Trial Size for Near-Optimal Choice Between Surveillance and Aggressive Treatment: Reconsidering MSLT-II

Abstract

A convention in designing randomized clinical trials has been to choose sample sizes that yield specified statistical power when testing hypotheses about treatment response. Manski and Tetenov recently critiqued this convention and proposed enrollment of sufficiently many subjects to enable near-optimal treatment choices. This article develops a refined version of that analysis applicable to trials comparing aggressive treatment of patients with surveillance. The need for a refined analysis arises because the earlier work assumed that there is only a primary health outcome of interest, without secondary outcomes. An important aspect of choice between surveillance and aggressive treatment is that the latter may have side effects. One should then consider how the primary outcome and side effects jointly determine patient welfare. This requires new analysis of sample design. As a case study, we reconsider a trial comparing nodal observation and lymph node dissection when treating patients with cutaneous melanoma. Using a statistical power calculation, the investigators assigned 971 patients to dissection and 968 to observation. We conclude that assigning 244 patients to each option would yield findings that enable suitably near-optimal treatment choice. Thus, a much smaller sample size would have sufficed to inform clinical practice.     

Estimation of treatment effects for heterogeneous matched‐pairs data with probit models

Estimating the effect of medical treatments on subject responses is one of the crucial problems in medical research. Matched‐pairs designs are commonly implemented in the field of medical research to eliminate confounding and improve efficiency. In this article, new estimators of treatment effects for heterogeneous matched‐pairs data are proposed. Asymptotic properties of the proposed estimators are derived. Simulation studies show that the proposed estimators have some advantages over the famous Heckman's estimator, the conditional maximum likelihood estimator, and the inverse probability weighted estimator. We apply the proposed methodology to a data set from a study of low‐birth‐weight infants.     

Seamless phase II/III/IIIb clinical trial designs with different endpoints for different phases

Abstract

In a seamless phase II/III/IIIb trial, K (K?≥?2) doses versus placebo control are evaluated at phase II. Based on phase II results, one dose will be selected for phases III and IIIb. Pre-specified additional numbers of patients will be enrolled into the selected dose and placebo control during phases III and IIIb. Results of the phase III endpoint may be submitted for an early New Drug Application. Final analyses will be conducted for ultimate claims of treatment effects for the selected dose on the phase III and IIIb endpoints. Multiplicity adjustment is performed for the overall type I error rate control.     

An integrated lean and green approach for improving sustainability performance: a case study of a packaging manufacturing SME in the U.K.

Abstract

Competitive landscape, informed consumers and stringent regulations have forced many manufacturing small- and medium-sized enterprises (SMEs) to focus on operational efficiency along with sustainability issues in recent years. While many manufacturing organisations have been taking lean initiatives for the past few years for operational excellence, an impulsive rush to adopt lean without a strategic deployment vision has led to scattered implementation of lean tools and projects without desired success. Many researchers and practitioners prescribe value stream mapping as a foundation for lean transformation initiatives; however, little empirical work is available on the symbiosis of lean and green paradigms to reap maximum benefits. This research, through a systematic methodology and a novel tool called Green Integrated Value Stream Mapping (GIVSM), integrates both paradigms in a case study on a U.K. packaging-manufacturing SME. Applying the GIVSM demonstrates that simultaneous deployment of lean and green paradigms have synergistic effect for improving both operational efficiency and environmental performance. In addition, continuous improvement framework with sustainable procurement is proposed to overcome the lean-green misalignments. This study also provides a guiding reference for practitioners to undertake similar improvement projects and identifies opportunities to expand this academic research on integrated lean-green approach into other industry sectors.     

Performance evaluation of propensity score methods for estimating average treatment effects with multi-level treatments*

The propensity score (PS) method is widely used to estimate the average treatment effect (TE) in observational studies. However, it is generally confined to the binary treatment assignment. In an extension to the settings of a multi-level treatment, Imbens proposed a generalized propensity score which is the conditional probability of receiving a particular level of the treatment given pre-treatment variables. The average TE can then be estimated by conditioning solely on the generalized PS under the assumption of weak unconfoundedness. In the present work, we adopted this approach and conducted extensive simulations to evaluate the performance of several methods using the generalized PS, including subclassification, matching, inverse probability of treatment weighting (IPTW), and covariate adjustment. Compared with other methods, IPTW had the preferred overall performance. We then applied these methods to a retrospective cohort study of 228,876 pregnant women. The impact of the exposure to different types of the antidepressant medications (no exposure, selective serotonin reuptake inhibitor (SSRI) only, non-SSRI only, and both) during pregnancy on several important infant outcomes (birth weight, gestation age, preterm labor, and respiratory distress) were assessed.     

Scenario Analysis for the Safety Assessment of Nuclear Waste Repositories: A Critical Review

A major challenge in scenario analysis for the safety assessment of nuclear waste repositories pertains to the comprehensiveness of the set of scenarios selected for assessing the safety of the repository. Motivated by this challenge, we discuss the aspects of scenario analysis relevant to comprehensiveness. Specifically, we note that (1) it is necessary to make it clear why scenarios usually focus on a restricted set of features, events, and processes; (2) there is not yet consensus on the interpretation of comprehensiveness for guiding the generation of scenarios; and (3) there is a need for sound approaches to the treatment of epistemic uncertainties.     

Cancer immunotherapy trial design with delayed treatment effect

A challenge arising in cancer immunotherapy trial design is the presence of a delayed treatment effect wherein the proportional hazard assumption no longer holds true. As a result, a traditional survival trial design based on the standard log‐rank test, which ignores the delayed treatment effect, will lead to substantial loss of statistical power. Recently, a piecewise weighted log‐rank test is proposed to incorporate the delayed treatment effect into consideration of the trial design. However, because the sample size formula was derived under a sequence of local alternative hypotheses, it results in an underestimated sample size when the hazard ratio is relatively small for a balanced trial design and an inaccurate sample size estimation for an unbalanced design. In this article, we derived a new sample size formula under a fixed alternative hypothesis for the delayed treatment effect model. Simulation results show that the new formula provides accurate sample size estimation for both balanced and unbalanced designs.     

农业废弃物资源化利用交易模式及定价研究

从系统稳定性的视角,采用演化博弈方法,通过对农业废弃物第三方资源化利用博弈模型均衡点的稳定性分析可知:受农业废弃物污染和资源双重属性的影响,收费和收购模式均具有一定的适用性,在不同条件下,存在仅收费或收购的单一交易模式以及收费与收购并存的混合交易模式;在混合交易模式中,收费与收购模式的稳定性存在此消彼长的反向变化关系,受农业废弃物中不可回收物占比的影响,收费模式与收购模式之间存在模式转换的临界点;针对混合交易模式中的收费与收购模式,分别得出每种模式下的最优定价表达式。     

Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study

Propensity score methods are increasingly used in medical literature to estimate treatment effect using data from observational studies. Despite many papers on propensity score analysis, few have focused on the analysis of survival data. Even within the framework of the popular proportional hazard model, the choice among marginal, stratified or adjusted models remains unclear. A Monte Carlo simulation study was used to compare the performance of several survival models to estimate both marginal and conditional treatment effects. The impact of accounting or not for pairing when analysing propensity‐score‐matched survival data was assessed. In addition, the influence of unmeasured confounders was investigated. After matching on the propensity score, both marginal and conditional treatment effects could be reliably estimated. Ignoring the paired structure of the data led to an increased test size due to an overestimated variance of the treatment effect. Among the various survival models considered, stratified models systematically showed poorer performance. Omitting a covariate in the propensity score model led to a biased estimation of treatment effect, but replacement of the unmeasured confounder by a correlated one allowed a marked decrease in this bias. Our study showed that propensity scores applied to survival data can lead to unbiased estimation of both marginal and conditional treatment effect, when marginal and adjusted Cox models are used. In all cases, it is necessary to account for pairing when analysing propensity‐score‐matched data, using a robust estimator of the variance. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantitative comparison of randomization designs in sequential clinical trials based on treatment balance and allocation randomness

To evaluate the performance of randomization designs under various parameter settings and trial sample sizes, and identify optimal designs with respect to both treatment imbalance and allocation randomness, we evaluate 260 design scenarios from 14 randomization designs under 15 sample sizes range from 10 to 300, using three measures for imbalance and three measures for randomness. The maximum absolute imbalance and the correct guess (CG) probability are selected to assess the trade-off performance of each randomization design. As measured by the maximum absolute imbalance and the CG probability, we found that performances of the 14 randomization designs are located in a closed region with the upper boundary (worst case) given by Efron's biased coin design (BCD) and the lower boundary (best case) from the Soares and Wu's big stick design (BSD). Designs close to the lower boundary provide a smaller imbalance and a higher randomness than designs close to the upper boundary. Our research suggested that optimization of randomization design is possible based on quantified evaluation of imbalance and randomness. Based on the maximum imbalance and CG probability, the BSD, Chen's biased coin design with imbalance tolerance method, and Chen's Ehrenfest urn design perform better than popularly used permuted block design, EBCD, and Wei's urn design.