# Cluster quality evaluation using follow-up data

### Description

Function to evaluate the overall quality of a given partition using follow-up data. A partition (clustering) is composed of non-overlapping clusters.

### Usage

1 | ```
surv_measure(parti, surv.time, status, method = "BIC")
``` |

### Arguments

`parti` |
A partition to be evaluated. |

`surv.time` |
A numeric vector contains follow-up time of patient's in |

`status` |
A binary vector contains survival status of patients in |

`method` |
Type of partition evaluation measures to use for assessing the relationship between follow-up and a partition. Default is |

### Details

This function fits a Cox model using follow-up data as response and cluster labels in the partition as covariate. The likelihood from the fitted model further used to calculate the modified *AIC* or *BIC*. See references for more details. Note that, for convenience in later usage, returned value is multiplied by -1 inside the function so that large value denotes good quality partition.

### Value

A numeric value representing the quality of partition under consideration in terms of follow-up.

### Author(s)

Askar Obulkasim

### References

Liang,H. and Zou,G.H. (2008). "Improved AIC selection strategy for survival analysis", *Comput Stat Anal*., 52, 2538-2548.

Volinsky,T.C. and Raftery,A.E. (2000). "Baysian information criteria for censored survival models", *Biometrics*, 56, 256-262.

Obulkasim,A. et al., (2013). "Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree", submitted.

### See Also

`measure`

### Examples

1 2 3 4 5 | ```
data(BullingerLeukemia)
attach(BullingerLeukemia)
cl <- HCsnipper(em[, 1:30], min = 5)
cl <- cl$partitions[cl$id, ]
result <- apply(cl, 1, function(x) surv_measure(x, surv.time[1:30], status[1:30]))
``` |