Description Usage Arguments Details Value References Examples
simjm
simulates data from a Cox proportional hazards or semi-parametric additive model with
two time-fixed covariates (Z1 and Z2) and three time-dependent covariates (Yij_1,Yij_2,Yij_3).
The user can specify various characteristics of these distributions.
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n |
Size of dataset to be generated |
surv_model |
Model for the time-to-event. Options are "Cox" for Cox proportional hazard model and "Add" for semi-parametric additive hazards model. |
marker_model |
Model for the multiple markers. Options are "RE" for the correlated random effects model and "PN" for the product normal model. |
MErr |
Degree of measurement error in the multiple markers. Options are "Low", "Mod" and "High". |
Miss |
Degree of missing at-risk measurements. Options are "None", "Low" and "High". |
effects |
Strength of effects of the markers on the hazard. Options are "Null", "Weak" and "Strong". |
corr |
Degree of marginal pairwise correlations between the markers. Options are "Low", "Mod" and "High". |
The function can be used to generate data from any of the scenarios considered in the the main simulation settings of Moreno-Betancur et al. (2017). See that reference for details.
A data.frame as required by survtd
. That is, in the long format, with one row per individual and
per visit time at which any of the time-dependent covariates were measured, with the corresponding measurements. The dataset also
includes a variable that uniquely identifies observations from the same individual; a variable that indicates the timing
of each measurement visit; and the fixed variables (time-to-event, event indicator, time-fixed covariates) which are constant across
rows of the same individual. The final columns of the dataset (from Xij_1
onwards) are to recover the true values
of the markers as per the data generation model for use with function simjm_benchmark
.
Specifically, the variables in the dataset are:
Unique identifier of observations from the same individual.
Time to event, possibly right-censored.
Indicator of event, with event=1 if an event occurred at tt and event=0 if the individual is censored.
Time-fixed binary covariate.
Time-fixed continuous covariate.
Timing of the measurement visit.
Measured value of marker 1 at time tj
Measured value of marker 2 at time tj
Measured value of marker 3 at time tj
True value of marker 1 at time tj
True value of marker 2 at time tj
True value of marker 3 at time tj
Time-fixed part of the linear predictor of the linear mixed model from which Yij_1 is generated.
Time-dependent part of the linear predictor of the linear mixed model from which Yij_1 is generated, excluding terms for other markers in the case of product-normal model.
Time-fixed part of the linear predictor of the linear mixed model from which Yij_2 is generated.
Time-dependent part of the linear predictor of the linear mixed model from which Yij_2 is generated, excluding terms for other markers in the case of product-normal model.
Time-fixed part of the linear predictor of the linear mixed model from which Yij_3 is generated.
Time-dependent part of the linear predictor of the linear mixed model from which Yij_3 is generated, excluding terms for other markers in the case of product-normal model.
Moreno-Betancur M, Carlin JB, Brilleman SL, Tanamas S, Peeters A, Wolfe R (2017). Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM). Biostatistics [Epub ahead of print 12 Oct 2017].
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