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epiR (version 2.0.19)

Tools for the Analysis of Epidemiological Data

Description

Tools for the analysis of epidemiological and surveillance data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, computation of confidence intervals around incidence risk and incidence rate estimates and sample size calculations for cross-sectional, case-control and cohort studies. Surveillance tools include functions to calculate an appropriate sample size for 1- and 2-stage representative freedom surveys, functions to estimate surveillance system sensitivity and functions to support scenario tree modelling analyses.

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Version

Install

install.packages('epiR')

Monthly Downloads

14,646

Version

2.0.19

License

GPL (>= 2)

Maintainer

Mark Stevenson

Last Published

January 12th, 2021

Functions in epiR (2.0.19)

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

Convert British National Grid georeferences to easting and northing coordinates

Lip cancer in Scotland 1975 - 1980

The library epiR: summary information

Write matrix to an ASCII raster file

R to WinBUGS data conversion

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

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

Bohning's test for overdispersion of Poisson data

Concordance correlation coefficient

Directly adjusted incidence rate estimates

Rates of use of epidural anaesthesia in trials of caregiver support

Empirical Bayes estimates of observed event counts

epi.indirectadj

Indirectly adjusted incidence risk estimates

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

Estimate the precision of a [structured] heterogeneity term

Decimal degrees and degrees, minutes and seconds conversion

Extract unique covariate patterns from a data set

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

epi.descriptives

Descriptive statistics

epi.interaction

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

Event instantaneous hazard based on Kaplan-Meier survival estimates

Covariate pattern residuals from a logistic regression model

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

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

Laryngeal and lung cancer cases in Lancashire 1974 - 1983

Estimate herd test characteristics when pooled sampling is used

Estimate population size

Estimated dissemination ratio

Overall concordance correlation coefficient (OCCC)

Create offset vector

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

Lactation to date and standard lactation milk yields

Partial rank correlation coefficients

Proportional similarity index

Kappa statistic

Estimate true prevalence

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

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

epi.ssclus1estb

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

epi.ssclus1estc

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

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

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

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

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

epi.ssstrataestc

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

epi.ssclus2estb

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

Sample size for a parallel superiority trial, binary outcome

Sample size for a non-inferiority trial, continuous outcome

epi.ssclus2estc

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

epi.sssimpleestb

Sample size to estimate a binary outcome using simple random sampling

epi.sssimpleestc

Sample size to estimate a continuous outcome using simple random sampling

epi.ssstrataestb

Sample size to estimate a binary outcome using stratified random sampling

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 to detect an event

Sample size for a parallel equivalence trial, continuous outcome

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

Sample size for a non-inferiority trial, binary outcome

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

Equilibrium probability of disease freedom assuming representative or risk based sampling

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

Design prevalence back calculation

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

Surveillance system sensitivity assuming data from a population census

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 passive surveillance and representative sampling within clusters

Effective probability of disease

Sensitivity and specificity of diagnostic tests interpreted in series or parallel

Surveillance system sensitivity assuming representative sampling

rsu.sep.rbvarse

Surveillance system sensitivity assuming risk based sampling and varying unit sensitivity

Sensitivity, specificity and predictive value of a diagnostic test

Surveillance system sensitivity assuming representative two-stage sampling

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

Adjusted risk values

rsu.sep.rsfreecalc

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 specificity assuming representative sampling

rsu.sssep.rb2st2rf

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

rsu.sep.rsvarse

Surveillance system sensitivity assuming representative sampling and varying unit sensitivity

rsu.sssep.rs2st

Sample size to achieve a desired surveillance system sensitivity assuming two-stage sampling

Sample size to achieve a desired surveillance system sensitivity assuming representative sampling

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

rsu.sssep.rsfreecalc

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

rsu.sssep.rbmrg

Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and multiple sensitivity values within risk groups

rsu.sssep.rbsrg

Sample size to achieve a desired surveillance system sensitivity assuming risk-based sampling and a single sensitivity value for each risk group

Surveillance system sensitivity assuming risk based, two-stage sampling

rsu.sssep.rspool

Sample size to achieve a desired surveillance system sensitivity using pooled samples assuming representative sampling

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

rsu.sssep.rb2st1rf

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