GPC_cpp: C++ function performing the pairwise comparison over several...
In BuyseTest: Generalized Pairwise Comparisons

GPC_cpp

R Documentation

C++ function performing the pairwise comparison over several endpoints.

Description

GPC_cpp call for each endpoint and each strata the pairwise comparison function suited to the type of endpoint and store the results.

Usage

GPC_cpp(
  endpoint,
  status,
  indexC,
  posC,
  indexT,
  posT,
  threshold,
  restriction,
  weightEndpoint,
  weightObs,
  method,
  pool,
  op,
  D,
  D_UTTE,
  n_strata,
  nUTTE_analyzedPeron_M1,
  index_endpoint,
  index_status,
  index_UTTE,
  list_survTimeC,
  list_survTimeT,
  list_survJumpC,
  list_survJumpT,
  list_lastSurv,
  p_C,
  p_T,
  iid_survJumpC,
  iid_survJumpT,
  zeroPlus,
  correctionUninf,
  hierarchical,
  hprojection,
  neutralAsUninf,
  addHalfNeutral,
  keepScore,
  precompute,
  returnIID,
  debug
)

GPC2_cpp(
  endpoint,
  status,
  indexC,
  posC,
  indexT,
  posT,
  threshold,
  restriction,
  weightEndpoint,
  weightObs,
  method,
  pool,
  op,
  D,
  D_UTTE,
  n_strata,
  nUTTE_analyzedPeron_M1,
  index_endpoint,
  index_status,
  index_UTTE,
  list_survTimeC,
  list_survTimeT,
  list_survJumpC,
  list_survJumpT,
  list_lastSurv,
  p_C,
  p_T,
  iid_survJumpC,
  iid_survJumpT,
  zeroPlus,
  correctionUninf,
  hierarchical,
  hprojection,
  neutralAsUninf,
  addHalfNeutral,
  keepScore,
  precompute,
  returnIID,
  debug
)

Arguments

`endpoint`	A matrix containing the values of each endpoint (in columns) for each observation (in rows).
`status`	A matrix containing the values of the status variables relative to each endpoint (in columns) for each observation (in rows).
`indexC`	A list containing, for each strata, which rows of the endpoint and status matrices corresponds to the control observations. Not unique when bootstraping.
`posC`	A list containing, for each strata, the unique identifier of each control observations.
`indexT`	A list containing, for each strata, which rows of the endpoint and status matrices corresponds to the treatment observations. Not unique when bootstraping.
`posT`	A list containing, for each strata, the unique identifier of each treatment observations.
`threshold`	Store the thresholds associated to each endpoint. Must have length D. The threshold is ignored for binary endpoints.
`restriction`	Store the restriction time associated to each endpoint. Must have length D.
`weightEndpoint`	Store the weight associated to each endpoint. Must have length D.
`weightObs`	A vector containing the weight associated to each observation.
`method`	The index of the method used to score the pairs. Must have length D. 1 for binary/continuous, 2 for Gaussian, 3/4 for Gehan (left or right-censoring), and 5/6 for Peron (right-censoring survival or competing risks).
`pool`	The index of the method used to pool results across strata. Can be 0 (weight inversely proportional to the sample size), 1 (Mantel Haenszel weights), 2 (equal weights), 3 (precision weights)
`op`	The index of the operator used to score the pairs. Must have length D. 1 for larger is beter, -1 for smaller is better.
`D`	The number of endpoints.
`D_UTTE`	The number of distinct time to event endpoints.
`n_strata`	The number of strata.
`nUTTE_analyzedPeron_M1`	The number of unique time-to-event endpoints that have been analyzed the Peron scoring rule before the current endpoint. Must have length D.
`index_endpoint`	The position of the endpoint at each priority in the argument endpoint. Must have length D.
`index_status`	The position of the status at each priority in the argument status. Must have length D.
`index_UTTE`	The position, among all the unique tte endpoints, of the TTE endpoints. Equals -1 for non tte endpoints. Must have length n_TTE.
`list_survTimeC`	A list of matrix containing the survival estimates (-threshold, 0, +threshold ...) for each event of the control group (in rows).
`list_survTimeT`	A list of matrix containing the survival estimates (-threshold, 0, +threshold ...) for each event of the treatment group (in rows).
`list_survJumpC`	A list of matrix containing the survival estimates and survival jumps when the survival for the control arm jumps.
`list_survJumpT`	A list of matrix containing the survival estimates and survival jumps when the survival for the treatment arm jumps.
`list_lastSurv`	A list of matrix containing the last survival estimate in each strata (rows) and treatment group (columns).
`p_C`	Number of nuisance parameter in the survival model for the control group, for each endpoint and strata
`p_T`	Number of nuisance parameter in the survival model for the treatment group, for each endpoint and strata
`iid_survJumpC`	A list of matrix containing the iid of the survival estimates in the control group.
`iid_survJumpT`	A list of matrix containing the iid of the survival estimates in the treatment group.
`zeroPlus`	Value under which doubles are considered 0?
`correctionUninf`	Should the uninformative weight be re-distributed to favorable and unfavorable?
`hierarchical`	Should only the uninformative pairs be analyzed at the lower priority endpoints (hierarchical GPC)? Otherwise all pairs will be compaired for all endpoint (full GPC).
`hprojection`	Order of the H-projection used to compute the variance.
`neutralAsUninf`	Should paired classified as neutral be re-analyzed using endpoints of lower priority?
`addHalfNeutral`	Should half of the neutral score be added to the favorable and unfavorable scores?
`keepScore`	Should the result of each pairwise comparison be kept?
`precompute`	Have the integrals relative to the survival be already computed and stored in list_survTimeC/list_survTimeT and list_survJumpC/list_survJumpT (derivatives)
`returnIID`	Should the iid be computed? Second element: is there any nuisance parameter?
`debug`	Print messages tracing the execution of the function to help debugging. The amount of messages increase with the value of debug (0-5).

Details

GPC_cpp implements GPC looping first over endpoints and then over pairs. To handle multiple endpoints, it stores some of the results which can be memory demanding when considering large sample - especially when computing the iid decomposition. GPC2_cpp implements GPC looping first over pairs and then over endpoints. It has rather minimal memory requirement but does not handle correction for uninformative pairs.