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Survival analysis
Message posted by Jim Atienza on October 27, 1999 at 12:00 AM (ET)
I'm doing a study on the prognostic value of two diagnostic tests. According to the literature, one ensures a benign outcome in all but less than 1% of patients. Now I would like to know if a second test predicts at least a 15% higher incidence of adverse events. I have gathered two independent samples totaling 89. So far no one's had any adverse events yet. This means that all my data would be censored in a Kaplan-Meier analysis. Is there any other way to analyze my data?
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Re: Survival analysis
Message posted by JG on October 27, 1999 at 12:00 AM (ET)
If the adverse reaction is 1% then the probability is approximately 10% that in 89 patients there will be no adverse reaction. You need a bigger sample. Once adverse reactions start to appear, you should plot a cumulative estimate of the probability of an adverse reaction.
Re: Survival analysis
Message posted by Jim Atienza on October 27, 1999 at 12:00 AM (ET)
Thanks for the response. The problem is I don't have any more time to get more subjects. My sample size was based on how many subjects it would take to detect a 15% difference (16% - 1%) with a single-sided test with a level of significance of 0.05 and a power of 0.8.
Isn't there anything I can do with the data I already have?
Re: Survival analysis
Message posted by JG on October 28, 1999 at 12:00 AM (ET)
By doing some basic computations you can see that if you wish to distinguish between p=.01 and p=.16 , with alpha .05 and beta .2 then the sample size is 1. That is, if you have more than 1 observation and there are no occurances then accept p=.01 otherwise accept p=.16 .
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