-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
100 lines (80 loc) · 4.58 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# Misclassification Bias in Longitudinal Udder Health Studies
[](https://travis-ci.org/dhaine/misclass)
[](https://cran.r-project.org/package=misclass)
R package to assess misclassification bias in hierarchical longitudinal studies
and the effect of various sampling strategies to control for it.
This package is part of the scientific paper "Diagnosing intramammary infection:
Controlling misclassification bias in longitudinal udder health studies" (in
[Preventive Veterinary
Medicine](https://doi.org/10.1016/j.prevetmed.2017.11.010)) and the [2017
SVEPM](http://www.svepm2017.org/) proceedings "Sampling Strategies to Control
Misclassification Bias in Longitudinal Udder Health Studies" by Denis Haine, Ian
Dohoo, Daniel Scholl, Henryk Stryhn, and Simon Dufour.
Learn more by reading the paper or in `vignette("misclass")`.
## License
This package is free and open source software, licensed under GPL-2.
## Abstract
Using imperfect tests may lead to biased estimates of disease frequency and of
associations between risk factors and disease.
For instance in longitudinal udder health studies, both quarters at risk and
incident intramammary infections (IMI) can be wrongly identified, resulting in
selection and misclassification bias, respectively.
Diagnostic accuracy can possibly be improved by using duplicate or triplicate
samples for identifying quarters at risk and, subsequently, incident IMI.
The objectives of this study were to evaluate the relative impact of selection
and misclassification biases resulting from IMI misclassification on measures of
disease frequency (incidence) and of association with hypothetical exposures.
The effect of improving the sampling strategy by collecting duplicate or
triplicate samples at first or second sampling was also assessed.
Data sets from a hypothetical cohort study were simulated and analyzed based on
a separate scenario for two common mastitis pathogens representing two distinct
prevailing patterns.
*Staphylococcus aureus*, a relatively uncommon pathogen with a low incidence, is
identified with excellent sensitivity and almost perfect specificity.
Coagulase negative staphylococci (CNS) are more prevalent, with a high
incidence, and with milk bacteriological culture having fair Se but excellent
Sp.
The generated data sets for each scenario were emulating a longitudinal cohort
study with two milk samples collected one month apart from each quarter of a
random sample of 30 cows/herd, from 100 herds, with a herd-level exposure having
a known strength of association.
Incidence of IMI and measure of association with exposure (odds ratio; OR) were
estimated using Markov Chain Monte Carlo (MCMC) for each data set and using
different sampling strategies (single, duplicate, triplicate samples with series
or parallel interpretation) for identifying quarters at risk and incident IMI.
For *S. aureus* biases were small with an observed incidence of 0.29 versus a
true incidence of 0.25 IMI/100 quarter-month.
In the CNS scenario, diagnostic errors in the two samples led to important
selection (40 IMI/100 quarter-month) and misclassification (23 IMI/100
quarter-month) biases for estimation of IMI incidence, respectively.
These biases were in opposite direction and therefore the incidence measure
obtained using single sampling on both the first and second test (29 IMI/100
quarter-month) was exactly the true value.
In the *S.aureus* scenario the OR for association with exposure showed little
bias (observed OR of 3.1 versus true OR of 3.2).
The CNS scenario revealed the presence of a large misclassification bias moving
the association towards the null value (OR of 1.7 versus true OR of 2.6).
Little improvement could be brought using different sampling strategies aiming
at improving Se and/or Sp on first and/or second sampling or using a two out of
three interpretation for IMI definition.
Increasing number of samples or tests can prevent bias in some situations but
efforts can be spared by holding to a single sampling approach in others.
When designing longitudinal studies, evaluating potential biases and best sampling
strategy is as critical as the choice of test.
## Installation
You can get the most current version from **GitHub** with **devtools** package:
```{r, eval=FALSE}
devtools::install_github('dhaine/misclass')
```