Patient selection for oncology phase I trials: a multi-institutional study of prognostic factors.

PURPOSE The appropriate selection of patients for early clinical trials presents a major challenge. Previous analyses focusing on this problem were limited by small size and by interpractice heterogeneity. This study aims to define prognostic factors to guide risk-benefit assessments by using a large patient database from multiple phase I trials. PATIENTS AND METHODS Data were collected from 2,182 eligible patients treated in phase I trials between 2005 and 2007 in 14 European institutions. We derived and validated independent prognostic factors for 90-day mortality by using multivariate logistic regression analysis. Results The 90-day mortality was 16.5% with a drug-related death rate of 0.4%. Trial discontinuation within 3 weeks occurred in 14% of patients primarily because of disease progression. Eight different prognostic variables for 90-day mortality were validated: performance status (PS), albumin, lactate dehydrogenase, alkaline phosphatase, number of metastatic sites, clinical tumor growth rate, lymphocytes, and WBC. Two different models of prognostic scores for 90-day mortality were generated by using these factors, including or excluding PS; both achieved specificities of more than 85% and sensitivities of approximately 50% when using a score cutoff of 5 or higher. These models were not superior to the previously published Royal Marsden Hospital score in their ability to predict 90-day mortality. CONCLUSION Patient selection using any of these prognostic scores will reduce non-drug-related 90-day mortality among patients enrolled in phase I trials by 50%. However, this can be achieved only by an overall reduction in recruitment to phase I studies of 20%, more than half of whom would in fact have survived beyond 90 days.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials

Background: As trials of 5 years of tamoxifen in early breast cancer mature, the relevance of hormone receptor measurements (and other patient characteristics) to long-term outcome can be assessed increasingly reliably. We report updated meta-analyses of the trials of 5 years of adjuvant tamoxifen.
Methods: We undertook a collaborative meta-analysis of individual patient data from 20 trials (n=21457) in early breast cancer of about 5 years of tamoxifen versus no adjuvant tamoxifen, with about 80% compliance. Recurrence and death rate ratios (RRs) were from log-rank analyses by allocated treatment.
Findings: In oestrogen receptor (ER)-positive disease (n=10 645), allocation to about 5 years of tamoxifen substantially reduced recurrence rates throughout the first 10 years (RR 0.53 [SE 0.03] during years 0-4 and RR 0.68 [0.06] during years 5-9 [both 2p<0.00001]; but RR 0.97 [0.10] during years 10-14, suggesting no further gain or loss after year 10). Even in marginally ER-positive disease (10-19 fmol/mg cytosol protein) the recurrence reduction was substantial (RR 0.67 [0.08]). In ER-positive disease, the RR was approximately independent of progesterone receptor status (or level), age, nodal status, or use of chemotherapy. Breast cancer mortality was reduced by about a third throughout the first 15 years (RR 0.71 [0.05] during years 0-4, 0.66 [0.05] during years 5-9, and 0.68 [0.08] during years 10-14; p<0.0001 for extra mortality reduction during each separate time period). Overall non-breast-cancer mortality was little affected, despite small absolute increases in thromboembolic and uterine cancer mortality (both only in women older than 55 years), so all-cause mortality was substantially reduced. In ER-negative disease, tamoxifen had little or no effect on breast cancer recurrence or mortality.
Interpretation: 5 years of adjuvant tamoxifen safely reduces 15-year risks of breast cancer recurrence and death. ER status was the only recorded factor importantly predictive of the proportional reductions. Hence, the absolute risk reductions produced by tamoxifen depend on the absolute breast cancer risks (after any chemotherapy) without tamoxifen.
Funding: Cancer Research UK, British Heart Foundation, and Medical Research Council.