Crossover in Adjuvant Trastuzumab Trials: Sparing Toxicity in Patient Care.

OBJECTIVES: Design and reporting of randomized control trials for drug therapies in the adjuvant setting require a nuanced consideration of patient crossover. Adjuvant trials can be susceptible to the misuse of crossover and may distort the interpretation of findings. We sought to investigate and describe crossover and/or postprogression access to trastuzumab within adjuvant trastuzumab randomized control trials for human epidermal growth factor receptor 2-positive breast cancer patients. METHODS: Seven clinical trials for adjuvant trastuzumab in human epidermal growth factor receptor 2-positive breast cancer were identified through a meta-analysis published in the Lancet . Primary study publications were located through MEDLINE, Google Scholar, and trials were identified, when possible, using Clincialtrials.gov. RESULTS: Sixteen publications, describing 7 studies, were reviewed. Four (57%) trials reported offering patients within the control arm the opportunity to crossover and receive trastuzumab in the adjuvant setting. Two (29%) trials did not report nor discuss crossover within the publication. Five (71%) trials reported the total number of patients who crossed over among the control arms. No trials specified the proportion of control patients who received trastuzumab at recurrence. CONCLUSIONS: Trials for adjuvant trastuzumab did not disambiguate between crossover (1) in the adjuvant setting or (2) at recurrence. Due to the low reported rate of crossover, it is questionable if participants received the standard of care.

Retrospective comparative effectiveness research: Will changing the analytical methods change the results?

In medicine, retrospective cohort studies are used to compare treatments to one another. We hypothesize that the outcomes of retrospective comparative effectiveness research studies can be heavily influenced by biostatistical analytic choices, thereby leading to inconsistent conclusions. We selected a clinical scenario currently under investigation: survival in metastatic prostate, breast or lung cancer after systemic vs systemic + definitive local therapy. We ran >300 000 regression models (each representing a publishable study). Each model had various forms of analytic choices (to account for bias): propensity score matching, left truncation adjustment, landmark analysis and covariate combinations. There were 72 549 lung, 14 904 prostate and 13 857 breast cancer patients included. In the most basic analysis, which omitted propensity score matching, left truncation adjustment and landmark analysis, all of the HRs were <1 (generally, 0.60-0.95, favoring addition of local therapy), with all P-values <.001. Left truncation adjustment landmark analysis produced results with nonsignificant P-values. The combination of propensity score matching, left truncation adjustment, landmark analysis and covariate combinations generally produced P-values that were >.05 and/or HRs that were >1 (favoring systemic therapy alone). The use of more statistical methods to reduce the selection bias caused reported HR ranges to approach 1.0. By varying analytic choices in comparative effectiveness research, we generated contrary outcomes. Our results suggest that some retrospective observational studies may find a treatment improves outcomes for patients, while another similar study may find it does not, simply based on analytical choices.