The development of oncology drugs progresses through multiple phases, where after each phase, a decision is made about whether to move a molecule forward. Early phase efficacy decisions are often made on the basis of single-arm studies based on a set of rules to define whether the tumor improves ("responds"), remains stable, or progresses (response evaluation criteria in solid tumors [RECIST]). These decision rules are implicitly assuming some form of surrogacy between tumor response and long-term endpoints like progression-free survival (PFS) or overall survival (OS). With the emergence of new therapies, for which the link between RECIST tumor response and long-term endpoints is either not accessible yet, or the link is weaker than with classical chemotherapies, tumor response-based rules may not be optimal. In this paper, we explore the use of a multistate model for decision-making based on singlearm early phase trials. The multistate model allows to account for more information than the simple RECIST response status, namely, the time to get to response, the duration of response, the PFS time, and time to death. We propose to base the decision on efficacy on the OS hazard ratio (HR) comparing historical control to data from the experimental treatment, with the latter predicted from a multistate model based on early phase data with limited survival follow-up. Using two case studies, we illustrate feasibility of the estimation of such an OS HR. We argue that, in the presence of limited follow-up and small sample size, and making realistic assumptions within the multistate model, the OS prediction is acceptable and may lead to better early decisions within the development of a drug. K E Y W O R D Sclinical trial, decision-making, multistate model

In this paper, we derive the joint distribution of progression-free and overall survival as a function of transition probabilities in a multistate model. No assumptions on copulae or latent event times are needed and the model is allowed to be non-Markov. From the joint distribution, statistics of interest can then be computed. As an example, we provide closed formulas and statistical inference for Pearson's correlation coefficient between progression-free and overall survival in a parametric framework. The example is inspired by recent approaches to quantify the dependence between progression-free survival, a common primary outcome in Phase 3 trials in oncology and overall survival. We complement these approaches by providing methods of statistical inference while at the same time working within a much more parsimonious modeling framework. Our approach is completely general and can be applied to other measures of dependence. We also discuss extensions to nonparametric inference.Our analytical results are illustrated using a large randomized clinical trial in breast cancer. KEYWORDS event history analysis, illness-death model, multistate model, randomized clinical trials INTRODUCTIONAn established way to evaluate performance of therapies in clinical trials is time-to-event endpoints. While overall survival (OS) remains the gold standard for demonstrating clinical benefit, especially in oncology, alternative endpoints such as progression-free survival (PFS), are also accepted by Health Authorities. 1,2 PFS is not only considered a surrogate for OS but may provide clinical benefit by itself, eg, by delaying symptoms or subsequent therapies. 3 Further advantages of the use of PFS are shorter trial durations and the fact that, as opposed to OS, PFS is not confounded by the use of later line, ie, post-progression therapies, and PFS is less vulnerable to competing causes of death than OS. 4 Methodology has been developed to assess surrogacy of one endpoint for another 5 and these methodologies have been applied to a wide range of indications. 6 One important aspect in the assessment of surrogacy is to quantify the correlation between the surrogate and the real endpoint. 7 This aspect has received quite some attention in the literature lately. 8,9 These two papers consider Pearson's correlation coefficient and rely on an illness-death model without recovery (IDM) to model the association between PFS and OS. To specify the underlying statistical model for the likelihood function for parametric estimation, they use a latent failure time approach. One characteristic of the latent failure time approach is that it allows for PFS events after OS, see Section 2.3. Within this model formulation, Fleischer et al 8 then derive closed formulas for the survival functions S PFS and S OS for PFS and OS as well as the correlation coefficient Corr (PFS, OS ) by assuming an exponential distribution for all transition intensities in the underlying multistate model. Li and Zhang 9 generalized these results to Weibull transition intensitie...

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The aim of this study is to review the practice and outcomes at our institution of percutaneous transhepatic placement of metallic biliary stents for non-hepato-biliary/pancreatic (non-HBP) malignant obstructive jaundice. A retrospective review was performed of the records of all patients undergoing transhepatic stenting for non-HBP malignant obstructive jaundice over a 7-year period. A total of 25 patients were successfully stented and linear regression analysis of a variety of demographic, clinical and laboratory markers against survival was performed. Survival after stenting varied from 1 to 1354 days (median 58, mean 152). An initial bilirubin level less than 300 micromol/L (P=0.01) and a reduction of greater than 50% in bilirubin post stenting (P=0.02) were strong predictors of improved survival. Older patients survived longer than younger ones (P<0.01). There was a weak association of survival with an albumin>30 g/L (P=0.06), but no statistically significant correlation with creatinine or haemoglobin levels or active tumour treatment after stenting. There were few major complications from the procedures. Transhepatic metallic biliary stenting for non-HBP malignant biliary obstruction is a safe and effective procedure, and with careful patient selection, significant periods of survival and palliation of jaundice can be achieved.

The influence of high-dose intravenous immunoglobulins (HD-IVIG) on the clinical status and T4 cell count of adults with AIDS-related complex (ARC) and Walter-Reed 5 (WR5) was evaluated in a randomized double-blind longitudinal study. Inclusion criteria were: (1) T4 cells less than 400/microliters and (2a) oral thrush or cutaneous anergy or (2b) two clinical ARC criteria (fever, diarrhea, weight loss, fatigue, night sweats). Thirty patients [28 males, 2 females, median age 41 (24-64) years] with ARC (n = 8), WR5 (n = 12) and both (n = 10) were stratified according to their T4 cell count (greater than or equal to vs. less than 300/microliters). Fifteen patients received 0.4 g/kg body weight IVIG and 15 placebo (albumin 0.03%) every other week for 26 weeks with follow-up for another 26 weeks. The clinical status was defined as a score consisting of fever, diarrhea, night sweats, fatigue, weight loss, oral candidiasis and mucosal or cutaneous herpes simplex. Clinical examination and routine laboratory assessments were performed before initiation of the study and before each administration, lymphocyte phenotyping every 4 weeks and cutaneous reaction, serology and lymphocyte stimulation every 12 weeks. Both groups were comparable in initial clinical symptoms and laboratory values. Seven patients developed AIDS (treatment group: 3, placebo group: 4), 1 patient died by homicide. After 26 weeks, the clinical score (particularly fatigue and fever) was significantly improved in the treatment group, while the T4 cell count and other clinical and immunological parameters remained unaltered. This limited effect was still evident at termination of the study after 52 weeks. In conclusion, HD-IVIG can improve the clinical status of patients with advanced HIV-1 infection without obviously correcting the underlying impaired cellular immunity. The substitution of intact antibodies in the state of functional hypogammaglobulinemia is suggested as possible therapeutic mechanism.