# transformCIBP: Compute Confidence Intervals/Bands and P-values After a... In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

 transformCIBP R Documentation

## Compute Confidence Intervals/Bands and P-values After a Transformation

### Description

Compute confidence intervals/bands and p-values after a transformation

### Usage

```transformCIBP(
estimate,
se,
iid,
null,
conf.level,
alternative,
ci,
type,
min.value,
max.value,
band,
method.band,
n.sim,
seed,
p.value,
df = NULL
)
```

### Arguments

 `estimate` [numeric matrix] the estimate value before transformation. `se` [numeric matrix] the standard error before transformation. `iid` [numeric array] the iid decomposition before transformation. `null` [numeric] the value of the estimate (before transformation) under the null hypothesis. `conf.level` [numeric, 0-1] Level of confidence. `alternative` [character] a character string specifying the alternative hypothesis, must be one of `"two.sided"` (default), `"greater"` or `"less"`. `ci` [logical] should confidence intervals be computed. `type` [character] the transforamtion. Can be `"log"`, `"loglog"`, `"cloglog"`, or `"atanh"` (Fisher transform), or `"atanh2"` (modified Fisher transform for [0-1] variable). `min.value` [numeric] if not `NULL` and the lower bound of the confidence interval is below `min`, it will be set at `min`. `max.value` [numeric] if not `NULL` and the lower bound of the confidence interval is below `max`, it will be set at `max`. `band` [integer 0,1,2] When non-0, the confidence bands are computed for each contrasts (`band=1`) or over all contrasts (`band=2`). `method.band` [character] method used to adjust for multiple comparisons. Can be any element of `p.adjust.methods` (e.g. `"holm"`), `"maxT-integration"`, or `"maxT-simulation"`. `n.sim` [integer, >0] the number of simulations used to compute the quantiles for the confidence bands. `seed` [integer, >0] seed number set before performing simulations for the confidence bands. `p.value` [logical] should p-values and adjusted p-values be computed. Only active if `ci=TRUE` or `band>0`. `df` [integer, >0] optional. Degrees of freedom used for the student distribution of the test statistic. If not specified, use a normal distribution instead.

### Details

The iid decomposition must have dimensions [n.obs,time,n.prediction] while estimate and se must have dimensions [n.prediction,time].

Single step max adjustment for multiple comparisons, i.e. accounting for the correlation between the test statistics but not for the ordering of the tests, can be performed setting the arguemnt `method.band` to `"maxT-integration"` or `"maxT-simulation"`. The former uses numerical integration (`pmvnorm` and `qmvnorm` to perform the adjustment while the latter using simulation. Both assume that the test statistics are jointly normally distributed.

### Examples

```set.seed(10)
n <- 100
X <- rnorm(n)

res2sided <- transformCIBP(estimate = mean(X), se = cbind(sd(X)/sqrt(n)), null = 0,
type = "none", ci = TRUE, conf.level = 0.95, alternative = "two.sided",
min.value = NULL, max.value = NULL, band = FALSE,
p.value = TRUE, seed = 10, df = n-1)

resLess <- transformCIBP(estimate = mean(X), se = cbind(sd(X)/sqrt(n)), null = 0,
type = "none", ci = TRUE, conf.level = 0.95, alternative = "less",
min.value = NULL, max.value = NULL, band = FALSE,
p.value = TRUE, seed = 10, df = n-1)

resGreater <- transformCIBP(estimate = mean(X), se = cbind(sd(X)/sqrt(n)), null = 0,
type = "none", ci = TRUE, conf.level = 0.95, alternative = "greater",
min.value = NULL, max.value = NULL, band = FALSE,
p.value = TRUE, seed = 10, df = n-1)

## comparison with t-test
GS <- t.test(X, alternative = "two.sided")
res2sided\$p.value - GS\$p.value
unlist(res2sided[c("lower","upper")]) - GS\$conf.int

GS <- t.test(X, alternative = "less")
resLess\$p.value - GS\$p.value
unlist(resLess[c("lower","upper")]) - GS\$conf.int

GS <- t.test(X, alternative = "greater")
resGreater\$p.value - GS\$p.value
unlist(resGreater[c("lower","upper")]) - GS\$conf.int

```

riskRegression documentation built on March 23, 2022, 5:07 p.m.