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- Very high hazard ratio from Proc Phreg

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01-20-2017 01:42 AM

I ran proc lfietest to generate KM curves using the following code:

proc lifetest data= dat;

time Survival*censor(1) ;

strata group /test=(logrank);

run;

Since I wanted the hazard ratios for this, I ran the following Proc Phreg code:

proc phreg data=dat;

class group;

model Survival*censor(1) =group ;

run;

The HR came out to be very high -a seven digit number (3797239) and not significant - pvalue=0.99. Can someone help me understand this?

Thanks so much!

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01-20-2017 02:19 AM

Can you post a PROC means on your survival time BY group and by censor. I suspect the proportionality assumption is violated for one thing, I think the ASSESS statement can be used to check this assumption.

If you could post the results that would be helpful.

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01-20-2017 10:55 AM

Thank you so much for helping with this. Any insight you could give with this is highly appreciated since I only have a couple of hours to deliver these HRs

Here are the Proc means: (I will be checking assess shortly)

The MEANS Procedure | ||||

Group=No | ||||

Analysis Variable : Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

107 | 756.79 | 355.51 | 9 | 1246 |

Group=Yes | ||||

Analysis Variable : Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

35 | 868.8 | 242.74 | 250 | 1141 |

The MEANS Procedure | ||||

Censoring variable =No | ||||

Analysis Variable : Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

16 | 406.88 | 158.85 | 196 | 701 |

Censoring variable =Yes | ||||

Analysis Variable : Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

126 | 832.33 | 319.79 | 9 | 1246 |

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01-20-2017 12:38 PM

You only have 16 uncensored events? That's a small rate which is contributing to the issue. Especially when you then break it down into groups as well.

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01-20-2017 12:44 PM

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01-20-2017 01:11 PM

It appears the Proportionality assumption required for a Cox regression is violated. This is compounded by the fact that you have very low event rates and really not a lot of observations. Therefore estimates from this data would have a large degree of uncertainty around them.

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01-20-2017 01:47 PM

Thank you so much for the help. I will update later on running assess statement

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01-20-2017 03:05 PM

Such big estimates only occur when the model doesnt converge. I think that there is no uncensored events in one of the two groups. It can even occur if there is uncensored events in both groups, but only if all uncensored events in one group comes when all persons in the other group are no longer in risk.

Try make the proc means again with censoring and group at the same time in the by statement.

I dont think it has to do with the the assumption of the proportionality statement.

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01-20-2017 03:27 PM

Thanks for your comments. Here are the mean measures:

The MEANS Procedure | ||||

Censoring variable=No Group=No | ||||

Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

11 | 396.73 | 172.71 | 196 | 701 |

Censoring variable =No Group=Yes | ||||

Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

5 | 429.2 | 138.35 | 250 | 578 |

Censoring variable =Yes Group=No | ||||

Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

96 | 798.04 | 348.07 | 9 | 1246 |

Censoring variable =Yes Group=Yes | ||||

Survival Time [Days] | ||||

N | Mean | Std Dev | Minimum | Maximum |

30 | 942.07 | 166.05 | 294 | 1141 |

As for the convergence, I don't get any errors/warnings. It says "convergence criteria satisfied"...

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01-20-2017 05:46 PM

It seems strange that the hazardratio should be that extreme. Can you attach the dataset? I would like to take a look on it.