Package 'clintools'

Description

calcrel() is a function used to calculate different reliability measures, including Coefficient of variance, smallest real difference, intraclass correlation coefficient, and Bland-Altman plot derived bias with 95% limits of agreement.

Usage

calcrel(d1,d2)

Arguments

Value

Returns a nested list of reliability measures.

Examples

d1 <- rnorm(15,10,1)
   d2 <- rnorm(15,10,1)
   calcrel(d1,d2)

Description

cdm.fig() is a function to assess any deviations

Usage

cdm.fig(df, col, site, meta_title, seedno, output, nmin)

Arguments

Value

Returns a full markdown output.

Examples

## Not run: 
   cdm.fig(df,col="Mode of birth",
      site="maternal_trial_site")
   cdm.fig(df,col="Gestational Age at birth",
      site="maternal_trial_site")
   library(knitr)
   kable(cdm.fig(df,col="Mode of birth",
      site="maternal_trial_site",output = ""),row.names=F)

## End(Not run)

Description

cdm.miss() is a small function which suppresses any output

Usage

cdm.miss(df, id, cols, fudate, lostFU, filter, blind)

Arguments

Value

Returns a full markdown output.

Examples

## Not run: 
   cdm.miss(data,id=idcols[[1]],cols=missing.cols,lostFU="lostFU",
   fudate = "follow_up_date", filter="missing")

## End(Not run)

Description

cdm.status() is a function to provide an overview of the trial status

Usage

cdm.status(d, sample.size, planned.years, rolling.average)

Arguments

Value

returns a list where ⁠$fig⁠ is the figure; ⁠$txt⁠ is as summary of the findings; and ⁠$df⁠is the dataframe generated in the function.

Examples

## Not run: 
   tmp <- cdm.status(d,sample.size=1808)

## End(Not run)

Description

clinmon() uses a continuous recording and returns a dataframe with hemodynamic indices for every period, epoch or block depending on the input. Calculates COest, CPPopt, CVRi, Dx, Mx, PI, PRx, PWA, RI, and Sx (see Hemodynamic indices).

Usage

clinmon(df, variables,
trigger = NULL, deleter = NULL,
blocksize = 3, epochsize = 20,
overlapping = FALSE, freq = 1000,
blockmin = 0.5, epochmin = 0.5,
output = "period", fast = FALSE)

Arguments

Details

Using a continuous raw recording, clinmon() calculates hemodynamic indices for every period, epoch or block depending on the chosen output.

View(data)

To calculate the indices insert the data and select the relevant variables.

clinmon(df=data, variables=c("abp","mcav"))

See Value for output description.

Value

Returns a dataframe with the results, with either every blocks, epochs or periods as rows, depending on the chosen output.

The columns of the output are:

• period - The period number corresponding to the row-number in the trigger file.

• epoch - The epoch number, or if period is chosen as output it reflects the number of epochs in the period.

• block - The block number, or if period or epoch is chosen as output it reflects the number of blocks in the period or epoch.

• time_min - The minimum time value or the period, epoch or block.

• time_max - The maximum time value or the period, epoch or block.

• missing_percent - The percentage of missing data in the period, epoch or block.

• XX_mean - The mean value of each variable for the period, epoch or block.

• XX_min - The minimum value of each variable for the period, epoch or block.

• XX_max - The maximum value of each variable for the period, epoch or block.

• YY - The indices in each column.

Hemodynamic indices

COest | Estimated cardiac output

Required variables: abp, hr; Required output: -.

Estimated cardiac output (COest) is calculated by utilizing the method described by Koenig et al. [1]:

$COest = PP / (SBP+DBP) * HR$

PP: Pulse pressure; SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate.

CPPopt | Optimal cerebral perfusion pressure

Required variables: abp, icp; Required output: period.

Optimal cerebral perfusion pressure (CPPopt) is calculated utilizing the method described by Steiner et al. [2]. The CPPopt return NA if CPPopt is the maximum or minimum CPP investigated. CPPopt is recommended to only be calculated after 'several hours' of recording:

$CPPopt = The 5 mmHg CPP Interval With Lowest Mean PRx$

CPP: cerebral perfusion pressure; PRx: Pressure reactivity index.

CVRi | Cardiovascular resistance index

Required variables: abp, mcav; Required output: -.

Cardiovascular resistance index (CVRi) is calculated utilizing the method described by Fan et al. [3]:

$CVRi = mean ABP / mean MCAv$

ABP: arterial blood pressure; MCAv: middle cerebral artery blood velocity.

Dx | Diastolic flow index

Required variables: cpp/abp, mcav; Required output: epoch, period.

Diastolic flow index (Dx) is calculated utilizing the method described by Reinhard et al. [4]:

$Dxc = cor( mean CPP / min MCAv )$

$Dxa = cor( mean ABP / min MCAv )$

cor: correlation coefficient; CPP: cerebral perfusion pressure; ABP: arterial blood pressure; MCAv: middle cerebral artery blood velocity.

Mx | Mean flow index

Required variables: cpp/abp, mcav; Required output: epoch, period.

Mean flow index (Mx) is calculated utilizing the method described by Czosnyka et al. [5]:

$Mxc = cor( mean CPP / mean MCAv )$

$Mxa = cor( mean ABP / mean MCAv )$

cor: correlation coefficient; CPP: cerebral perfusion pressure; ABP: arterial blood pressure; MCAv: middle cerebral artery blood velocity.

PI | Gosling index of pulsatility

Required variables: mcav; Required output: -.

Gosling index of pulsatility (PI) is calculated utilizing the method described by Michel et al. [6]:

$PI = (systolic MCAv - diastolic MCAv) / mean MCAv$

MCAv: middle cerebral artery blood velocity.

PRx | Pressure reactivity index

Required variables: abp, icp; Required output: epoch, period.

Pressure reactivity index (PRx) is calculated utilizing the method described by Czosnyka et al. [7]:

$PRx = cor( mean ABP / mean ICP )$

cor: correlation coefficient; CPP: cerebral perfusion pressure; ICP: intracranial pressure.

PWA | Pulse wave amplitude

Required variables: cpp/icp/abp/mcav; Required output: -.

Pulse wave amplitude (PWA) is calculated utilizing the method described by Norager et al. [8]:

$PWA = systolic - diastolic$

RI | Pourcelots resistive (resistance) index

Required variables: mcav; Required output: -.

Pourcelots resistive (resistance) index (RI) is calculated utilizing the method described by Forster et al. [9]:

$RI = (systolic MCAv - diastolic MCAv) / systolic MCAv$

MCAv: middle cerebral artery blood velocity.

Sx | Systolic flow index

Required variables: cpp/abp, mcav; Required output: epoch, period.

Systolic flow index (Sx) is calculated utilizing the method described by Czosnyka et al. [5]:

$Sxc = cor( mean CPP / systolic MCAv )$

$Sxa = cor( mean ABP / systolic MCAv )$

cor: correlation coefficient; CPP: cerebral perfusion pressure; ABP: arterial blood pressure; MCAv: middle cerebral artery blood velocity.

References

1. Koenig et al. (2015) Biomed Sci Instrum. 2015;51:85-90. (PubMed)

2. Steiner et al. (2002) Crit Care Med. 2002 Apr;30(4):733-8. (PubMed)

3. Fan et al. (2018) Front Physiol. 2018 Jul 16;9:869. (PubMed)

4. Reinhard et al. (2003) Stroke. 2003 Sep;34(9):2138-44. (PubMed)

5. Czosnyka et al. (1996) Stroke. 1996 Oct;27(10):1829-34. (PubMed)

6. Michel et al. (1998) Ultrasound Med Biol. 1998 May;24(4):597-9. (PubMed)

7. Czosnyka et al. (1997) Neurosurgery. 1997 Jul;41(1):11-7; discussion 17-9. (PubMed)

8. Norager et al. (2020) Acta Neurochir (Wien). 2020 Dec;162(12):2983-2989. (PubMed)

9. Forster et al. (2017) J Paediatr Child Health. 2018 Jan;54(1):61-68. (PubMed)

Examples

data(testdata)
clinmon(df.data10, variables=c('abp','mcav','hr'), freq=10)

Description

comparerel() is a function which compares measures from calcrel using bootstrapping.

Usage

comparerel(d1,d2,d3,d4,n_boot,seedno)

Arguments

Value

Returns a nested list of difference between reliability measures using bootstrapping.

Examples

## Not run: 
   d1 <- rnorm(15,10,1)
   d2 <- rnorm(15,10,1)
   d3 <- rnorm(15,10,1)
   d4 <- rnorm(15,10,1)
   comparerel(d1,d2,d3,d4)

## End(Not run)

Description

danishcpr() converts a list of CPR-numbers to a corresponding list birthday and sex.

Usage

danishcpr(code)

Arguments

Value

Returns a list with $birthday which is a list of dates, and $sex which is a list of Male/Female.

Examples

## Not run: 
   cpr <- danishcpr(code)
   birthdays <- cpr$birthday
   sex <- cpr$sex

## End(Not run)

Description

Recording with four columns: time (t), non-invasive arterial blood pressure (abp), middle cerebral artery velocity measured using transcranial Doppler (mcav), and heart rate (hr).

Usage

data(testdata)

Format

An object of class "dataframe"; an example of the usage in clinmon-function.

References

Olsen MH et al. (Unpublished data, 2020) (GitHub)

Examples

data(testdata)
variables <- c("abp","mcav","hr")
clinmon(df.data1000,variables,fast=50)

Description

Deleter dataframe with two columns: start (start) and end (end) of the deleter-period.

Usage

data(testdata)

Format

An object of class "dataframe"; an example of the usage in clinmon-function.

References

Olsen MH et al. (Unpublished data, 2020) (GitHub)

Examples

data(testdata)
variables <- c("abp","mcav","hr")
clinmon(df.data1000,variables,deleter=df.deleter,fast=50)

Description

dilations() is a function which converts longformat recording of pupillary size and uses markers to generate a dataframe, plot, and markdown output to inspect the results.

Usage

dilations(pupils, markers,
  remove_markers = NULL, add_markers = NULL,
  not_assess = NULL, artefacts_static = c(0.55,9.95),
  artefacts_dynamic = c(`1` = 1.5, `0.66` = 1, `0.33` = 0.5),
  time_assess = c(`1` = 10, `3` = 5),
  sig.level = 0.05, min_change = NULL,
  resting_delay = c(`3` = 0))

Arguments

Value

Returns a nested list one dataframe of the results ($dilations), plot ($plot$id[record id]), and a markdown output ($plot$markdown$id[record_id]). The dilations dataframe include the following columns: Record ID (record_id); Patient ID (pt_id); Date (date); pupil side (side); start of the period (min); end of the period (max); length of period (rec_length); number of measurements (n); median size (median); P value when comparing with the previous period (p_before); P value when comparing with the following period (p_after); and if dilation is identified (dilation, 1 is successful dilation and 0 is no dilation).

Examples

## Not run: 
   recordings <- PLR3000("C:/PLR3000/R_20200105_205901.xls")
   dilations <- dilations(recordings$pupils,recordings$markers)

   # The dataframe of the results
   dilations$dilations

   # The plot of one of the recordings
   dilations$plot$id833

   # The markdown output of one of the recordings
   dilations$markdown$id833

 
## End(Not run)

Description

iscus() is a function which converts XML files extracted from the Microdialysis-apparatur of ISCUSFlex apparatus to a dataframe.

Usage

iscus(filename)

Arguments

Value

Returns a dataframe with the measurements.

Examples

## Not run: 
   iscus("C:/ISCUSfiles/7888e844-1c7a-40af-a3f2-3bb27a8dd9e5.xml")
 
## End(Not run)

Description

ortable() is a small function which utilises the output from the glm-function to print a dataframe with odds ratio, confidence limits, and p-values.

Usage

ortable(x, d, d_p, intercept, simple)

Arguments

Value

Returns a dataframe with with odds ratio, confidence limits, and p-values.

Examples

df <- data.frame(outcome=sample(0:1, 100,replace=TRUE),
        var=sample(0:100,100,replace=TRUE))
ortable(glm(outcome ~ ., data=df))

Description

PLR3000() is a function which converts the XLS file imported from the eurOpticsTM PLR-3000 pupillometer to a nested list with two dataframes.

Usage

PLR3000(filename = NULL, df = NULL)

Arguments

Value

Returns a list with two dataframe, one with the measurements (pupils) and one with the markers (markers).

Examples

## Not run: 
   PLR3000("C:/PLR3000/R_20200105_205901.xls")
 
## End(Not run)

Description

questionaire() is a function which calculates scores from a questionaire.

Usage

questionaire(df,id,questions,scale,prefix,...)

Arguments

Details

PARCA-R (Parent Report of Children’s Abilities-Revised)

This only calculates the scores for the 34 Non-verbal cognitive score. The questionaire needs to have 34 questions, scale = "PARCA-R", and the following additional parameters need to be set:

• birthday | The name of the column for the birthday; if children are born before 37 weeks of gestatoin it is suggested to have corrected age; and then the column should be date of term.

• date | The name of the column for the date of when the participants went through the questionaire.

• sex | The name of the column for the sex of the participants. Male's must be coded as M and females as F.

The calculation summarizes both the raw score and the standard score. As per the PARCA-R manual from 2019 and from the protocol from SafeBoosC-III follow-up study Rasmussen et al. 2029 moderate-or-severe cognitive impairment is defined as < -2SD. This corresponds to a standard score below 70.

CBI (Copenhagen Burnout Inventory)

Using the 19 questions from the CBI it will both calculate score and group.

Using setting it can either be the English version (default; score = 'english') or the Danish version (score = 'danish'). In the English version the Likert-scale is converted to 100, 75, 50, 25, 0. Here, no more than 3 questions may be missing in each subscore English CBI.

The groups are based on the average score:

• 0-25: 'no burnout'

• 25-50: 'light burnout'

• 50-75: 'moderate burnout'

• 75-100: 'severe burnout'

In the Danish score the questions are summed up from 0 to 4 points. Here, all questions must be answered. Danish CBI

The groups are based on the summed score:

• 0-5: 'no burnout' (0-6 for Work-related burnout)

• 6-11: 'light burnout' (7-13 for Work-related burnout)

• 12-17: 'moderate burnout' (14-20 for Work-related burnout)

• 18+: 'severe burnout' (21+ for Work-related burnout)

KIDSCREEN-52

Works, but not documented

Value

Returns summarised information in dataframe.

Examples

## Not run: 

   df <- df_b[,grepl("ssid|KIDSCREEN52_D_",colnames(df_b))][,c(1:53)]
   k52 <- questionaire(df,id = "ssid",
   questions=colnames(df)[grepl("KIDSCREEN",colnames(df))],
   scale="Kidscreen-52",
   setting="proxy")#'

## End(Not run)

Description

quietly() is a small function which suppresses any output

Usage

quietly(x)

Arguments

Value

Returns x, but without any output

Examples

## Not run: 
tmp <- quietly(print("hello"))

## End(Not run)

Description

recol() is a small function which can rename columns and change factors to relevant input

Usage

recol(df, old, new, factors=NULL, remove=TRUE, na=NA)

Arguments

Value

Returns the dataframe with new columns.

Examples

## Not run: 
   # NUMERIC VARIABLE
   df <- recol(df, "X5d_55_Alcohol_127", "Alcohol")
   # FACTOR VARIABLE
   df <- recol(df, "X5d_55_Alcohol_127", "Alcohol", factors=c("No","Yes"))

## End(Not run)

Description

rowMedians() converts a dataframe to a list of row medians.

Usage

rowMedians(x, na.rm=FALSE)

Arguments

Value

Returns a list of the median of each row in the inputted dataframe.

Examples

## Not run: 
   rowMedians(df[,c("test1","test2","test3")])

## End(Not run)

Description

rrGcomp() is a small function which generates population-level (marginal) relative risks derived by G-computation. For models with random effects mixed-effects generalized linear model with a logit link with adjustment for stratification variables will be used, while those without random effects a logistic regression will be used. The code is based on the method used in the paper by Dankiewicz et al. (2021) N Engl J Med. Jun 17;384(24):2283-2294. (PubMed

Usage

rrGcomp(df, outcome_col, group_col,
fixed_strata = NULL, random_strata = NULL,
nbrIter = 5000, conf_level = 0.95)

Arguments

Value

Returns a list with relative risk (rr), simulated rr (simRR), lower- and upper confidence level (simLCL/simUCL), and the p-value (p_val)

Examples

df <- sRCT(n_sites=3,n_pop=50)
rrGcomp(df,outcome_col="outcome",group_col="Var1",random_strata="site",nbrIter=10)

Description

sRCT() is a function which simulates a randomised clinical trial with a binary outcome and returns a dataframe. This version is validated to be used for analysis of interaction in a factorial design.

Usage

sRCT(all_sizes = NULL, n_pop = 1000,
n_sites = 10, design = c(2,2,2),
rrr = c(0.05,0.05,0), outcome_risk = 0.492,
interaction = c(`1-2` = 0.05, `1-2-3` = -0.05),
site_re = 0.05)

Arguments

Details

The sRCT function is continuously being developed to answer specific questions in simulation studies. sRCT will be updated and tested for each specific question. For each update the function will be validated for the current purpose and all previous purposes. sRCT is not validated for all simulation studies

Value

Returns a dataframe with an individual participant data frame.

Examples

sRCT()

Description

tbl() is a function which create a dataframe, which can be copied directly into word or presented in as a summary table.

Usage

tbl(df,strata,vars,render.numeric,
render.factor, tests, test.vars, paired,
   digs_n,digs_f, digs_p, digs_s,
   only_stats, strata.fixed, strata.random,
   time.to, present.missing, conf.level,
   zeroonetoyn,
   markdown, caption)

Arguments

Value

Returns summarised information in dataframe.

Examples

## Not run: 
   hmm <- tbl(df,strata="group",
   vars = c("Gestational Age at birth","Maternal preeclampsia"),
   tests=c("wilcox.test","glm"),only_stats=F,strata.random = "site",
   markdown=F)
   pander::pander(hmm, keep.line.breaks = TRUE,split.tables=Inf, row.names = F)

## End(Not run)

Description

Recording with four columns: time (t), non-invasive arterial blood pressure (abp), middle cerebral artery velocity measured using transcranial Doppler (mcav), and heart rate (hr).

Usage

data(testdata)

Format

An object of class "dataframe"; an example of the usage in clinmon-function.

References

Olsen MH et al. (Unpublished data, 2020) (GitHub)

Examples

data(testdata)
variables <- c("abp","mcav","hr")
clinmon(df.data10,variables,freq=10)

Description

TFA() calculates dynamic cerebral autoregulation trough a transfer function analysis from a continuous recording. This function follows the recommendations from Claassen et al. [1] and mimicks the matlab script created by David Simpsons in 2015 (Matlab TFA function). TFA() also includes the possibility to analyse raw recordings with application of cyclic (beat-to-beat) average with the possiblity of utilizing interpolation. (see details).

Usage

TFA(df, variables,
trigger = NULL, deleter = NULL,
freq = 1000, fast = 50, raw_data = FALSE,
interpolation = 3, output = "table",
vlf = c(0.02,0.07),lf = c(0.07,0.2),
hf = c(0.2,0.5), detrend = FALSE,
spectral_smoothing = 3,
coherence2_thresholds = cbind(c(3:15),
c(0.51,0.40,0.34,0.29,0.25,0.22,0.20,0.18,
0.17,0.15,0.14,0.13,0.12)),
apply_coherence2_threshold = TRUE,
remove_negative_phase = TRUE,
remove_negative_phase_f_cutoff = 0.1,
normalize_ABP = FALSE,
normalize_CBFV = FALSE,
window_type = 'hanning',
window_length = 102.4,
overlap = 59.99,
overlap_adjust = TRUE,
na_as_mean = TRUE)

Arguments

Details

Using a continuous raw recording, TFA() calculates dynamic cerebral autoregulation trough a transfer function analysis. This function utilizes the recommendations from Claassen et al [1] and mimicks the matlab script created by David Simpsons in 2015.

View(data)

To calculate the variables insert the data and select the relevant variables.

TFA(df=data, variables=c("abp","mcav"))

See Value for output description.

Value

TFA() returns a dataframe depending on the output selected. 'table' results in a dataframe with values for the three frequencies defined by Claassen et al. [1]; 'long' results in a dataframe with the results in a long format; 'plot' results in a daframe which can help plot gain, phase and coherence; 'plot-peak' results in a dataframe, which can be used to validate the cyclic average, and 'raw' results in a nested list with results primarily for debugging.

Some generic variables are listed below:

• abp_power - The blood pressure power measured in mmHg^2.

• cbfv_power - The cerebral blood flow velocity power measured in cm^2\*s^-2

• coherence - Coherence.

• gain_not_normal - Not normalized gain measured in cm\*s^-1\*mmHg^-1.

• gain_normal - Normalized gain measured in %\*mmHg^-1.

• phase - Phase measured in radians.

output = 'table'

Wide format output table with period, VLF, LF, and HF as columns, and the TFA-variables as rows.

output = 'long'

Long format output table which can be manipulated depending on the intended use, with period, interval, variables and values as columns.

output = 'plot'

Plot format output table which can be used to draw figures with gain, phase and coherence depending on frequency.

TFA-paramters

A series of parameters that control TFA analysis (window-length, frequency bands …). If this is not provided, default values, corresponding to those recommended in the white paper, will be used. These default values are given below for each parameter.

• vlf Limits of very low frequency band (in Hz). This corresponds to the matematical inclusion of ⁠[X:Y[⁠. Default is c(0.02-0.07).

• lf Limits of low frequency band (in Hz). This corresponds to the matematical inclusion of ⁠[X:Y[⁠. Default is c(0.07-0.2).

• hf Limits of high frequency band (in Hz). This corresponds to the matematical inclusion of ⁠[X:Y[⁠. Default is c(0.2-0.5).

• detrend Linear detrending of data prior to TFA-analysis (detrending is carried out as one continuous trend over the whole length of the recording, not segment-by-segment). Default is FALSE.

• spectral_smoothing The length, in samples, of the triangular spectral smoothing function. Note that this must be an odd number, to ensure that smoothing is symmetrical around the centre frequency. Default is 3.

• coherence2_thresholds The critical values (alpha=5%, second column) for coherence for a number of windows (first column, here from 3 to 15). These values were obtained by Monte Carlo simulation, using the default parameter settings for the TFA-analysis (Hanning window, overlap of 50% and 3-point spectral smoothing was assumed). These values should be recalculated for different settings. Note that if overlap_adjust=TRUE, the overlap will vary depending on the length of data. With an overlap of 60% (see below), the critical values increase by between 0.04 (for 3 windows) and 0.02 (for 15 windows). Default is ⁠cbind(c(3:15),c(0.51,0.40,⁠ ⁠0.34,0.29,0.25,0.22,0.20,0.18,0.17,⁠ ⁠0.15,0.14,0.13,0.12))⁠.

• apply_coherence2_threshold Apply the thresholds given above to the TFA-estimates. All frequencies with magnitude-squared coherence below the threshold value are excluded from averaging when calculating the mean values of gain and phase across the bands. Note that low values of coherence are not excluded in the average of coherence across the bands. Default is TRUE.

• remove_negative_phase Remove (ignore) negative values of phase in averaging across bands. Negative phase values are removed only for frequencies below the frequency given below, when calculating the average phase in bands. Default is TRUE.

• remove_negative_phase_f_cutoff The cut-off frequency below-which negative phase values are neglected (only if remove_negative_phase is TRUE). Default is 0.1.

• normalize_ABP Normalize ABP by dividing by the mean and multiplying by 100, to express ABP change in %. Note that mean-values are always removed from ABP prior to analysis. Default is FALSE.

• normalize_CBFV Normalize CBFV by dividing by the mean and multiplying by 100, to express CBFV change in %. Note that the band-average values of gain are always calculated both with and without normalization of CBFV, in accordance with the recommendations. Note also that mean-values are always removed from CBFV prior to analysis. Default is FALSE.

• window_type Chose window 'hanning' or 'boxcar'. Default is 'hanning'.

• window_length Length of the data-window, in seconds. Default is 102.4.

• overlap Overlap of the windows, in %. If overlap_adjust is TRUE (see below), then this value may be automatically reduced, to ensure that windows cover the full length of data. Default is ⁠59.99%⁠ rather than 60%, so that with data corresponding to 5 windows of 100 s at an overlap of 50%, 5 windows are indeed chosen.

• overlap_adjust Ensure that the full length of data is used (i.e. the last window finishes as near as possible to the end of the recording), by adjusting the overlap up to a maximum value given by params.overlap. Default is TRUE.

• na_as_mean Changes all missing non-interpolated values to the mean value of the corresponding variable. This have not been adressed in the paper by Claassen, and to ensure the dataframes are not 'gathered' this should generate the most stable results. Default is TRUE.

References

1. Claassen et al. (2016) J Cereb Blood Flow Metab. 2016 Apr;36(4):665-80. (PubMed)

Examples

data(tfa_sample_data)
TFA(tfa_sample_data[,c(1:3)], variables=c("abp","mcav"), freq=10)

Description

Dataframe with data provided by Prof. Simpsons, with time (t), arterial blood pressure (abp), left MCAv (mcav_l), right MCAv (mcav_r), and end-tidal CO2 (etco2).

Usage

data(tfa_sample_data)

Format

An object of class "dataframe"; an example of the usage in TFA-function.

Source

GitHub

References

• Simpsons D (2015) (Cerebral Autoregulation Research Network)

• Claassen et al. (2016) J Cereb Blood Flow Metab. 2016 Apr;36(4):665-80. (PubMed)

Examples

data(tfa_sample_data)
TFA(tfa_sample_data[,c(1:3)], variables=c("abp","mcav"), freq=10)

Description

Dataframe with data provided by Prof. Simpsons, with time (t), arterial blood pressure (abp), left MCAv (mcav_l), right MCAv (mcav_r), and end-tidal CO2 (etco2).

Usage

data(tfa_sample_data)

Format

An object of class "dataframe"; an example of the usage in TFA-function.

Source

GitHub

References

• Simpsons D (2015) (Cerebral Autoregulation Research Network)

• Claassen et al. (2016) J Cereb Blood Flow Metab. 2016 Apr;36(4):665-80. (PubMed)

Examples

data(tfa_sample_data)
TFA(tfa_sample_data_1[,c(1:3)], variables=c("abp","mcav"), freq=10)

Description

Dataframe with data provided by Prof. Simpsons, with time (t), arterial blood pressure (abp), left MCAv (mcav_l), right MCAv (mcav_r), and end-tidal CO2 (etco2).

Usage

data(tfa_sample_data)

Format

An object of class "dataframe"; an example of the usage in TFA-function.

Source

GitHub

References

• Simpsons D (2015) (Cerebral Autoregulation Research Network)

• Claassen et al. (2016) J Cereb Blood Flow Metab. 2016 Apr;36(4):665-80. (PubMed)

Examples

data(tfa_sample_data)
TFA(tfa_sample_data_2[,c(1:3)], variables=c("abp","mcav"), freq=10)