When is it impractical to ask informed consent? A systematic review.

BACKGROUND: Informed consent is one of the cornerstones of biomedical research with human subjects. Research ethics committees may allow for a modification or a waiver of consent when the research has social value, involves minimal risk, and if consent is impractical to obtain. While the conditions of social value and minimal risk have received ample attention in research ethics literature, the impractical condition remains unclear. There seem to be different interpretations of the meaning of impractical within academic literature. To address this lack of clarity, we performed a systematic review on the interpretation of impractical. METHODS: First, we examined international research ethics guidelines on their usage and interpretation of impractical. Next, we used international ethical guidelines to identify synonyms of the term "impractical." Accordingly, PubMed, Embase, and Web of Science were searched for articles that included "informed consent" and "impractical" or one of its synonyms. RESULTS: We found that there were only a few international ethics guidelines that described what could be considered impractical. Out of 2329 identified academic articles, 42 were included. Impractical was used to describe four different conditions: (1) obtaining informed consent becomes too demanding for researchers, (2) obtaining informed consent leads to invalid study outcomes, (3) obtaining informed consent harms the participant, and (4) obtaining informed consent is meaningless for the participant. CONCLUSION: There are conditions that render conventional informed consent truly impractical, such as untraceable participants or harm for participants. At the same time, researchers have a moral responsibility to design an infrastructure in which consent can be obtained, even if they face hardship in obtaining consent. In addition, researchers should seek to minimize harm inflicted upon participants when harm may occur as a result of the consent procedure. Invalidity of research due to consent issues should not be regarded as impractical but as a condition that limits the social value of research. Further research is essential for when a waiver of informed consent based on impractical is also reasonable.

Negative controls: Concepts and caveats.

Unmeasured confounding is a well-known obstacle in causal inference. In recent years, negative controls have received increasing attention as a important tool to address concerns about the problem. The literature on the topic has expanded rapidly and several authors have advocated the more routine use of negative controls in epidemiological practice. In this article, we review concepts and methodologies based on negative controls for detection and correction of unmeasured confounding bias. We argue that negative controls may lack both specificity and sensitivity to detect unmeasured confounding and that proving the null hypothesis of a null negative control association is impossible. We focus our discussion on the control outcome calibration approach, the difference-in-difference approach, and the double-negative control approach as methods for confounding correction. For each of these methods, we highlight their assumptions and illustrate the potential impact of violations thereof. Given the potentially large impact of assumption violations, it may sometimes be desirable to replace strong conditions for exact identification with weaker, easily verifiable conditions, even when these imply at most partial identification of unmeasured confounding. Future research in this area may broaden the applicability of negative controls and in turn make them better suited for routine use in epidemiological practice. At present, however, the applicability of negative controls should be carefully judged on a case-by-case basis.

Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment.

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.

Application of quantitative bias analysis for unmeasured confounding in cost-effectiveness modelling.

Due to uncertainty regarding the potential impact of unmeasured confounding, health technology assessment (HTA) agencies often disregard evidence from nonrandomized studies when considering new technologies. Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, particularly in the context of cost-effectiveness modelling (CEM). This study demonstrated the application of an aggregate and patient-level QBA approach to quantify and adjust for unmeasured confounding in a simulated nonrandomized comparison of survival outcomes. Application of the QBA output within a CEM through deterministic and probabilistic sensitivity analyses and under different scenarios of knowledge of an unmeasured confounder demonstrates the potential value of QBA in HTA.

The potential value of observational studies of elective surgical interventions using routinely collected data.

PURPOSE: To assess the apparent validity of observational studies of elective arthroplasty interventions. METHODS: Data from the nationwide Dutch Arthroplasty Register were used. The first case study compared surgical approaches for total hip arthroplasty (posterolateral approach vs. straight lateral approach), where allocation of the intervention was assumed to be mostly independent of patient characteristics. The second case study compared fixation methods (cemented vs. uncemented), where choice of fixation method was expected to depend on patient characteristics. The potential for confounding was quantified by differences between intervention groups and the impact of confounding adjustment. RESULTS: The study of posterolateral approach versus straight lateral approach included 73,750 and 16,557 patients, respectively, and showed no meaningful differences in patient characteristics between treatment groups (standardized mean differences <0.1) and also no relevant impact of confounding adjustment (Z-scores <1). The study of cemented versus uncemented total hip arthroplasty (THA) included 29,579 and 79,360 patients, respectively. Several meaningful imbalances were observed in patient characteristic between the two treatment groups (standardized mean differences >0.1), as well as a relevant impact of confounding adjustment (Z-scores >2). CONCLUSIONS: This study provides insight in the reasoning behind the credibility of observational studies of surgical interventions using routinely collected data and when confounding is expected to have a major impact and thus additional precautions to limit confounding are needed.

A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications.

Joint misclassification of exposure and outcome variables can lead to considerable bias in epidemiological studies of causal exposure-outcome effects. In this paper, we present a new maximum likelihood based estimator for marginal causal effects that simultaneously adjusts for confounding and several forms of joint misclassification of the exposure and outcome variables. The proposed method relies on validation data for the construction of weights that account for both sources of bias. The weighting estimator, which is an extension of the outcome misclassification weighting estimator proposed by Gravel and Platt (Weighted estimation for confounded binary outcomes subject to misclassification. Stat Med 2018; 37: 425-436), is applied to reinfarction data. Simulation studies were carried out to study its finite sample properties and compare it with methods that do not account for confounding or misclassification. The new estimator showed favourable large sample properties in the simulations. Further research is needed to study the sensitivity of the proposed method and that of alternatives to violations of their assumptions. The implementation of the estimator is facilitated by a new R function (ipwm) in an existing R package (mecor).

Evaluating privacy of individuals in medical data.

Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data. Because privacy is evaluated per variable, these adjustments can be made while incorporating how likely it is that these variables will be exploited to uncover sensitive information in practice, as is mandatory in the European Union. Additionally, the analysis of privacy can be used to evaluate to what extent knowledge on specific variables in the data can contribute to privacy breaches, which can subsequently guide the use of anonymization techniques, such as generalization.

ORIF versus MIPO for humeral shaft fractures: a meta-analysis and systematic review of randomized clinical trials and observational studies.

BACKGROUND: There is no consensus on the optimal operative technique for humeral shaft fractures. This meta-analysis aims to compare minimal-invasive plate osteosynthesis (MIPO) with open reduction internal fixation (ORIF) for humeral shaft fractures regarding non-union, re-intervention, radial nerve palsy, time to union, operation duration and functional outcomes. METHODS: PubMed/Medline/Embase/CENTRAL/CINAHL were searched for both randomized clinical trials (RCT) and observational studies comparing MIPO with ORIF for humeral shaft fractures. Effect estimates were pooled across studies using random effects models and presented as weighted odds ratio (OR), risk difference (RD), mean difference (MD) and standardized mean difference (SMD) with corresponding 95% confidence interval (95%CI). Subgroup analysis was performed stratified by study design (RCTs and observational studies). RESULTS: A total of two RCT's (98 patients) and seven observational studies (263 patients) were included. The effect estimates obtained from observational studies and RCT's were similar in direction and magnitude. MIPO carries a lower risk for non-union (RD: 5%; OR 0.3, 95% CI 0.1-0.9) and secondary radial nerve palsy (RD 5%; OR 0.3, 95%CI 0.1- 0.9). Nerve function eventually restored spontaneously in all patients in both groups. Results were inconclusive regarding re-intervention (RD 7%; OR: 0.7, 95%CI 0.2-1.9), infection (RD 4%; OR 0.4, 95%CI 0.1-1.5), time to union (MD -1 week, 95%CI -3 - 1) and operation duration (MD -13 minutes, 95%CI -38.9 - 11.9). Functional shoulder scores (SMD 0.01, 95%CI -0.3 - 0.3) and elbow scores (SMD 0.01, 95%CI -0.3 - 0.3) were similar for the different operative techniques. CONCLUSION: MIPO has a lower risk for non-union than ORIF for the treatment of humeral shaft fractures. Radial nerve palsy secondary to operation is a temporary issue resolving in all patients in both treatment groups. Although both treatment options are viable, the general balance leans towards MIPO having more favorable outcomes.

Introduction to statistical simulations in health research.

In health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our specific study? Like in any science, in statistics, experiments can be run to find out which methods should be used under which circumstances. The main objective of this paper is to demonstrate that simulation studies, that is, experiments investigating synthetic data with known properties, are an invaluable tool for addressing these questions. We aim to provide a first introduction to simulation studies for data analysts or, more generally, for researchers involved at different levels in the analyses of health data, who (1) may rely on simulation studies published in statistical literature to choose their statistical methods and who, thus, need to understand the criteria of assessing the validity and relevance of simulation results and their interpretation; and/or (2) need to understand the basic principles of designing statistical simulations in order to efficiently collaborate with more experienced colleagues or start learning to conduct their own simulations. We illustrate the implementation of a simulation study and the interpretation of its results through a simple example inspired by recent literature, which is completely reproducible using the R-script available from online supplemental file 1.

Use of Smart Technology for the Early Diagnosis of Complications After Cardiac Surgery: The Box 2.0 Study Protocol.

BACKGROUND: Atrial fibrillation (AF), sternal wound infection, and cardiac decompensation are complications that can occur after cardiac surgery. Early detection of these complications is clinically relevant, as early treatment is associated with better clinical outcomes. Remote monitoring with the use of a smartphone (mobile health [mHealth]) might improve the early detection of complications after cardiac surgery. OBJECTIVE: The primary aim of this study is to compare the detection rate of AF diagnosed with an mHealth solution to the detection rate of AF diagnosed with standard care. Secondary objectives include detection of sternal wound infection and cardiac decompensation, as well as assessment of quality of life, patient satisfaction, and cost-effectiveness. METHODS: The Box 2.0 is a study with a prospective intervention group and a historical control group for comparison. Patients undergoing cardiac surgery at Leiden University Medical Center are eligible for enrollment. In this study, 365 historical patients will be used as controls and 365 other participants will be asked to receive either The Box 2.0 intervention consisting of seven home measurement devices along with a video consultation 2 weeks after discharge or standard cardiac care for 3 months. Patient information will be analyzed according to the intention-to-treat principle. The Box 2.0 devices include a blood pressure monitor, thermometer, weight scale, step count watch, single-lead electrocardiogram (ECG) device, 12-lead ECG device, and pulse oximeter. RESULTS: The study started in November 2018. The primary outcome of this study is the detection rate of AF in both groups. Quality of life is measured with the five-level EuroQol five-dimension (EQ-5D-5L) questionnaire. Cost-effectiveness is calculated from a society perspective using prices from Dutch costing guidelines and quality of life data from the study. In the historical cohort, 93.9% (336/358) completed the EQ-5D-5L and patient satisfaction questionnaires 3 months after cardiac surgery. CONCLUSIONS: The rationale and design of a study to investigate mHealth devices in postoperative cardiac surgery patients are presented. The first results are expected in September 2020. TRIAL REGISTRATION: ClinicalTrials.gov NCT03690492; http://clinicaltrials.gov/show/NCT03690492. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16326.

Investigating Risk Adjustment Methods for Health Care Provider Profiling When Observations are Scarce or Events Rare.

BACKGROUND: When profiling health care providers, adjustment for case-mix is essential. However, conventional risk adjustment methods may perform poorly, especially when provider volumes are small or events rare. Propensity score (PS) methods, commonly used in observational studies of binary treatments, have been shown to perform well when the amount of observations and/or events are low and can be extended to a multiple provider setting. The objective of this study was to evaluate the performance of different risk adjustment methods when profiling multiple health care providers that perform highly protocolized procedures, such as coronary artery bypass grafting. METHODS: In a simulation study, provider effects estimated using PS adjustment, PS weighting, PS matching, and multivariable logistic regression were compared in terms of bias, coverage and mean squared error (MSE) when varying the event rate, sample size, provider volumes, and number of providers. An empirical example from the field of cardiac surgery was used to demonstrate the different methods. RESULTS: Overall, PS adjustment, PS weighting, and logistic regression resulted in provider effects with low amounts of bias and good coverage. The PS matching and PS weighting with trimming led to biased effects and high MSE across several scenarios. Moreover, PS matching is not practical to implement when the number of providers surpasses three. CONCLUSIONS: None of the PS methods clearly outperformed logistic regression, except when sample sizes were relatively small. Propensity score matching performed worse than the other PS methods considered.

Assessment of channeling bias among initiators of glucose-lowering drugs: A UK cohort study.

BACKGROUND: Channeling bias may occur when a newly marketed drug and an established drug, despite similar indications, are prescribed to patients with different prognostic characteristics (ie, confounding). AIM: To investigate channeling bias and its impact on relative effectiveness of glucagon-like peptide-1 (GLP-1) analogs versus basal insulin and dipeptidyl peptidase-4 inhibitors (DPP-4i) versus sulfonylurea. METHODS: In the UK Clinical Practice Research Datalink, patients with type 2 diabetes initiating treatment between 2006 and 2015 were included. Analyses were stratified by years since first prescription of GLP-1 and DPP-4i, respectively. The characteristics of GLP-1 versus insulin and DPP-4i versus sulfonylurea initiators were compared over time. After propensity score matching, the relative effectiveness regarding 6-month changes in glycated hemoglobin (HbA1c) and body weight was estimated. RESULTS: In total, 8,398 GLP-1, 14,807 insulin, 24,481 DPP-4i, and 33,505 sulfonylurea initiators were identified. No major channeling was observed. Considerable overlap in distributions of characteristics allowed for propensity score-matched analyses. Relative effectiveness was similar across time. The overall relative effect of GLP-1 versus insulin showed no difference for HbA1c and relative increase in body weight (3.57 kg [95% confidence interval {CI}: 3.21, 3.92]) for insulin. The overall relative effect of DPP-4i versus sulfonylurea showed relative decrease in HbA1c (-0.34% [95% CI: -0.38, -0.30]) and increase in body weight (1.58 kg [95% CI: 1.38, 1.78]) for sulfonylurea. CONCLUSION: No major channeling was identified in the investigated glucose-lowering drugs. Relative effectiveness could be estimated already in the first year after launch and was consistent in the years thereafter.

A systematic literature review on the efficacy-effectiveness gap: comparison of randomized controlled trials and observational studies of glucose-lowering drugs.

AIM: To identify a potential efficacy-effectiveness gap and possible explanations (drivers of effectiveness) for differences between results of randomized controlled trials (RCTs) and observational studies investigating glucose-lowering drugs. METHODS: A systematic literature review was conducted in English language articles published between 1 January, 2000 and 31 January, 2015 describing either RCTs or observational studies comparing glucagon-like peptide-1 analogs (GLP-1) with insulin or comparing dipeptidyl peptidase-4 inhibitors (DPP-4i) with sulfonylurea, all with change in glycated hemoglobin (HbA1c) as outcome. Medline, Embase, Current Content, and Biosis were searched. Information on effect estimates, baseline characteristics of the study population, publication year, study duration, and number of patients, and for observational studies, characteristics related to confounding adjustment and selection- and information bias were extracted. RESULTS: From 312 hits, 11 RCTs and 7 observational studies comparing GLP-1 with insulin, and from 474 hits, 16 RCTs and 4 observational studies comparing DPP-4i with sulfonylurea were finally included. No differences were observed in baseline characteristics of the study populations (age, sex, body mass index, time since diagnosis of type 2 diabetes mellitus, and HbA1c) or effect sizes across study designs. Mean effect sizes ranged from -0.43 to 0.91 and from -0.80 to 1.13 in RCTs and observational studies, respectively, comparing GLP-1 with insulin, and from -0.13 to 2.70 and -0.20 to 0.30 in RCTs and observational studies, respectively, comparing DPP-4i and sulfonylurea. Generally, the identified observational studies held potential flaws with regard to confounding adjustment and selection- and information bias. CONCLUSIONS: Neither potential drivers of effectiveness nor an efficacy-effectiveness gap were identified. However, the limited number of studies and potential problems with confounding adjustment, selection- and information bias in the observational studies, may have hidden a true efficacy-effectiveness gap.

The cohort multiple randomized controlled trial design: a valid and efficient alternative to pragmatic trials?

Randomized controlled trials (RCTs)-the gold standard for evaluating the effects of medical interventions-are notoriously challenging in terms of logistics, planning and costs. The cohort multiple randomized controlled trial approach is designed to facilitate randomized trials for pragmatic evaluation of (new) interventions and is a promising variation from conventional pragmatic RCTs. In this paper, we evaluate methodological challenges of conducting an RCT within a cohort. We argue that equally valid results can be obtained from trials conducted within cohorts as from pragmatic RCTs. However, whether this design is more efficient compared with conducting a pragmatic RCT depends on the amount and nature of non-compliance in the intervention arm.

Best (but oft-forgotten) practices: propensity score methods in clinical nutrition research.

In observational studies, treatment assignment is a nonrandom process and treatment groups may not be comparable in their baseline characteristics, a phenomenon known as confounding. Propensity score (PS) methods can be used to achieve comparability of treated and nontreated groups in terms of their observed covariates and, as such, control for confounding in estimating treatment effects. In this article, we provide a step-by-step guidance on how to use PS methods. For illustrative purposes, we used simulated data based on an observational study of the relation between oral nutritional supplementation and hospital length of stay. We focused on the key aspects of PS analysis, including covariate selection, PS estimation, covariate balance assessment, treatment effect estimation, and reporting. PS matching, stratification, covariate adjustment, and weighting are discussed. R codes and example data are provided to show the different steps in a PS analysis.

Do self-management interventions in COPD patients work and which patients benefit most? An individual patient data meta-analysis.

BACKGROUND: Self-management interventions are considered effective in patients with COPD, but trials have shown inconsistent results and it is unknown which patients benefit most. This study aimed to summarize the evidence on effectiveness of self-management interventions and identify subgroups of COPD patients who benefit most. METHODS: Randomized trials of self-management interventions between 1985 and 2013 were identified through a systematic literature search. Individual patient data of selected studies were requested from principal investigators and analyzed in an individual patient data meta-analysis using generalized mixed effects models. RESULTS: Fourteen trials representing 3,282 patients were included. Self-management interventions improved health-related quality of life at 12 months (standardized mean difference 0.08, 95% confidence interval [CI] 0.00-0.16) and time to first respiratory-related hospitalization (hazard ratio 0.79, 95% CI 0.66-0.94) and all-cause hospitalization (hazard ratio 0.80, 95% CI 0.69-0.90), but had no effect on mortality. Prespecified subgroup analyses showed that interventions were more effective in males (6-month COPD-related hospitalization: interaction P=0.006), patients with severe lung function (6-month all-cause hospitalization: interaction P=0.016), moderate self-efficacy (12-month COPD-related hospitalization: interaction P=0.036), and high body mass index (6-month COPD-related hospitalization: interaction P=0.028 and 6-month mortality: interaction P=0.026). In none of these subgroups, a consistent effect was shown on all relevant outcomes. CONCLUSION: Self-management interventions exert positive effects in patients with COPD on respiratory-related and all-cause hospitalizations and modest effects on 12-month health-related quality of life, supporting the implementation of self-management strategies in clinical practice. Benefits seem similar across the subgroups studied and limiting self-management interventions to specific patient subgroups cannot be recommended.