Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.     

PubPredict: Prediction of progression and survival in oncology leveraging publications and early efficacy data

In oncology/hematology early phase clinical trials, efficacies were often observed in terms of response rate, depth, timing, and duration. However, the true clinical benefits that eventually support registrational purpose are progression-free survival (PFS) and/or overall survival (OS), the follow-up of which are typically not long enough in early phase trials. This gap imposes challenges in strategies for late phase drug development. In this article, we tackle the question by leveraging published study to establish a quantitative link between early efficacy outcomes and late phase efficacy endpoints. We used solid tumor cancer as disease model. We modeled the disease course of a RECISTv1.1 assessed solid tumor with a continuous Markov chain (CMC) model. We parameterize the transition intensity matrix of a CMC model based on published aggregate-level summary statistics, and then simulate subject-level time-to-event data. The simulated data is shown to have good approximation to published studies. PFS and/or OS could be predicted with the transition intensity matrix modified given clinical knowledge to reflect various assumptions on response rate, depth, timing, and duration. The authors have built a R shiny application named PubPredict, the tool implements the algorithm described above and allows customized features including multiple response levels, treatment crossover and varying follow-up duration. This toolset has been applied to advise phase 3 trial design when only early efficacy data are available from phase 1 or 2 studies.     

Statistical methods for handling missing data to align with treatment policy strategy

The International Council for Harmonization (ICH) E9(R1) addendum recommends choosing an appropriate estimand based on the study objectives in advance of trial design. One defining attribute of an estimand is the intercurrent event, specifically what is considered an intercurrent event and how it should be handled. The primary objective of a clinical study is usually to assess a product's effectiveness and safety based on the planned treatment regimen instead of the actual treatment received. The estimand using the treatment policy strategy, which collects and analyzes data regardless of the occurrence of intercurrent events, is usually utilized. In this article, we explain how missing data can be handled using the treatment policy strategy from the authors' viewpoint in connection with antihyperglycemic product development programs. The article discusses five statistical methods to impute missing data occurring after intercurrent events. All five methods are applied within the framework of the treatment policy strategy. The article compares the five methods via Markov Chain Monte Carlo simulations and showcases how three of these five methods have been applied to estimate the treatment effects published in the labels for three antihyperglycemic agents currently on the market.     

Statistical Treatment Rules for Heterogeneous Populations

An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimax‐regret treatment choice using the sample data generated by a classical randomized experiment. Consider a utilitarian social planner who must choose among the feasible statistical treatment rules, these being functions that map the sample data and observed covariates of population members into a treatment allocation. If the planner knew the population distribution of treatment response, the optimal treatment rule would maximize mean welfare conditional on all observed covariates. The appropriate use of covariate information is a more subtle matter when only sample data on treatment response are available. I consider the class of conditional empirical success rules; that is, rules assigning persons to treatments that yield the best experimental outcomes conditional on alternative subsets of the observed covariates. I derive a closed‐form bound on the maximum regret of any such rule. Comparison of the bounds for rules that condition on smaller and larger subsets of the covariates yields sufficient sample sizes for productive use of covariate information. When the available sample size exceeds the sufficiency boundary, a planner can be certain that conditioning treatment choice on more covariates is preferable (in terms of minimax regret) to conditioning on fewer covariates.     

Risk Assessment of Salmonellosis from Consumption of Alfalfa Sprouts and Evaluation of the Public Health Impact of Sprout Seed Treatment and Spent Irrigation Water Testing

We developed a risk assessment of human salmonellosis associated with consumption of alfalfa sprouts in the United States to evaluate the public health impact of applying treatments to seeds (0–5‐log₁₀ reduction in Salmonella ) and testing spent irrigation water (SIW) during production. The risk model considered variability and uncertainty in Salmonella contamination in seeds, Salmonella growth and spread during sprout production, sprout consumption, and Salmonella dose response. Based on an estimated prevalence of 2.35% for 6.8 kg seed batches and without interventions, the model predicted 76,600 (95% confidence interval (CI) 15,400 – 248,000) cases/year. Risk reduction (by 5 ‐ to 7‐fold) predicted from a 1‐log₁₀ seed treatment alone was comparable to SIW testing alone, and each additional 1‐log₁₀ seed treatment was predicted to provide a greater risk reduction than SIW testing. A 3‐log₁₀ or a 5‐log₁₀ seed treatment reduced the predicted cases/year to 139 (95% CI 33 – 448) or 1.4 (95% CI <1 – 4.5), respectively. Combined with SIW testing, a 3‐log₁₀ or 5‐log₁₀ seed treatment reduced the cases/year to 45 (95% CI 10–146) or <1 (95% CI <1 – 1.5), respectively. If the SIW coverage was less complete (i.e., less representative), a smaller risk reduction was predicted, e.g., a combined 3‐log₁₀ seed treatment and SIW testing with 20% coverage resulted in an estimated 92 (95% CI 22 – 298) cases/year. Analysis of alternative scenarios using different assumptions for key model inputs showed that the predicted relative risk reductions are robust. This risk assessment provides a comprehensive approach for evaluating the public health impact of various interventions in a sprout production system.     

Substance Use Decision-Making – Are Clinicians Using the Evidence?

Clinical assessment and treatment decision-making is a complex, everyday task for the substance use workforce. This Canadian study conducted with community substance use providers in the Interior region of British Columbia examines the factors clinicians pay attention to in their decision-making. A randomized factorial survey approach, using three unique vignettes embedded with factors of interest, was used to test the effect of case and respondent factors on assessment and treatment decisions. Responses were received from 106 participants, representing approximately a 35% response rate, yielding a sample size (n) of 308 vignettes. Multiple regression tested the independent effects of the vignette and clinician factors on assessment and treatment decisions. Factors within the vignette associated with withdrawal, physical illness and mental health issues emerged as the most predictive elements. The social complexity of people’s lives, client’s stated treatment preferences and readiness for change, and respondent characteristics were obscured in decision-making. This study indicates a lack of fidelity in the use of core assessment and treatment-matching tools, suggesting that clinician decision-making may, in everyday practice, be more heuristic and evidence-informed than evidence-based. Further research on normative decision-making practices in the substance-field is warranted.     

论环境污染治理PPP机制的法律关系

环境污染治理向来被视为公关部门的独角戏,排斥私人资本的参与。但当前公共部门在环境污染治理中出现了资金不足、资金利用率低下等问题,引入ppp机制成为解决这些问题的有效选择之一。研究环境污染治理PPP机制应以我国现行法律法规为依据,以PPP机制的原理以及环境污染治理的特殊性为基础。处于PPP机制支配地位的是特许经营合同,其本质上是公法关系。环境污染治理PPP机制法律关系的内容涉及公共部门的权责、公共部门与私人投资主体之间的风险分担以及社会公众在该机制中的参与。     

Supervision and Treatment of Juveniles with Sexual Behavior Problems

Responses to a survey, distributed at the 2005 Conference for Sex Offender Management, were studied to identify treatment theories, client behavior expectations, and level of interagency collaboration relied upon to produce positive outcomes for Juveniles with Sexual Behavior Problems (JSBP). Analysis of 112 responses to the 31-item survey identified three potential subscales (Integration of Theory, Readiness for Program Release, and Collaboration) and indicated support for an integrative approach to treatment. Clinicians were more likely than probation, institutional officers and caseworkers to use cognitive-behavioral approaches to enhance positive outcomes. Results are discussed regarding three controversial issues related to JSBP supervision and treatment: use of polygraphs with juvenile sex offenders, referrals to 12-Step Sexual Addiction programs, and victim/offender reunification.     

西夏官吏“禄食”标准管窥——以《天盛律令》为中心

本文通过对西夏法典《天盛律令》中有关史料的整理,力求对西夏官吏的“禄食”标准进行比较系统的研究,认为“屠”（肉食）＋“米谷”（粮食）＋“马食”＋童仆食物构成了西夏具体餐饮标准。这一标准,从侧面又反映了西夏社会等级森严、贫富悬殊、物质相对匮乏的现实。另外,本文对史金波“一屠应是指屠杀羊一只”的解释提出了质疑。     

Live Your Life Out Loud

Susan Love, MD, MBA, has dedicated her professional life to the eradication of breast cancer. Author of the bestselling Dr. Susan Love's Breast Book, Dr. Susan Love's Menopause and Hormone Book, and Live a Little, Susan is Chief Visionary Officer of the Dr. Susan Love Research Foundation where she oversees an active research program centered on breast cancer cause and prevention. Susan co-founded the National Breast Cancer Coalition in the early 1990s, and she served on President Clinton's National Cancer Advisory Board from 1998–2004. Her recent projects include recruiting 377,000 women for the Love Army of Women, an Internet program that partners women and scientists to accelerate breast cancer research; and the online Health of Women Study designed to identify the cause of breast cancer. A recipient of six honorary doctorate degrees, Susan is Clinical Professor of Surgery at the David Geffen School of Medicine at the University of California, Los Angeles. In June of 2012, Susan was diagnosed with acute myelogenous leukemia and was treated with an allogenic stem cell transplant.