Clinical trials in specific indications require the administration of rescue medication in case a patient does not sufficiently respond to investigational treatment. The application of additional treatment on an as needed basis causes problems to the analysis and interpretation of the results of these studies since the effect of the investigational treatment can be confounded by the additional medication. Following-up all patients until study end and capturing all data is not fully addressing the issue. We present an analysis that takes care of the fact that rescue is a study outcome and not a covariate when rescue medication is administered according to a deterministic rule. This approach allows to clearly define a biological effect. For normally distributed longitudinal data a practically unbiased estimator of the biological effect can be obtained. The results are compared to an ITT analysis and an analysis on all patients not receiving rescue.
The interpretation of randomised clinical trial results is often complicated by intercurrent events. For instance, rescue medication is sometimes given to patients in response to worsening of their disease, either in addition to the randomised treatment or in its place. The use of such medication complicates the interpretation of the intention-to-treat analysis. In view of this, we propose a novel estimand defined as the intention-to-treat effect that would have been observed, had patients on the active arm been switched to rescue medication if and only if they would have been switched when ra…
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for the ongoing clinical trials in non-COVID-19 conditions. Motivated by four currently ongoing clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve type I error r…
Randomized controlled trials (RCT) are the gold standard for evaluation of the efficacy and safety of investigational interventions. If every patient in an RCT were to adhere to the randomized treatment, one could simply analyze the complete data to infer the treatment effect. However, intercurrent events (ICEs) including the use of concomitant medication for unsatisfactory efficacy, treatment discontinuation due to adverse events, or lack of efficacy, may lead to interventions that deviate from the original treatment assignment. Therefore, defining the appropriate estimand (the appropriate pa…
[Invited Discussion] Randomization Tests to Address Disruptions in Clinical Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understanding how the direct and indirect effects develop over time. Hence, importantly, we allow for a time varying mediator. To define direct and indirect effects in such a longitudinal survival setting we take an interventional approach (Didelez (2018)) where treatment is separated into one aspect affecting the mediator and a different aspect affecting survival. In general, this leads to a version of the non-parametric g-…
Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. Material and Methods: We used data from…
Incorporating estimands into meta-analyses of clinical trials
Novo Nordisk Pharma（诺和诺德制药）；University College London（伦敦大学学院）；Merck Healthcare, KGaA（默克医疗）；Daiichi Sankyo Italia（第一三共意大利）；Novo Nordisk A/S（诺和诺德）；Medicines and Healthcare products Regulatory Agency（英国药品和医疗产品监管局）；Sanofi-Aventis Deutschland GmbH（赛诺菲安万特德国）；University of Bristol（布里斯托大学）；Core Clinical Sciences Inc.（核心临床科学公司）；McMaster University（麦克马斯特大学）；Astellas Pharma Europe Ltd（安斯泰来欧洲）
Automated Hierarchical Graph Construction for Multi-source Electronic Health Records
University of Chicago（芝加哥大学）；National University of Singapore（新加坡国立大学）；Harvard University（哈佛大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Significant evidence has become available that emphasizes the importance of personalization in medicine. In fact, it has become a common belief that personalized medicine is the future of medicine. The core of personalized medicine is the ability to design clinical trials that investigate the role of patient covariates on treatment effects. In this work, we study the optimal design of two-armed clinical trials to maximize accuracy of statistical models where the interaction between patient covariates and treatment effect are incorporated to enable precision medication. Such a modeling extensio…
Patients often discontinue treatment in a clinical trial because their health condition is not improving. Consequently, the patients still in the study at the end of the trial have better health outcomes on average than the initial patient population would have had if every patient had completed the trial. If we only analyze the patients who complete the trial, then this missing data problem biases the estimator of a medication's efficacy because study outcomes are missing not at random (MNAR). One way to overcome this problem - the trimmed means approach for missing data - sets missing values…
Estimating Causal Treatment Effects in Placebo-Controlled Randomized Clinical Trials When High Placebo Response is Anticipated
Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center（贝斯以色列女医生医疗中心心脏结局研究史密斯中心）；Department of Statistics, SungKyunKwan University（成均馆大学统计系）；Biostatistics Department, Boston University（波士顿大学生物统计学系）
Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury
Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam Public Health（阿姆斯特丹公共卫生研究所）；Institute for Logic, Language and Computation（逻辑、语言与计算研究所）；Pacmed（Pacmed公司）
Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data
University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）；Lady Davis Institute, Jewish General Hospital（戴维斯研究所，犹太总医院）；McGill University（麦吉尔大学）；University of British Columbia（不列颠哥伦比亚大学）；Norwegian Institute of Public Health（挪威公共卫生研究所）；University of Pennsylvania（宾夕法尼亚大学）；RTI Health Solutions（RTI健康解决方案）；Northwestern University Feinberg School of Medicine（西北大学费恩伯格医学院）；University of Toronto（多伦多大学）；The Hospital for Sick Children（儿童医院）
Interventional effects have been proposed as a solution to the unidentifiability of natural (in)direct effects under mediator-outcome confounders affected by the exposure. Such confounders are an intrinsic characteristic of studies with time-varying exposures and mediators, yet the generalization of the interventional effect framework to the time-varying case has received little attention in the literature. We present an identification result for interventional effects in a general longitudinal data structure that allows flexibility in the specification of treatment-outcome, treatment-mediator…
The regression discontinuity (RD) design is a quasi-experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can be applied in any context where a particular treatment or intervention is administered according to a pre-specified rule linked to a continuous variable. Such thresholds are common in primary care drug prescription where the RD design can be used to estimate the causal effect of medication in the general population. Such results can then be contrasted to those obtained from randomised controlled trials (RCTs) and info…
University of New South Wales（新南威尔士大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61部门）
We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observational studies are growing in significance in answering such questions due to their widespread accumulation and comparatively easier acquisition than Randomized Control Trials (RCTs). Recently, some works have introduced representation learning and domain adaptation into counterfactual inference. However, most current works focus on the setting of binary treatments. None of them considers that different treatments' sam…
Living forwards or understanding backwards? A comparison of Inverse Probability of Treatment Weighting and G-estimation methods for targeting hypothetical full adherence estimands in longitudinal cohort studies
University of Exeter（埃克塞特大学）；Royal Devon University Healthcare NHS Foundation Trust（皇家德文郡大学医疗保健NHS基金会信托）；Novo Nordisk Research Centre (NNRCO)（诺和诺德研究中心）
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients
Duke University（杜克大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Massachusetts General Hospital（麻省总医院）；Harvard T. H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Institute of Natural Sciences（自然科学研究所）；Shanghai Jiao Tong University（上海交通大学）
