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epi.about

The library epiR: summary information

Description

Tools for the analysis of epidemiological data.

Usage

epi.about()

Details

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.

CONTENTS:

The functions in epiR can be categorised into tools for analysis of epidemiological and surveillance data. A summary of the package functions is as follows:

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 continous 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 continous 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 trial, binary outcome.
epi.ssequc Sample size for a parallel equivalence 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.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.

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

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), Faculty of Veterinary and Agricultural Sciences, 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, Faculty of Veterinary and Agricultural Sciences, 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.

epiR

Tools for the Analysis of Epidemiological Data

v2.0.19
GPL (>= 2)
Authors
Mark Stevenson <mark.stevenson1@unimelb.edu.au> and Evan Sergeant <evansergeant@gmail.com> with contributions from Telmo Nunes, Cord Heuer, Jonathon Marshall, Javier Sanchez, Ron Thornton, Jeno Reiczigel, Jim Robison-Cox, Paola Sebastiani, Peter Solymos, Kazuki Yoshida, Geoff Jones, Sarah Pirikahu, Simon Firestone, Ryan Kyle, Johann Popp, Mathew Jay and Charles Reynard.
Initial release
2021-01-12

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