monitor_long_surv function

Monitor Longitudinal Data for Survival Outcomes

monitor_long_surv(
  data_array_new,
  time_matrix_new,
  nobs_new,
  pattern,
  method,
  parameter = 0.5,
  CL = Inf
)

Arguments

• data_array_new: observed data arranged in a numeric array format.

  data_array_new[i,j,k] is the jth observation of the kth dimension of the ith subject.

• time_matrix_new: observation times arranged in a numeric matrix format.

  time_matrix_new[i,j] is the jth observation time of the ith subject.

  data_array_new[i,j,] is observed at time_matrix[i,j].

• nobs_new: number of observations arranged as an integer vector.

  nobs_new[i] is the number of observations for the ith subject.

• pattern: the estimated longitudinal and survival pattern from estimate_pattern_long_surv.

• method: a character value specifying the smoothing method

  If method="risk", apply the risk monitoring method by You and Qiu (2020).

• parameter: a numeric value.

  The weighting parameter in the modified EWMA charts.

• CL: a numeric value specifying the control limit

Returns

a list that stores the result.

• $chart: charting statistics arranged in a matrix.

• $standardized_values: standardized values arranged in a matrix.

Examples

data("data_example_long_surv")

result_pattern<-estimate_pattern_long_surv(
  data_array=data_example_long_surv$data_array_IC,
  time_matrix=data_example_long_surv$time_matrix_IC,
  nobs=data_example_long_surv$nobs_IC,
  starttime=data_example_long_surv$starttime_IC,
  survtime=data_example_long_surv$survtime_IC,
  survevent=data_example_long_surv$survevent_IC,
  design_interval=data_example_long_surv$design_interval,
  n_time_units=data_example_long_surv$n_time_units,
  estimation_method="risk",
  smoothing_method="local linear",
  bw_beta=0.05,
  bw_mean=0.1,
  bw_var=0.1)

result_monitoring<-monitor_long_surv(
  data_array_new=data_example_long_surv$data_array_OC,
  time_matrix_new=data_example_long_surv$time_matrix_OC,
  nobs_new=data_example_long_surv$nobs_OC,
  pattern=result_pattern,
  method="risk",
  parameter=0.5)

References

You, L. and Qiu, P. (2020). An effective method for online disease risk monitoring. Technometrics, 62(2):249-264.