This vignette shows the general purpose and usage of the
`mcradds`

R package.

`mcradds`

is a successor of the `mcr`

R package
that is developed by Roche, and therefore the fundamental coding ideas
for method comparison regression have been borrowed from it. In
addition, I supplement a series of useful functions and methods based on
several reference documents from CLSI and NMPA guidance. You can perform
the statistical analysis and graphics in different IVD trials utilizing
these analytical functions.

However, unfortunately these functions and methods have not been validated and QC’ed, I can not guarantee that all of them are entirely proper and error-free. But I always strive to compare the results to other resources in order to obtain a consistent for them. And because some of them were utilized in my past routine workflow, so I believe the quality of this package is temporarily sufficient to use.

In this vignette you are going to learn how to:

- Estimate of sample size for trials, following NMPA guideline.
- Evaluate diagnostic accuracy with/without reference, following CLSI EP12-A2.
- Perform regression methods analysis and plots, following CLSI EP09-A3.
- Perform bland-Altman analysis and plots, following CLSI EP09-A3.
- Detect outliers with 4E method from CLSI EP09-A2 and ESD from CLSI EP09-A3.
- Estimate bias in medical decision level, following CLSI EP09-A3.
- Perform Pearson and Spearman correlation analysis adding hypothesis test and confidence interval.
- Evaluate Reference Range/Interval, following CLSI EP28-A3 and NMPA guideline.
- Add paired ROC/AUC test for superiority and non-inferiority trials, following CLSI EP05-A3/EP15-A3.
- Perform reproducibility analysis (reader precision) for immunohistochemical assays, following CLSI I/LA28-A2 and NMPA guideline.
- Evaluate precision of quantitative measurements, following CLSI EP05-A3.
- Descriptive statistics summary.

The reference of `mcradds`

functions is available on the
mcradds website functions reference.

Every above analysis purpose can be achieved by few functions or S4
methods from `mcradds`

package, I will present the general
usage below.

The packages used in this vignette are:

The data sets with different purposes used in this vignette are:

```
data("qualData")
data("platelet")
data(creatinine, package = "mcr")
data("calcium")
data("ldlroc")
data("PDL1RP")
data("glucose")
data("adsl_sub")
```

Suppose that the expected sensitivity criteria of an new assay is
`0.9`

, and the clinical acceptable criteria is
`0.85`

. If we conduct a two-sided normal Z-test at a
significance level of `α = 0.05`

and achieve a power of 80%,
the total sample is `363`

.

Suppose that the expected sensitivity criteria of an new assay is
`0.85`

, and the lower 95% confidence interval of Wilson Score
at a significance level of `α = 0.05`

for criteria is
`0.8`

, the total sample is `246`

.

```
size_ci_one_prop(p = 0.85, lr = 0.8, alpha = 0.05, method = "wilson")
#>
#> Sample size determination for a Given Lower Confidence Interval
#>
#> Call: size_ci_one_prop(p = 0.85, lr = 0.8, alpha = 0.05, method = "wilson")
#>
#> optimal sample size: n = 246
#>
#> p:0.85 lr:0.8 alpha:0.05 interval:c(1, 1e+05) tol:1e-05 alternative:two.sided method:wilson
```

If we don’t want to use the CI of Wilson Score just following the
NMPA’s suggestion in the appendix, the CI of Simple-asymptotic is
recommended with the `196`

of sample size, as shown
below.

```
size_ci_one_prop(p = 0.85, lr = 0.8, alpha = 0.05, method = "simple-asymptotic")
#>
#> Sample size determination for a Given Lower Confidence Interval
#>
#> Call: size_ci_one_prop(p = 0.85, lr = 0.8, alpha = 0.05, method = "simple-asymptotic")
#>
#> optimal sample size: n = 196
#>
#> p:0.85 lr:0.8 alpha:0.05 interval:c(1, 1e+05) tol:1e-05 alternative:two.sided method:simple-asymptotic
```

Suppose that the expected correlation coefficient between test and
reference assays is `0.95`

, and the clinical acceptable
criteria is `0.9`

. If we conduct an one-sided test at a
significance level of `α = 0.025`

and achieve a power of 80%,
the total sample is `64`

.