NCA Docstrings

NCA.DoseLinearityPowerModelMethod
DoseLinearityPowerModel(nca::NCA.NCAReport, variable::Symbol; level = 0.95)

Run a power model to assess dose linearity of variable in NCAReport nca. The power model is of the form log(variable) ~ log(α) + β × log(dose).

The fitted parameters can be assessed by showing the model or running StatsBase.coeftable(model).

The confidence interval level can be set using level.

Example

DoseLinearityPowerModel(ncareport, :cmax; level = 0.9)
NCA.DoseLinearityRatioTestMethod
DoseLinearityRatioTest(nca::NCA.NCAReport, variable::Symbol; level = 0.95)

Run ratio tests to assess dose linearity for variable in NCAReport nca. The test runs HypothesisTests.EqualVarianceTTests on the log transform of variable. This effectively tests the difference of geometric means between two doses. The groups in each TTest are the low dose and one of each of the remaining dose groups. A confidence interval level for the difference of the means of the log transformed data can be set using level.

The result can be inspected by showing the returned object or using StatsBase.coeftable(model). The real dose ratio can be compared with the estimated ratio and its confidence interval, where these values are re-transformed from the TTest output using exp.

Example

DoseLinearityRatioTest(ncareport, :cmax; level = 0.9)
NCA.NCADoseType
NCADose(
  time,
  amt,
  duration::D = nothing,
  route = NullRoute,
  ii = zero(time),
  ss = false,
)

NCADose takes the following arguments

  • time: time of the dose
  • amt: The amount of dosage
  • duration: The infusion duration
  • route: Type of route, NCA.IVBolus, NCA.IVInfusion or NCA.EV
  • ii: interdose interval
  • ss: steady-state
NCA.NCASubjectMethod
NCASubject(
  conc,
  time;
  id = "1",
  group = nothing,
  dose = nothing,
  llq = nothing,
  nominal_time = nothing,
  start_time = nothing,
  end_time = nothing,
  volume = nothing,
  concu = true,
  timeu = true,
  volumeu = true,
  clean = true,
  check = true,
  num_blqs_df = 0,
  concblq = :keep,
  removed_inds = nothing,
  missingconc = nothing,
  missingvolume = nothing,
)

Construct a NCASubject.

Note that llq keyword argument still takes effects with the presence of the blq data column, and llq defaults to 0.

Setting clean=false disables all checks on conc and time to remove the cost of checking and cleaning data. It should only be used when the data is for sure "clean".

NCA.RouteType
Route

Type of routes. There are IVBolus and IVInfusion which are intravenous while EV is used for extravascular. The NullRoute value indicates that no route is specified.

NCA.accumulationindexMethod
accumulationindex(nca::NCASubject)

Theoretical accumulation ratio. $Accumulation\_index = 1/(1-\exp(-λ_z*τ))$.

NCA.aucMethod
auc(nca::NCASubject; auctype::Symbol, method::Symbol, interval=nothing, normalize=false)

Compute area under the curve (AUC) by linear trapezoidal rule (method = :linear) or by log-linear trapezoidal rule (method = :linuplogdown).

NCA.auclastMethod
auclast(nca::NCASubject; method::Symbol, interval=nothing)

Alias for auc(subj; auctype=:last).

NCA.auclinearMethod
auclinear(C₁, C₂, t₁, t₂)

Compute area under the curve (AUC) in an interval by linear trapezoidal rule.

NCA.auclogMethod
auclog(C₁, C₂, t₁, t₂)

Compute area under the curve (AUC) in an interval by log-linear trapezoidal rule.

NCA.auctauMethod
auctau(subj::NCASubject; method::Symbol)

Alias for auctau(subj; auctype=:last, interval=(zero(τ), τ)).

NCA.aumcMethod
aumc(nca::NCASubject; method::Symbol, interval=(0, Inf))

Compute area under the first moment of the concentration (AUMC) by linear trapezoidal rule (method = :linear) or by log-linear trapezoidal rule (method = :linuplogdown).

NCA.aumclastMethod
aumclast(nca::NCASubject; method::Symbol, interval=nothing)

Alias for aumc(subj; auctype=:last).

NCA.aumclinearMethod
aumclinear(C₁, C₂, t₁, t₂)

Compute area under the first moment of the concentration (AUMC) in an interval by linear trapezoidal rule.

NCA.aumclogMethod
aumclog(C₁, C₂, t₁, t₂)

Compute area under the first moment of the concentration (AUMC) in an interval by log-linear trapezoidal rule.

NCA.aumctauMethod
aumctau(subj::NCASubject; method::Symbol)

Alias for aumctau(subj; auctype=:last, interval=(zero(τ), τ)).

NCA.bioavMethod
bioav(nca::NCASubject; ithdose::Integer)

Compute the bioavailability as the ratio of two AUC values. $Bioavailability (F) = (AUC_{0,po}^\infty/Dose_{po})/(AUC_{0,iv}^\infty/Dose_{iv})$

NCA.c0Method
c0(nca::NCASubject)

Estimate the concentration at dosing time for an IV bolus dose.

NCA.cavgssMethod
cavgss(nca::NCASubject, method = :linear, pred=false)

Average concentration over one period. $Cavgss = AUC_{\tau}/\tau$. The keyword arguments are passed to the auc function and more information about them can be found in the auc documentation.

NCA.checkconctimeFunction
checkconctime(conc, time=nothing; monotonictime=true, dose=nothing)

Verify that the concentration and time are valid

If the concentrations or times are invalid, will provide an error. Reasons for being invalid are:

  1. conc is not a Number
  2. time is not a Number
  3. time value is missing
  4. time is not monotonically increasing
  5. conc and time are not of same length
  6. conc is empty.
  7. time is empty.

Some cases may generate warnings

  1. A negative concentration is often but not always an error; it will generate a warning.
NCA.clMethod
cl(nca::NCASubject, auctype=:inf, method = :linear, pred=false)

Calculate total drug clearance. The keyword arguments are passed to the auc function and more information about them can be found in the auc documentation.

NCA.clastMethod
clast(nca::NCASubject)

Calculate clast

NCA.cleanblqMethod
cleanblq(conc′, time′; llq=nothing, concblq=nothing)

Handle BLQ values in the concentration measurements as requested by the user.

missing concentrations (and their associated times) will be handled as described in cleanmissingconc before working with the BLQ values. The method for handling missing concentrations can affect the output of which points are considered BLQ and which are considered "middle". Values are considered BLQ if they are 0.

Arguments

  • conc: Measured concentrations

  • time: Time of the concentration measurement

  • concblq: How to handle a BLQ value that is between above LOQ values? See details for description.

  • cleanmissingconc: How to handle NA concentrations.

concblq can be set either a scalar indicating what should be done for all BLQ values or a list with elements named "first", "middle", and "last" each set to a scalar. If nothing, BLQ values will be dropped (:drop)

The meaning of each of the list elements is:

  1. first: Values up to the first non-BLQ value. Note that if all values are BLQ, this includes all values.
  2. middle: Values that are BLQ between the first and last non-BLQ values.
  3. last: Values that are BLQ after the last non-BLQ value

The valid settings for each are:

  1. "drop" Drop the BLQ values
  2. "keep" Keep the BLQ values
  3. a number Set the BLQ values to that number
NCA.cleanmissingconcMethod
cleanmissingconc(conc, time; missingconc=nothing)

Handle missing values in the concentration measurements as requested by the user.

missing concentrations (and their associated times) will be removed

Arguments

  • missingconc: How to handle missing concentrations? Either :drop or a number to impute.
NCA.cmaxMethod
cmax(nca::NCASubject; normalize=false, interval=nothing)

Calculate $Cmax_{t_0}^{t_1}$

NCA.cmaxssMethod
cmaxss(nca::NCASubject; normalize=false)

Calculate $Cmaxss$

NCA.cminMethod
cmin(nca::NCASubject; normalize=false, interval=nothing)

Calculate $Cmin_{t_0}^{t_1}$

NCA.cminssMethod
cminss(nca::NCASubject; normalize=false)

Calculate $Cminss$

NCA.cpredMethod
cpred(nca::NCASubject, t)

Predicted concentrations at any time t

NCA.ctauMethod
ctau(nca::NCASubject; method=:linear)

Calculate concentration at τ

NCA.extrapaucinfMethod
extrapaucinf(clast, tlast, lambdaz)

Extrapolate AUC to the infinite.

NCA.extrapaumcinfMethod
extrapaumcinf(clast, tlast, lambdaz)

Extrapolate the first moment to the infinite.

NCA.fluctuationMethod
fluctuation(nca::NCASubject; usetau=false)

Peak trough fluctuation over one dosing interval at steady state. $Fluctuation = 100*(Cmaxss - Cminss)/Cavgss$ (usetau=false) or $Fluctuation = 100*(Cmaxss - C_{tau})/Cavgss$ (usetau=true)

NCA.lambdazMethod
lambdaz(nca::NCASubject; concthreshold=1e-10, threshold=10, idxs=nothing) -> lambdaz

Calculate terminal elimination rate constant $λ_z$ with the end point greater than concthreshold.

NCA.lambdazadjr2Method
lambdazadjr2(nca::NCASubject)

Give the adjusted coefficient of determination ($adjr²$) when calculating $λ_z$.

See also lambdaz.

NCA.lambdazinterceptMethod
lambdazintercept(nca::NCASubject)

Give the y-intercept in the log-linear scale when calculating $λ_z$.

See also lambdaz.

NCA.lambdaznpointsMethod
lambdaznpoints(nca::NCASubject)

Give the number of points that is used in the $λ_z$ calculation.

See also lambdaz.

NCA.lambdazrMethod
lambdazr(nca::NCASubject)

Give the correlation coefficient ($r$) when calculating $λ_z$.

See also lambdaz.

NCA.lambdazr2Method
lambdazr2(nca::NCASubject)

Give the coefficient of determination ($r²$) when calculating $λ_z$.

See also lambdaz.

NCA.lambdaztimefirstMethod
lambdaztimefirst(nca::NCASubject)

Give the first time point that is used in the $λ_z$ calculation.

See also lambdaz.

NCA.lambdaztimelastMethod
lambdaztimelast(nca::NCASubject)

Give the last time point that is used in the $λ_z$ calculation.

See also lambdaz.

NCA.liftunits2headerMethod
liftunits2header(df::AbstractDataFrame)

Lift units to the header row.

Example:

julia> first(df, 3)
3×8 DataFrame
│ Row │ id    │ conc            │ time     │ ii       │ addl  │ occasion │ route  │ amt       │
│     │ Int64 │ Quantity…       │ Quantit… │ Quantit… │ Int64 │ Int64    │ String │ Quantity… │
├─────┼───────┼─────────────────┼──────────┼──────────┼───────┼──────────┼────────┼───────────┤
│ 1   │ 1     │ 157.021 mg L^-1 │ 0.0 hr   │ 10 hr    │ 4     │ 1        │ iv     │ 5000.0 mg │
│ 2   │ 1     │ 141.892 mg L^-1 │ 0.05 hr  │ 10 hr    │ 4     │ 1        │ iv     │ 0.0 mg    │
│ 3   │ 1     │ 116.228 mg L^-1 │ 0.35 hr  │ 10 hr    │ 4     │ 1        │ iv     │ 0.0 mg    │

julia> first(NCA.liftunits2header(df), 3)
3×8 DataFrame
│ Row │ id    │ conc (mg L^-1) │ time (hr) │ ii (hr) │ addl  │ occasion │ route  │ amt (mg) │
│     │ Int64 │ Float64        │ Float64   │ Int64   │ Int64 │ Int64    │ String │ Float64  │
├─────┼───────┼────────────────┼───────────┼─────────┼───────┼──────────┼────────┼──────────┤
│ 1   │ 1     │ 157.021        │ 0.0       │ 10      │ 4     │ 1        │ iv     │ 5000.0   │
│ 2   │ 1     │ 141.892        │ 0.05      │ 10      │ 4     │ 1        │ iv     │ 0.0      │
│ 3   │ 1     │ 116.228        │ 0.35      │ 10      │ 4     │ 1        │ iv     │ 0.0      │
NCA.mrtMethod
mrt(nca::NCASubject)

Mean residence time from the time of dosing to the time of the last measurable concentration.

IV infusion: $AUMC/AUC - TI/2$ where $TI$ is the length of infusion. non-infusion: $AUMC/AUC$

NCA.normalizedoseMethod
normalizedose(x::Number, d::NCADose)

Normalize x, a parameter of NCA, e.g., AUC, by dosing amount.

NCA.read_ncaFunction
read_nca(file::AbstractString; kwargs...)
read_nca(df_obs::AbstractDataFrame, df_dose::AbstractDataFrame; id = :id, time = :time, kwargs...)
read_nca(df::DataFrame; id=:id, time=:time, observations=:conc, nominal_time = :nominal_time,
                        start_time=:start_time, end_time=:end_time, volume=:volume,
                        amt=:amt, route=:route, duration=:duration, blq=:blq,
                        ii=:ii, ss=:ss, group=nothing, concu=true, timeu=true, amtu=true, volumeu=true,
                        verbose=true, sparse = false, kwargs...)

Parse a DataFrame object or a CSV file to NCAPopulation. NCAPopulation holds an array of NCASubjects which contain relevant data for the individual subjects.

Remark

Concentrations at dosing rows are NOT ignored in read_nca.

  • df : DataFrame containing the data for the analysis.

2 dataframes, in order, observations dataframe and dosing dataframe, can be passed to read_nca as well, rest of the arguments stay consistent in this case.

The following keyword arguments are used to specify column names in the df:

  • id : The numeric or string id of the subject. Defaults to :id.
  • time : The actual time at which the observations were measured. Defaults to :time.
  • observations: The observation (e.g. concentration) time series measurements. Values must be numbers or missing. Defaults to :conc.
  • amt: The amount of a dose. Can either be the dosing amount at each dosing time and otherwise missing or the dosing amount is present at each time, in this case the first time (for a subject in a subgroup) is considered as the dosing time. Defaults to :amt.
  • route: The route of administration. Possible choices are iv for intravenous, ev for extravascular, and inf for infusion. These can be specified as lower, upper or mixed case. E.g. iv, IV or Ev are accepted. Defaults to :route.
  • duration: The infusion duration. Should be the duration value or missing. Defaults to :duration.
  • blq: Below the lower Limit of Quantification (BLQ). Used to specify the observation is BLQ. The BLQ column can take a value of 1 for BLQ observation and 0 otherwise. Defaults to :blq.
  • ii: The interdose interval, equivalent to tau. Used to specify the interval length for steady-state dosing. Defaults to the :ii column. If specified, and ss is true, then analysis returns steady-state parameters e.g., cminss, cavgss, cmaxss by computing the accumulationindex.
  • ss: The steady-state. Used to specify whether a dose is steady-state, a steady-state dose takes the value 1 and 0 otherwise. It defaults to the :ss column. If ss is set to 1 for a subject, ii should be greater than 0.
  • group: The columns to group the data by, splits the subjects based on the group information associated with them. Defaults to no grouping.
  • llq: The Lower Limit of Quantification (LLQ). Defaults to nothing.
  • concblq: The scheme for handling of BLQ values. Defaults to the dictionary Dict(:first=>:keep, :middle=>:drop, :last=>:keep), further explanation is available in the Handling BLQ Data section.
  • concu: The units for observations (e.g. concentration). Defaults to no units.
  • amtu: The units for dosing amount. Defaults to no units.
  • timeu: The units for time. Defaults to no units.
  • volumeu: The units for volume. Defaults to no units.
  • verbose: When true, warnings will be thrown when the output does not match PumasNCADF. Defaults to true.
  • nominal_time: The nominal time corresponding to the observations. Defaults to :nominal_time.
  • sparse: Boolean flag to indicate if the dataset should be treated as a case of sparse sampling. Defaults to false.

Urine analysis requires the following columns not used in case of plasma.

  • start_time : The beginning of the urine collection time. Defaults to :start_time.
  • end_time : The end of the urine collection time. Defaults to :end_time.
  • volume: Collected urine volume. Defaults to :volume.

For details about the handling of concentration values below the lower limit of quantification, please check out the documentation of NCA.cleanblq. All the keyword arguments of NCA.cleanblq are applicable to read_nca, too.

NCA.run_ncaMethod
  run_nca(pop::NCAPopulation;
              parameters = nothing,
              studyid::String="",
              studytitle::String="",
              author=[],
              sponsor::String = "",
              date::DateTime=Dates.now(),
              conclabel::String="",
              timelabel::String="",
              grouplabels::Array=String[],
              groupsizes::NamedTuple=NamedTuple(),
              versionnumber::Union{String, VersionNumber}=v"0.1",
              sigdigits=nothing,
              )

The primary function in NCA.jl, to run NCA analysis on a population. Returns a NCAReport object that has a data frame of parameters computed in the analysis and other metadata information passed when run_nca is called.

NCA.spanMethod
span(nca::NCASubject)

Calculate span

NCA.summarizeMethod
  summarize(data::AbstractDataFrame;
              stratify_by::AbstractVector{<:Union{Symbol,Integer,AbstractString}} = Symbol[],
              parameters::AbstractVector{<:Union{Symbol,Integer,AbstractString}} = [:cmax, :tmax, :half_life, :aucinf_obs, :auclast, :cl, :vz],
              stats::AbstractVector{<:Function} = [NCA.numsamples, extrema, Statistics.mean, Statistics.std, StatsBase.geomean, NCA.geostd, NCA.geomeanCV]
              skipmissings = false
            )

Computes summary statistics specified by stats kwarg for parameters obtained from NCA analysis with run_nca. stratify_by allows you to group your computations. The skipmissings argument can be used to obtain summary statistics in presence of missing values.

NCA.superpositionMethod
superposition(data::Union{NCAPopulation,NCASubject}; ii, ndoses=5, amt=nothing, steadystatetol=3e-2, method=:linear)

Superposition calculation.

Arguments:

  • ii: interdose interval.
  • ndoses: number of doses. It can be a positive integer or Inf.
  • amt: dose amount. It defaults to the dose amount of the subject.
  • steadystatetol: steady state tolerance. Superposition computation terminates when $abs(1 - c(t-ii) / c(t)) <= steadystatetol$.
  • method: the method for interpolation. It defaults to :linear.
NCA.swingMethod
swing(nca::NCASubject; usetau=false)

$swing = (Cmaxss - Cminss)/Cminss$ (usetau=false) or $swing = (Cmaxss-C_τ)/C_τ$ (usetau=true)

NCA.tauMethod
tau(nca::NCASubject)

Dosing interval. For multiple dosing only.

NCA.thalfMethod
thalf(nca::NCASubject)

Calculate half life time.

NCA.timesfromstartMethod
timesfromstart(subj::NCASubject)

get a flat array with all the timepoints.

Ensures consistency between single-dose and multi-dose NCASubjects.

NCA.tlagMethod
tlag(nca::NCASubject)

The time prior to the first increase in concentration.

NCA.tlastMethod
tlast(nca::NCASubject)

Calculate tlast

NCA.tmaxMethod
tmax(nca::NCASubject; interval=nothing)

Calculate time of maximum observed concentration

NCA.tminMethod
tmin(nca::ncasubject)

Calculate time of minimum observed concentration

NCA.vssMethod
vss(nca::NCASubject, method = :linear, pred = false)

Calculate apparent volume of distribution at equilibrium for IV bolus doses. The keyword arguments are passed to the auc function and more information about them can be found in the auc documentation. $V_{ss} = MRT * CL$.

NCA.vzMethod
vz(nca::NCASubject, method = :linear, pred=false)

Calculate the volume of distribution during the terminal phase. $V_z = 1/(AUC⋅λ_z)$ for dose normalizedosed AUC. The keyword arguments are passed to the auc function and more information about them can be found in the auc documentation.