirectly adjusted incidence risk estimates
Estimate the characteristics of diagnostic tests applied at the herd (group) level
Relative excess risk due to interaction in a case-control study
An R version of the Winton Centre's RealRisk calculator
Proportional similarity index
Kappa statistic
Estimate herd test characteristics when pooled sampling is used
Post-test probability of disease given sensitivity and specificity of a test
Overall concordance correlation coefficient (OCCC)
Estimate true prevalence and the expected number of false positives
Estimate population size on the basis of capture-recapture sampling
Fixed-effects meta-analysis of continuous outcomes using the standardised mean difference method
Create offset vector
Fixed-effects meta-analysis of binary outcomes using the inverse variance method
Fixed-effects meta-analysis of binary outcomes using the Mantel-Haenszel method
Sample size, power or minimum detectable incidence risk ratio for a cohort study using individual count data
Partial rank correlation coefficients
Confidence intervals and tests of significance of the standardised mortality [morbidity] ratio
Sample size, power or minimum detectable incidence rate ratio for a cohort study using person or animal time data
Sample size and power when comparing binary outcomes
Sample size to estimate a binary outcome using one-stage cluster sampling
Number of clusters to be sampled to estimate a binary outcome using two-stage cluster sampling
Sample size and power when comparing continuous outcomes
Sample size, power or minimum detectable odds ratio for an unmatched or matched case-control study
Sample size to estimate a continuous outcome using one-stage cluster sampling
Number of clusters to be sampled to estimate a continuous outcome using two-stage cluster sampling
Sample size for a non-inferiority trial, binary outcome
Sample size to estimate a binary outcome using simple random sampling
Sample size for a non-inferiority trial, continuous outcome
Sample size to estimate a continuous outcome using simple random sampling
Sample size for a parallel equivalence or equality trial, binary outcome
Sample size to estimate the sensitivity or specificity of a diagnostic test
Sample size for a parallel equivalence or equality trial, continuous outcome
Sample size and power when comparing time to event
Sample size to validate a diagnostic test in the absence of a gold standard
Sample size to detect an event
Adjusted risk values
Sample size, power or minimum detectable prevalence ratio or odds ratio for a cross-sectional study
Sensitivity and specificity of diagnostic tests interpreted in series or parallel
Sample size for a parallel superiority trial, binary outcome
Sample size to estimate a continuous outcome using a stratified random sampling design
Sample size for a parallel superiority trial, continuous outcome
Sample size to estimate a binary outcome using stratified random sampling
Design prevalence back calculation
Calculate the probability of freedom for given population sensitivity and probability of introduction
Sensitivity, specificity and predictive value of a diagnostic test
Surveillance system sensitivity assuming risk-based sampling on one risk factor
Surveillance system sensitivity assuming risk-based sampling on two risk factors
Effective probability of disease
Surveillance system sensitivity assuming passive surveillance and representative sampling within clusters
Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on two risk factors at either the cluster level, unit level, or both
Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on one risk factor at the cluster level
Equilibrium probability of disease freedom assuming representative or risk based sampling
Surveillance system specificity assuming representative sampling
Surveillance system sensitivity assuming risk-based sampling and varying unit sensitivity
Probability that the prevalence of disease in a population is less than or equal to a specified design prevalence
Surveillance system sensitivity assuming risk based, two-stage sampling
Sample size to achieve a desired probability of disease freedom assuming representative sampling
Surveillance system sensitivity assuming risk based sampling and varying unit sensitivity
Surveillance system sensitivity assuming representative sampling
Surveillance system sensitivity assuming data from a population census
Surveillance system sensitivity assuming representative sampling, imperfect pooled sensitivity and perfect pooled specificity
Surveillance system sensitivity assuming representative two-stage sampling
Surveillance system sensitivity assuming representative sampling and varying unit sensitivity
Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.
Surveillance system sensitivity by combining multiple surveillance components
Sample size to achieve a desired surveillance system sensitivity to detect disease at a specified design prevalence assuming representative sampling, imperfect unit sensitivity and specificity
Sample size to achieve a desired surveillance system sensitivity using pooled samples assuming representative sampling
Sample size to achieve a desired surveillance system sensitivity assuming representative sampling
Sample size to achieve a desired surveillance system sensitivity assuming two-stage sampling
Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and multiple sensitivity values within risk groups
Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and a single sensitivity value for each risk group