In 2000, the CALGB 9011 trial demonstrated the apparent superiority of fludarabine over chlorambucil in previously untreated, symptomatic CLL. With a median follow-up of 5 years there was a higher overall response rate with fludarabine (63% vs 37%; P < .001), a longer median progression-free survival with fludarabine (20 vs 14 months; P < .001), but median overall survival was statistically similar with fludarabine vs chlorambucil (66 vs 56 months, respectively; P = .21).
At ASH 2009 a further 10 years follow-up was presented. Overall survival is now significantly significantly longer with fludarabine (63 months) than with chlorambucil (59 months) Or is it? Although the unadjusted p value was 0.04, after adjusted for co-variables (the fact that there were minor differences between the two groups) the p value was 0.07 - that is, not statistically significant or there was a 7% chance of such a result occuring by chance.
When one takes into consideration that the dose of chlorambucil used in this trial was 40 mg per sq m (as opposed to the 70 mg per sq m used in the Catovsky trial) one is rather driven to the conclusion that there is not much to choose between the two drugs.
In this trial there was another arm that combined fludarabine and chlorambucil that was terminated early because of toxicity. Long term follow-up of those receiving this arm demonstrated that 6/137 developed a myeloid malignancy (acute leukemia or MDS) compare to only 1 receiving fludarabine and zero receiving chlorambucil.
It would be nice to know about this years ago. I guess there is so much information out there about CLL some of the details just gets lost.
ReplyDeleteThank you for posting this information. I do have one question. In your remark "significantly longer with fludarabine(63 months)than with chlorambucil(59 months)" was that a typo or is 4 months considered significantly longer? Deb
ReplyDelete'Significant' means statistically significant not clinically significant. It means that there is less than a 1 in 20 chance that they are wrong about it. The adjustment for co-variables means that there are 7 chances in 100, or 1 in 14 that they have got it wrong. However, I agree, it's probably not worth the money.
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