Analysis of cause-specific failure endpoints using simple proportions: an example from a randomized controlled clinical trial in early breast cancer.
OBJECTIVE
To describe a statistically valid method for analyzing cause-specific failure data based on simple proportions, that is easy to understand and apply, and outline under what conditions its implementation is well-suited.


METHODS
In the comparison of treatment groups, time to first failure (in any site) was analyzed first, followed by an analysis of the pattern of first failure, preferably at the latest complete follow-up time common to each group.


RESULTS
A retrospective analysis of time to contralateral breast cancer in 777 early breast cancer patients was undertaken. Patients previously treated by mastectomy plus radiation therapy to the chest wall and regional nodal areas were randomized to receive further radiation and prednisone (R+P), radiation alone (R), or no further treatment (NT). Those randomized to R+P had a statistically significantly delayed time to first failure compared to the group randomized to NT (p = 0.0008). Patients randomized to R also experienced a delayed time to first failure compared to NT, but the difference was not statistically significant (p = 0.14). At 14 years from the date of surgery (the latest common complete follow-up time) the distribution of first failures was statistically significantly different between R+P and NT (p = 0.005), but not between R and NT (p = 0.09). The contralateral breast cancer first failure rate at 14 years from surgery was 7.2% for NT, 4.6% for R, and 3.7% for R+P. The corresponding Kaplan-Meier estimates were 13.2%, 8.2%, and 5.4%, respectively.


CONCLUSIONS
Analyzing cause-specific failure data using methods developed for survival endpoints is problematic. We encourage the use of the two-step analysis strategy described when, as in the example presented, competing causes of failure are not likely to be statistically independent, and when a treatment comparison at a single time-point is clinically relevant and feasible; that is, all patients have complete follow-up to this point.
Biostatistics Department, Princess Margaret Hospital, Toronto, Ontario, Canada.
T
T
Panzarella
J W
JW
Meakin
D000328:Adult; D000368:Aged; D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000401:mortality / Q000473:pathology / Q000532:radiotherapy* / Q000601:surgery*; D003131:Combined Modality Therapy; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008875:Middle Aged; D011241:Prednisone / Q000627:therapeutic use; D011879:Radiotherapy Dosage; D012189:Retrospective Studies; D013997:Time Factors; D017211:Treatment Failure
D018931:Antineoplastic Agents, Hormonal; D011241:Prednisone