Abstract Background Lack of standardization of outcome measures limits the usefulness of clinical trial evidence to inform health care decisions. This can be addressed by agreeing on a minimum core ...set of outcome measures per health condition, containing measures relevant to patients and decision makers. Since 1992, the Outcome Measures in Rheumatology (OMERACT) consensus initiative has successfully developed core sets for many rheumatologic conditions, actively involving patients since 2002. Its expanding scope required an explicit formulation of its underlying conceptual framework and process. Methods Literature searches and iterative consensus process (surveys and group meetings) of stakeholders including patients, health professionals, and methodologists within and outside rheumatology. Results To comprehensively sample patient-centered and intervention-specific outcomes, a framework emerged that comprises three core “Areas,” namely Death, Life Impact, and Pathophysiological Manifestations; and one strongly recommended Resource Use. Through literature review and consensus process, core set development for any specific health condition starts by identifying at least one core “Domain” within each of the Areas to formulate the “Core Domain Set.” Next, at least one applicable measurement instrument for each core Domain is identified to formulate a “Core Outcome Measurement Set.” Each instrument must prove to be truthful (valid), discriminative, and feasible. In 2012, 96% of the voting participants ( n = 125) at the OMERACT 11 consensus conference endorsed this model and process. Conclusion The OMERACT Filter 2.0 explicitly describes a comprehensive conceptual framework and a recommended process to develop core outcome measurement sets for rheumatology likely to be useful as a template in other areas of health care.
Creative use of new mobile and wearable health information and sensing technologies (mHealth) has the potential to reduce the cost of health care and improve well-being in numerous ways. These ...applications are being developed in a variety of domains, but rigorous research is needed to examine the potential, as well as the challenges, of utilizing mobile technologies to improve health outcomes. Currently, evidence is sparse for the efficacy of mHealth. Although these technologies may be appealing and seemingly innocuous, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious. In order to outline an approach to evidence generation in the field of mHealth that would ensure research is conducted on a rigorous empirical and theoretic foundation, on August 16, 2011, researchers gathered for the mHealth Evidence Workshop at NIH. The current paper presents the results of the workshop. Although the discussions at the meeting were cross-cutting, the areas covered can be categorized broadly into three areas: (1) evaluating assessments; (2) evaluating interventions; and (3) reshaping evidence generation using mHealth. This paper brings these concepts together to describe current evaluation standards, discuss future possibilities, and set a grand goal for the emerging field of mHealth research.
The design of randomised controlled trials (RCTs) should incorporate characteristics (such as concealment of randomised allocation and blinding of participants and personnel) that avoid biases ...resulting from lack of comparability of the intervention and control groups. Empirical evidence suggests that the absence of such characteristics leads to biased intervention effect estimates, but the findings of different studies are not consistent.
To examine the influence of unclear or inadequate random sequence generation and allocation concealment, and unclear or absent double blinding, on intervention effect estimates and between-trial heterogeneity, and whether or not these influences vary with type of clinical area, intervention, comparison and outcome measure.
Data were combined from seven contributing meta-epidemiological studies (collections of meta-analyses in which trial characteristics are assessed and results recorded). The resulting database was used to identify and remove overlapping meta-analyses. Outcomes were coded such that odds ratios < 1 correspond to beneficial intervention effects. Outcome measures were classified as mortality, other objective or subjective. We examined agreement between assessments of trial characteristics in trials assessed in more than one contributing study. We used hierarchical Bayesian bias models to estimate the effect of trial characteristics on average bias quantified as ratios of odds ratios (RORs) with 95% credible intervals (CrIs) comparing trials with and without a characteristic and in increasing between-trial heterogeneity.
The analysis data set contained 1973 trials included in 234 meta-analyses. Median kappa statistics for agreement between assessments of trial characteristics were: sequence generation 0.60, allocation concealment 0.58 and blinding 0.87. Intervention effect estimates were exaggerated by an average 11% in trials with inadequate or unclear (compared with adequate) sequence generation (ROR 0.89, 95% CrI 0.82 to 0.96); between-trial heterogeneity was higher among such trials. Bias associated with inadequate or unclear sequence generation was greatest for subjective outcomes (ROR 0.83, 95% CrI 0.74 to 0.94) and the increase in heterogeneity was greatest for such outcomes standard deviation (SD) 0.20, 95% CrI 0.03 to 0.32. The effect of inadequate or unclear (compared with adequate) allocation concealment was greatest among meta-analyses with a subjectively assessed outcome intervention effect (ROR 0.85, 95% CrI 0.75 to 0.95), and the increase in between-trial heterogeneity was also greatest for such outcomes (SD 0.20, 95% CrI 0.02 to 0.33). Lack of, or unclear, double blinding (compared with double blinding) was associated with an average 13% exaggeration of intervention effects (ROR 0.87, 95% CrI 0.79 to 0.96), and between-trial heterogeneity was increased for such studies (SD 0.14, 95% CrI 0.02 to 0.30). Average bias (ROR 0.78, 95% CrI 0.65 to 0.92) and between-trial heterogeneity (SD 0.37, 95% CrI 0.19 to 0.53) were greatest for meta-analyses assessing subjective outcomes. Among meta-analyses with subjectively assessed outcomes, the effect of lack of blinding appeared greater than the effect of inadequate or unclear sequence generation or allocation concealment.
Bias associated with specific reported study design characteristics leads to exaggeration of beneficial intervention effect estimates and increases in between-trial heterogeneity. For each of the three characteristics assessed, these effects were greatest for subjectively assessed outcomes. Assessments of the risk of bias in RCTs should account for these findings. Further research is needed to understand the effects of attrition bias, as well as the relative importance of blinding of patients, care-givers and outcome assessors, and thus separate the effects of performance and detection bias.
National Institute for Health Research Health Technology Assessment programme.
The objective of this study was to assess the cumulative incidence of invasive candidiasis (IC) in intensive care units (ICUs) in Europe.
A multinational, multicenter, retrospective study was ...conducted in 23 ICUs in 9 European countries, representing the first phase of the candidemia/intra-abdominal candidiasis in European ICU project (EUCANDICU).
During the study period, 570 episodes of ICU-acquired IC were observed, with a cumulative incidence of 7.07 episodes per 1000 ICU admissions, with important between-center variability. Separated, non-mutually exclusive cumulative incidences of candidemia and IAC were 5.52 and 1.84 episodes per 1000 ICU admissions, respectively. Crude 30-day mortality was 42%. Age (odds ratio OR 1.04 per year, 95% CI 1.02-1.06, p < 0.001), severe hepatic failure (OR 3.25, 95% 1.31-8.08, p 0.011), SOFA score at the onset of IC (OR 1.11 per point, 95% CI 1.04-1.17, p 0.001), and septic shock (OR 2.12, 95% CI 1.24-3.63, p 0.006) were associated with increased 30-day mortality in a secondary, exploratory analysis.
The cumulative incidence of IC in 23 European ICUs was 7.07 episodes per 1000 ICU admissions. Future in-depth analyses will allow explaining part of the observed between-center variability, with the ultimate aim of helping to improve local infection control and antifungal stewardship projects and interventions.
Globally, liver cancer is the most frequent fatal malignancy; in the United States, it ranks fifth. Patients are often diagnosed with liver cancer in advanced stages, contributing to its poor ...prognosis. Of all liver cancer cases, >90% are hepatocellular carcinomas (HCCs) for which chemotherapy and immunotherapy are the best options for therapy. For liver cancer patients, new treatment options are necessary. Use of natural compounds and/or nanotechnology may provide patients with better outcomes with lower systemic toxicity and fewer side effects. Improved treatments can lead to better prognoses. Finally, in this review, we present some of the problems and current treatment options contributing to the poor outcomes for patients with liver cancer.
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment ...options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems.
We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.
We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients.
The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.
Treatment effects, especially when comparing two or more therapeutic alternatives as in comparative effectiveness research, are likely to be heterogeneous across age, gender, co‐morbidities and ...co‐medications. Propensity scores (PSs), an alternative to multivariable outcome models to control for measured confounding, have specific advantages in the presence of heterogeneous treatment effects. Implementing PSs using matching or weighting allows us to estimate different overall treatment effects in differently defined populations. Heterogeneous treatment effects can also be due to unmeasured confounding concentrated in those treated contrary to prediction. Sensitivity analyses based on PSs can help to assess such unmeasured confounding. PSs should be considered a primary or secondary analytic strategy in nonexperimental medical research, including pharmacoepidemiology and nonexperimental comparative effectiveness research.
The ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS) version 1.0 (v1.0) was published in May 2015 and was the first version of a validated and reproducible tool to assess the magnitude of ...clinical benefit from new cancer therapies. The ESMO-MCBS was designed to be a dynamic tool with planned revisions and updates based upon recognition of expanding needs and shortcomings identified since the last review.
The revision process for the ESMO-MCBS incorporates a nine-step process: Careful review of critiques and suggestions, and identification of problems in the application of v1.0; Identification of shortcomings for revision in the upcoming version; Proposal and evaluation of solutions to address identified shortcomings; Field testing of solutions; Preparation of a near-final revised version for peer review for reasonableness by members of the ESMO Faculty and Guidelines Committee; Amendments based on peer review for reasonableness; Near-final review by members of the ESMO-MCBS Working Group and the ESMO Executive Board; Final amendments; Final review and approval by members of the ESMO-MCBS Working Group and the ESMO Executive Board.
Twelve issues for revision or amendment were proposed for consideration; proposed amendments were formulated for eight identified shortcomings. The proposed amendments are classified as either structural, technical, immunotherapy triggered or nuanced. All amendments were field tested in a wide range of studies comparing scores generated with ESMO-MCBS v1.0 and version 1.1 (v1.1).
ESMO-MCBS v1.1 incorporates 10 revisions and will allow for scoring of single-arm studies. Scoring remains very stable; revisions in v1.1 alter the scores of only 12 out of 118 comparative studies and facilitate scoring for single-arm studies.
Effects of treatment or other exposure on outcome events are commonly measured by ratios of risks, rates, or odds. Adjusted versions of these measures are usually estimated by maximum likelihood ...regression (eg, logistic, Poisson, or Cox modelling). But resulting estimates of effect measures can have serious bias when the data lack adequate case numbers for some combination of exposure and outcome levels. This bias can occur even in quite large datasets and is hence often termed sparse data bias. The bias can arise or be worsened by regression adjustment for potentially confounding variables; in the extreme, the resulting estimates could be impossibly huge or even infinite values that are meaningless artefacts of data sparsity. Such estimate inflation might be obvious in light of background information, but is rarely noted let alone accounted for in research reports. We outline simple methods for detecting and dealing with the problem focusing especially on penalised estimation, which can be easily performed with common software packages.
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