R: The library epiR: summary information

epi.about {epiR}

R Documentation

The library epiR: summary information

Description

Tools for the analysis of epidemiological data.

Usage

epi.about()

Details

More information about the epiR package can be found at https://mvs.unimelb.edu.au/research/groups/veterinary-epidemiology-melbourne and https://www.ausvet.com.au/.

FUNCTIONS AND DATASETS

The following is a summary of the main functions and datasets in the epiR package. An alphabetical list of all functions and datasets is available by typing library(help = epiR).

For further information on any of these functions, type help(name) or ?name where name is the name of the function or dataset.

For details on how to use epiR for routine epidemiological work start R, type help.start() to open the help browser and navigate to Packages > epiR > Vignettes.

I. EPIDEMIOLOGY

1. Descriptive statistics

`epi.conf`	Confidence intervals.
`epi.descriptives`	Descriptive statistics.

2. Measures of health and measures of association

`epi.directadj`	Directly adjusted incidence rate estimates.
`epi.edr`	Compute estimated dissemination ratios from outbreak event data.
`epi.empbayes`	Empirical Bayes estimates of observed event counts.
`epi.indirectadj`	Indirectly adjusted incidence risk estimates.
`epi.insthaz`	Instantaneous hazard estimates based on Kaplan-Meier survival estimates.
`epi.2by2`	Measures of association from data presented in a 2 by 2 table.

3. Diagnostic tests

`epi.betabuster`	An R version of Wes Johnson and Chun-Lung Su's Betabuster.
`epi.herdtest`	Estimate the characteristics of diagnostic tests applied at the herd (group) level.
`epi.nomogram`	Compute the post-test probability of disease given characteristics of a diagnostic test.
`epi.pooled`	Estimate herd test characteristics when samples are pooled.
`epi.prev`	Compute the true prevalence of a disease in a population on the basis of an imperfect test.
`epi.tests`	Sensitivity, specificity and predictive value of a diagnostic test.

4. Meta-analysis

`epi.dsl`	Mixed-effects meta-analysis of binary outcome data using the DerSimonian and Laird method.
`epi.iv`	Fixed-effects meta-analysis of binary outcome data using the inverse variance method.
`epi.mh`	Fixed-effects meta-analysis of binary outcome data using the Mantel-Haenszel method.
`epi.smd`	Fixed-effects meta-analysis of continuous outcome data using the standardised mean difference method.

5. Regression analysis tools

`epi.cp`	Extract unique covariate patterns from a data set.
`epi.cpresids`	Compute covariate pattern residuals from a logistic regression model.
`epi.interaction`	Relative excess risk due to interaction in a case-control study.

6. Data manipulation tools

`epi.asc`	Write matrix to an ASCII raster file.
`epi.convgrid`	Convert British National Grid georeferences to easting and northing coordinates.
`epi.dms`	Convert decimal degrees to degrees, minutes and seconds and vice versa.
`epi.ltd`	Calculate lactation to date and standard lactation (that is, 305 or 270 day) milk yields.
`epi.offset`	Create an offset vector based on a list suitable for WinBUGS.
`epi.RtoBUGS`	Write data from an R list to a text file in WinBUGS-compatible format.

7. Sample size calculations

The naming convention for the sample size functions in epiR is: epi.ss (sample size) + an abbreviation to represent the sampling design (e.g., simple, strata, clus1, clus2) + an abbreviation of the objectives of the study (est when you want to estimate a population parameter or comp when you want to compare two groups) + a single letter defining the outcome variable type (b for binary, c for continuous and s for survival data).

`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.
`epi.ssstrataestc`	Sample size to estimate a continuous outcome using stratified random sampling.

`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.

`epi.ssclus2estb`	Sample size to estimate a binary outcome using two-stage cluster sampling.
`epi.ssclus2estc`	Sample size to estimate a continuous outcome using two-stage cluster sampling.

`epi.ssxsectn`	Sample size, power or detectable prevalence ratio for a cross-sectional study.
`epi.sscohortc`	Sample size, power or detectable risk ratio for a cohort study using count data.
`epi.sscohortt`	Sample size, power or detectable risk ratio for a cohort study using time at risk data.
`epi.sscc`	Sample size, power or detectable odds ratio for case-control studies.

`epi.sscompb`	Sample size, power and detectable risk ratio when comparing binary outcomes.
`epi.sscompc`	Sample size, power and detectable risk ratio when comparing continuous outcomes.
`epi.sscomps`	Sample size, power and detectable hazard when comparing time to event.

`epi.ssequb`	Sample size for a parallel equivalence or equality trial, binary outcome.
`epi.ssequc`	Sample size for a parallel equivalence or equality trial, continuous outcome.

`epi.sssupb`	Sample size for a parallel superiority trial, binary outcome.
`epi.sssupc`	Sample size for a parallel superiority trial, continuous outcome.

`epi.ssninfb`	Sample size for a non-inferiority trial, binary outcome.
`epi.ssninfc`	Sample size for a non-inferiority trial, continuous outcome.

`epi.ssdetect`	Sample size to detect an event.
`epi.ssdxsesp`	Sample size to estimate the sensitivity or specificity of a diagnostic test.
`epi.ssdxtest`	Sample size to validate a diagnostic test in the absence of a gold standard.

8. Miscellaneous functions

`epi.prcc`	Compute partial rank correlation coefficients.
`epi.psi`	Compute proportional similarity indices.
`epi.realrisk`	Return absolute risks from odds, incidence risk and hazard ratios.

9. Data sets

`epi.epidural`	Rates of use of epidural anaesthesia in trials of caregiver support.
`epi.incin`	Laryngeal and lung cancer cases in Lancashire 1974 - 1983.
`epi.SClip`	Lip cancer in Scotland 1975 - 1980.

II. SURVEILLANCE

Below, SSe stands for surveillance system sensitivity. That is, the average probability that a surveillance system (as a whole) will return a positive surveillance outcome, given disease is present in the population at a level equal to or greater than a specified design prevalence.

1. Representative sampling — sample size

`rsu.sspfree.rs`	Defined probability of disease freedom.
`rsu.sssep.rs`	SSe, perfect test specificity.
`rsu.sssep.rs2st`	SSe, two stage sampling.
`rsu.sssep.rsfreecalc`	SSe, imperfect test specificity.
`rsu.sssep.rspool`	SSe, pooled sampling.

2. Representative sampling — surveillance system sensitivity and specificity

`rsu.sep.rs`	SSe, representative sampling.
`rsu.sep.rs2st`	SSe, representative two-stage sampling.
`rsu.sep.rsmult`	SSe, representative multiple surveillance components.
`rsu.sep.rsfreecalc`	SSe, imperfect test specificity.
`rsu.sep.rspool`	SSe, representative pooled sampling.
`rsu.sep.rsvarse`	SSe, varying surveillance unit sensitivity.
`rsu.spp.rs`	Surveillance system specificity.

3. Representative sampling — probability of disease freedom

`rsu.pfree.rs`	Probability of disease freedom for a single or multiple time periods.
`rsu.pfree.equ`	Equilibrium probability of disease freedom.

4. Risk-based sampling — sample size

`rsu.sssep.rbsrg`	SSe, single sensitivity for each risk group.
`rsu.sssep.rbmrg`	SSe, multiple sensitivities within risk groups.
`rsu.sssep.rb2st1rf`	SSe, 2 stage sampling, 1 risk factor.
`rsu.sssep.rb2st2rf`	SSe, 2 stage sampling, 2 risk factors.

5. Risk-based sampling — surveillance system sensitivity and specificity

`rsu.sep.rb`	SSe, risk-based sampling.
`rsu.sep.rb1rf`	SSe, risk-based sampling, 1 risk factor.
`rsu.sep.rb2rf`	SSe, risk-based sampling, 2 risk factors.
`rsu.sep.rbvarse`	SSe, risk-based sampling, varying unit sensitivity.
`rsu.sep.rb2st`	SSe, 2-stage risk-based sampling.

6. Risk-based sampling — probability of disease freedom

`rsu.pfree.equ`	Equilibrium probability of disease freedom.

7. Census sampling — surveillance system sensitivity

`rsu.sep.cens`	SSe, census sampling.

8. Passive surveillance — surveillance system sensitivity

`rsu.sep.pass`	SSe, passive surveillance.

9. Miscellaneous functions

`rsu.adjrisk`	Adjusted risk values.
`rsu.dxtest`	Series and parallel diagnostic test interpretation.
`rsu.epinf`	Effective probability of disease.
`rsu.pstar`	Design prevalence back calculation.
`rsu.sep`	Probability disease is less than specified design prevalence.

Author(s)

Mark Stevenson (mark.stevenson1@unimelb.edu.au), Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville Victoria 3010, Australia.

Evan Sergeant (evansergeant@gmail.com), Ausvet Pty Ltd, Level 1 34 Thynne St, Bruce ACT 2617, Australia.

Simon Firestone, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville Victoria 3010, Australia.

Telmo Nunes, UISEE/DETSA, Faculdade de Medicina Veterinaria — UTL, Rua Prof. Cid dos Santos, 1300 - 477 Lisboa Portugal.

Javier Sanchez, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown Prince Edward Island, C1A 4P3, Canada.

Ron Thornton, Ministry for Primary Industries New Zealand, PO Box 2526 Wellington, New Zealand.

With contributions from: Cord Heuer, Jonathon Marshall, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Yoshida Kazuki, Geoff Jones, Sarah Pirikahu, Ryan Kyle, Johann Popp, Methew Jay, Allison Cheung, Nagendra Singanallur, Aniko Szabo and Ahmad Rabiee.

[Package epiR version 2.0.73 Index]