epiR

Summary measures for count data presented in a 2 by 2 table

R to WinBUGS data conversion

Lip cancer in Scotland 1975 - 1980

The library epiR: summary information

Write matrix to an ASCII raster file

An R version of Wes Johnson and Chun-Lung Su's Betabuster

Bohning's test for overdispersion of Poisson data

Concordance correlation coefficient

Confidence intervals for means, proportions, incidence, and standardised mortality ratios

Convert British National Grid georeferences to easting and northing coordinates

Extract unique covariate patterns from a data set

Covariate pattern residuals from a logistic regression model

Descriptive statistics

Estimate the precision of a [structured] heterogeneity term

Directly adjusted incidence rate estimates

Decimal degrees and degrees, minutes and seconds conversion

Mixed-effects meta-analysis of binary outcomes using the DerSimonian and Laird method

Estimated dissemination ratio

Empirical Bayes estimates of observed event counts

Rates of use of epidural anaesthesia in trials of caregiver support

Estimate the characteristics of diagnostic tests applied at the herd (group) level

Laryngeal and lung cancer cases in Lancashire 1974 - 1983

Indirectly adjusted incidence risk estimates

Event instantaneous hazard based on Kaplan-Meier survival estimates

Relative excess risk due to interaction in a case-control study

Fixed-effects meta-analysis of binary outcomes using the inverse variance method

Kappa statistic

Lactation to date and standard lactation milk yields

Fixed-effects meta-analysis of binary outcomes using the Mantel-Haenszel method

Post-test probability of disease given sensitivity and specificity of a test

Overall concordance correlation coefficient (OCCC)

Create offset vector

Estimate herd test characteristics when pooled sampling is used

Estimate population size

Partial rank correlation coefficients

Estimate true prevalence

Proportional similarity index

Fixed-effects meta-analysis of continuous outcomes using the standardised mean difference method

Confidence intervals and tests of significance of the standardised mortality [morbidity] ratio

Sample size, power or minimum detectable odds ratio for an unmatched or matched case-control study

Sample size to estimate a binary outcome using one-stage cluster sampling

Sample size to estimate a continuous outcome using one-stage cluster sampling

Number of clusters to be sampled to estimate a binary outcome using two-stage cluster sampling

Number of clusters to be sampled to estimate a continuous outcome using two-stage cluster sampling

Sample size, power or minimum detectable incidence risk ratio for a cohort study using individual count data

Sample size, power or minimum detectable incidence rate ratio for a cohort study using person or animal time data

Sample size, power and minimum detectable risk ratio when comparing binary outcomes

Sample size, power and minimum detectable difference when comparing continuous outcomes

Sample size, power and minimum detectable hazard when comparing time to event

Sample size to detect an event

Sample size to validate a diagnostic test in the absence of a gold standard

Sample size for a parallel equivalence trial, binary outcome

Sample size for a parallel equivalence trial, continuous outcome

Sample size for a non-inferiority trial, binary outcome

Sample size for a non-inferiority trial, continuous outcome

Sample size to estimate a binary outcome using simple random sampling

Sample size to estimate a continuous outcome using simple random sampling

Sample size to estimate a binary outcome using stratified random sampling

Sample size to estimate a continuous outcome using a stratified random sampling design

Sample size for a parallel superiority trial, binary outcome

Sample size for a parallel superiority trial, continuous outcome

Sample size, power or minimum detectable prevalence ratio or odds ratio for a cross-sectional study

Sensitivity, specificity and predictive value of a diagnostic test

Adjusted risk values

Sensitivity and specificity of diagnostic tests interpreted in series or parallel

Effective probability of disease

Equilibrium probability of disease freedom assuming representative or risk based sampling

Calculate Probability of freedom for given population sensitivity and probability of introduction

Design prevalence back calculation

Probability that the prevalence of disease in a population is less than or equal to a specified design prevalence

Surveillance system sensitivity assuming data from a population census

Surveillance system sensitivity assuming passive surveillance and representative sampling within clusters

Surveillance system sensitivity assuming risk-based sampling and varying unit sensitivity

Surveillance system sensitivity assuming risk-based sampling on one risk factor

Surveillance system sensitivity assuming risk-based sampling on two risk factors

Surveillance system sensitivity assuming risk based, two-stage sampling

Surveillance system sensitivity assuming risk based sampling and varying unit sensitivity

Surveillance system sensitivity assuming representative sampling

Surveillance system sensitivity assuming representative two-stage sampling

Surveillance system sensitivity for detection of disease assuming representative sampling and imperfect test sensitivity and specificity.

Surveillance system sensitivity by combining multiple surveillance components

Surveillance system sensitivity assuming representative sampling, imperfect pooled sensitivity and perfect pooled specificity

Surveillance system sensitivity assuming representative sampling and varying unit sensitivity

Surveillance system specificity assuming representative sampling

Sample size to achieve a desired probability of disease freedom assuming representative sampling

Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on one risk factor at the cluster level

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 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

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 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