A simple two-stage design for quantitative responses with application to a study in diabetic neuropathic pain

Two-stage designs offer substantial advantages for early phase II studies. The interim analysis following the first stage allows the study to be stopped for futility, or more positively, it might lead to early progression to the trials needed for late phase II and phase III. If the study is to continue to its second stage, then there is an opportunity for a revision of the total sample size. Two-stage designs have been implemented widely in oncology studies in which there is a single treatment arm and patient responses are binary. In this paper the case of two-arm comparative studies in which responses are quantitative is considered. This setting is common in therapeutic areas other than oncology. It will be assumed that observations are normally distributed, but that there is some doubt concerning their standard deviation, motivating the need for sample size review. The work reported has been motivated by a study in diabetic neuropathic pain, and the development of the design for that trial is described in detail.     

Models for the analysis of C-reactive protein in statin trials

Elevation in C-reactive protein (CRP) is an independent risk factor for cardiovascular disease progression and levels are reduced by treatment with statins. However, on-treatment CRP, given baseline CRP and treatment, is not normally distributed and outliers exist even when transformations are applied. Although classical non-parametric tests address some of these issues, they do not enable straightforward inclusion of covariate information. The aims of this study were to produce a model that improved efficiency and accuracy of analysis of CRP data. Estimation of treatment effects and identification of outliers were addressed using controlled trials of rosuvastatin. The robust statistical technique of MM-estimation was used to fit models to data in the presence of outliers and was compared with least-squares estimation. To develop the model, appropriate transformations of the response and baseline variables were selected. The model was used to investigate how on-treatment CRP related to baseline CRP and estimated treatment effects with rosuvastatin. On comparing least-squares and MM-estimation, MM-estimation was superior to least-squares estimation in that parameter estimates were more efficient and outliers were clearly identified. Relative reductions in CRP were higher at higher baseline CRP levels. There was also evidence of a dose-response relationship between CRP reductions from baseline and rosuvastatin. Several large outliers were identified, although there did not appear to be any relationships between the incidence of outliers and treatments. In conclusion, using robust estimation to model CRP data is superior to least-squares estimation and non-parametric tests in terms of efficiency, outlier identification and the ability to include covariate information.     

Missing item imputation for quality-of-life instruments with application to asthma quality-of-life questionnaires

There has been increasing use of quality-of-life (QoL) instruments in drug development. Missing item values often occur in QoL data. A common approach to solve this problem is to impute the missing values before scoring. Several imputation procedures, such as imputing with the most correlated item and imputing with a row/column model or an item response model, have been proposed. We examine these procedures using data from two clinical trials, in which the original asthma quality-of-life questionnaire (AQLQ) and the miniAQLQ were used. We propose two modifications to existing procedures: truncating the imputed values to eliminate outliers and using the proportional odds model as the item response model for imputation. We also propose a novel imputation method based on a semi-parametric beta regression so that the imputed value is always in the correct range and illustrate how this approach can easily be implemented in commonly used statistical software. To compare these approaches, we deleted 5% of item values in the data according to three different missingness mechanisms, imputed them using these approaches and compared the imputed values with the true values. Our comparison showed that the row/column-model-based imputation with truncation generally performed better, whereas our new approach had better performance under a number scenarios.     

古印度哲学的信息、系统、复杂性思想的基本特质(上)

古印度哲学中蕴涵着丰富而深刻的信息、系统与复杂性思想,可以归纳为十二个方面的特质:丰富的宇宙及其事物自生性的过程论思想;以大梵为本体的世界整体统一性理论;形形色色的多元实在论的本体论观念;明确的信息自在显现思想;丰富的宇宙、事物的全息思想;深刻的信息认识中介论思想;差异整体论和差异认识论的复杂性观念;和合论中的整体涌现论的复杂性思想;事物自然因、无目的、非决定论的复杂性观念;无常无我论、空论中的变易、无主宰、无主体的复杂性思想;"中道论"中两极兼容、对立互补的复杂性思想;具有直观性、猜测性、神秘性、神学性等缺陷.     

Active‐controlled,non‐inferiority trials in oncology: arbitrary limits,infeasible sample sizes and uninformative data analysis. Is there another way?

In oncology, it may not always be possible to evaluate the efficacy of new medicines in placebo-controlled trials. Furthermore, while some newer, biologically targeted anti-cancer treatments may be expected to deliver therapeutic benefit in terms of better tolerability or improved symptom control, they may not always be expected to provide increased efficacy relative to existing therapies. This naturally leads to the use of active-control, non-inferiority trials to evaluate such treatments. In recent evaluations of anti-cancer treatments, the non-inferiority margin has often been defined in terms of demonstrating that at least 50% of the active control effect has been retained by the new drug using methods such as those described by Rothmann et al., Statistics in Medicine 2003; 22:239-264 and Wang and Hung Controlled Clinical Trials 2003; 24:147-155. However, this approach can lead to prohibitively large clinical trials and results in a tendency to dichotomize trial outcome as either 'success' or 'failure' and thus oversimplifies interpretation. With relatively modest modification, these methods can be used to define a stepwise approach to design and analysis. In the first design step, the trial is sized to show indirectly that the new drug would have beaten placebo; in the second analysis step, the probability that the new drug is superior to placebo is assessed and, if sufficiently high in the third and final step, the relative efficacy of the new drug to control is assessed on a continuum of effect retention via an 'effect retention likelihood plot'. This stepwise approach is likely to provide a more complete assessment of relative efficacy so that the value of new treatments can be better judged.     

Identifying the most successful dose (MSD) in dose-finding studies in cancer

For a dose finding study in cancer, the most successful dose (MSD), among a group of available doses, is that dose at which the overall success rate is the highest. This rate is the product of the rate of seeing non-toxicities together with the rate of tumor response. A successful dose finding trial in this context is one where we manage to identify the MSD in an efficient manner. In practice we may also need to consider algorithms for identifying the MSD which can incorporate certain restrictions, the most common restriction maintaining the estimated toxicity rate alone below some maximum rate. In this case the MSD may correspond to a different level than that for the unconstrained MSD and, in providing a final recommendation, it is important to underline that it is subject to the given constraint. We work with the approach described in O'Quigley et al. [Biometrics 2001; 57(4):1018-1029]. The focus of that work was dose finding in HIV where both information on toxicity and efficacy were almost immediately available. Recent cancer studies are beginning to fall under this same heading where, as before, toxicity can be quickly evaluated and, in addition, we can rely on biological markers or other measures of tumor response. Mindful of the particular context of cancer, our purpose here is to consider the methodology developed by O'Quigley et al. and its practical implementation. We also carry out a study on the doubly under-parameterized model, developed by O'Quigley et al. but not     

Some Cautions on the Measurement of User Information Satisfaction*

User information satisfaction (UIS) is important because of its potential effects on MIS department goals, quality of user work life, and extent of voluntary usage of systems. Reliable measurement of UIS is important for providing evaluative information for both researchers and practitioners. This study used 92 managers and executives as subjects to compare the test/retest reliability of a widely used, 13-scale UIS instrument together with four summary questions under experimental and control conditions. The summary questions behaved more reliably than the detailed questions for all groups, perhaps because of problems with scale units and origins and with item heterogeneity. This suggests that researchers need more reliable measures of UIS and that practitioners need to exercise caution when collecting and interpreting UIS scores.     

A Warning Zone Method for the Multiperiod Cost Variance Investigation Problem

This paper develops a warning zone approach to make variance investigation decisions for a multiperiod process. The assumed cost generation process varies between an in-control and out-of-control state. These states cannot be directly observed, but must be inferred from the reported cost variances. Using the warning zone method of inference, the manager investigates the process whenever an upper threshold is exceeded or a lower threshold is exceeded for two consecutive periods. A four-state Markov chain models the resulting decision process. Steady state probabilities are derived for this chain and are used to obtain explicit formulas for the effectiveness and efficiency of the decision process. These formulas permit computation of the cost savings attainable by the warning zone method. Compared to other decision rules, the warning zone method is much simpler than the theoretically optimal Bayesian revision method, but uses more information than the Markovian control limit method. Numerical comparison of results shows that the warning zone method usually captures most of the available cost savings, even in cases where the Markovian control limit method does not perform well.