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Transform methods have proved effective for networks describing a progression of events. In semi-Markov networks, we calculated the transform of time to a terminating event from corresponding transforms of intermediate steps. Saddlepoint inversion then provided survival and hazard functions, which integrated, and fully utilised, the network data. However, the presence of censored data introduces significant difficulties for these methods. Many participants in controlled trials commonly remain event-free at study completion, a consequence of the limited period of follow-up specified in the trial design. Transforms are not estimable using nonparametric methods in states with survival truncated by end-of-study censoring. We propose the use of parametric models specifying residual survival to next event. As a simple approach to extrapolation with competing alternative states, we imposed a proportional incidence (constant relative hazard) assumption beyond the range of study data. No proportional hazards assumptions are necessary for inferences concerning time to endpoint; indeed, estimation of survival and hazard functions can proceed in a single study arm. We demonstrate feasibility and efficiency of transform inversion in a large randomised controlled trial of cholesterol-lowering therapy, the Long-Term Intervention with Pravastatin in Ischaemic Disease study. Transform inversion integrates information available in components of multistate models: estimates of transition probabilities and empirical survival distributions. As a by-product, it provides some ability to forecast survival and hazard functions forward, beyond the time horizon of available follow-up. Functionals of survival and hazard functions provide inference, which proves sharper than that of log-rank and related methods for survival comparisons ignoring intermediate events., (Copyright © 2013 John Wiley & Sons, Ltd.)

Methods for combining measurements on multiple dimensions of quality of life can reduce the dimensionality of the data and increase the precision of estimation. When the dimensions are weighted according to their importance to patients, the resulting estimate is clinically useful and provides a step towards a true utility estimate. We derive two such weighting methods using linear regression on a measure of overall quality of life and demonstrate their usefulness in the analysis of quality of life data from two clinical trials of cancer therapies. Procedures for transforming the quality of life measures into utility measures are demonstrated., (Copyright 2001 John Wiley & Sons, Ltd.)

One way of examining trade-offs between quantity and quality of life (QOL) is to combine them into a single measure such as quality-adjusted life year (QALY). If censoring occurs, then estimation presents some difficulties. One approach, known as Q-TWiST, is to define a series of health states, use a 'partitioned' survival analysis to calculate the average time in each state, and then weight each state according to its quality of life to calculate QALYs. Such health-state models, however, are unhelpful when the transitions between health states are unclear or if they do not adequately reflect variations in quality of life. We therefore examine an alternative analysis to be used when repeated measures of quality of life are available from individual patients in a clinical trial. The method proceeds by separating quality of life and survival, that is, dQALY/dt = S(t)Q(t), where S(t) is the survival curve, estimated from the standard Kaplan-Meier method, and Q(t) is the quality of life function, derived from individual repeated measures of quality of life. We derive single health-state (QALY) and multiple health-state (Q-TWiST) models and illustrate the approach by comparing different durations of adjuvant chemotherapy for breast cancer.

Missing information on quality of life (QOL) is a significant problem in many cancer trials particularly for patients with advanced disease, where clinical deterioration may be a reason for not responding to quality of life assessments. Examples from four clinical trials are presented where non-respondents to quality of life assessments have poorer health than respondents. In this context, auxiliary outcome variables, such as health status, may be useful proxies in assessing the impact of missing QOL data on estimated treatment effects. This approach is illustrated in a trial of palliative treatment in advanced cancer. A method for imputation of missing QOL data based on auxiliary outcome variables is also illustrated. However, the most effective method of minimizing the problem of missing data is in designing the trial with preventative strategies in place. Since some missing data due to deteriorating health may still occur, the design should include the collection of auxiliary QOL information. Preventative strategies are illustrated with an ongoing trial in advanced breast cancer.

Common methods of treatment allocation for multi-centre and/or stratified randomized clinical trials can result in substantial differences between the number of patients allocated to each treatment arm. This can occur in the overall trial for a permuted block design or within individual institutions/strata when using a minimization scheme. This may lead to a bias in the result. Also, these procedures can be predictable, with the possibility of an investigator-introduced selection bias. An easily implemented method of randomization is proposed which attempts to overcome these problems by balancing treatment allocations both within strata and across the trial as a whole. The method keeps a running tally on total treatment allocation numbers at all stratification levels. When a patient accrues a hierarchical decision rule is applied, and the allocation is deterministic if certain pre-defined limits are exceeded, and random otherwise. The method is an extension of the big stick design of Soares and Wu, and is related to both Zelen's key number randomization methods and the schemes of Nordle and Brantmark. Simulation studies are used to demonstrate that major imbalances possible with other schemes do not occur using this method, and that the potential for selection bias is much reduced.

We present a technique, quality adjusted survival analysis, for the analysis of controlled trials where patients may experience several health states which differ in their quality of life. When the data are censored, a survival analysis of the quality adjusted life years achieved may involve informative censoring, and produce biased estimates. To overcome this, we partition the survival curve; the resulting areas, which represent the mean time in each state, are multiplied by utility weights to provide an unbiased estimate of (restricted) quality adjusted survival. If the appropriate weights are in doubt, the results are best presented as a threshold analysis over the utility weights, allowing individual recommendation to be read from a simple graph. The certainty of the conclusions can be presented as confidence bands on the threshold line. The techniques are illustrated with a re-analysis of a large three-arm trial of adjuvant chemoendocrine therapy for stage II breast cancer in postmenopausal women. This shows that if the value of time spent in toxicity is greater than the time spent in relapse, we can be 95 per cent confident that chemoendocrine therapy is the preferred option.

This paper explores the application of statistical decision theory to treatment choices in cancer which involve difficult value judgements in weighing beneficial and deleterious outcomes of treatment. Strengths and weaknesses of using decision theory are illustrated by considering the problem of selecting chemotherapy in advanced ovarian cancer. The paper includes an assessment of individual preferences in 27 volunteers and a discussion of some problems in utility assessment. An alternative approach, using threshold analysis, is presented in which the results of the decision analysis are expressed as a function of utility parameters. By knowing what sets of utilities favour each treatment, the assessment of patient preferences can then be focused on important differences of treatment options. The implications of these results for the design and analysis of clinical trials are discussed.

In evaluating therapies, clinical investigators often need to rely on the published clinical trial literature which may be biased in favour of studies with positive or 'encouraging' results and this may lead to erroneous conclusions of therapeutic effectiveness. The problem of publication bias can be magnified when the evaluation is based on a pooled analysis of clinical trial results, since in this case even small differences between treatment groups may reach statistical significance. In this paper a model is developed for pooling the results of clinical trials which is free from publication bias. It is proposed that an international registry of all clinical trials be established with the objectives and endpoints of each trial clearly defined in the register. In this way for each therapeutic issue researchers can select a cohort of clinical trials independently from the trial results. The approach is illustrated using the International Cancer Research Data Bank (ICRDB) registry of cancer clinical trials to evaluate the effect of chemotherapy on survival in advanced ovarian cancer. In this example, the conclusions based on a pooled analysis of registered trials have important differences from a more traditional review of the published trials. Implications of the results and problems in implementing the model are discussed.