The reporting of statistics in medical educational studies: an observational study

<p>Abstract</p> <p>Background</p> <p>There is confusion in the medical literature as to whether statistics should be reported in survey studies that query an entire population, as is often done in educational studies. Our objective was to determine how often statistical tests have been reported in such articles in two prominent journals that publish these types of studies.</p> <p>Methods</p> <p>For this observational study, we used electronic searching to identify all survey studies published in <it>Academic Medicine </it>and the <it>Journal of General Internal Medicine </it>in which an entire population was studied. We tallied whether inferential statistics were used and whether p-values were reported.</p> <p>Results</p> <p>Eighty-four articles were found: 62 in <it>Academic Medicine </it>and 22 in the <it>Journal of General Internal Medicine</it>. Overall, 38 (45%) of the articles reported or stated that they calculated statistics: 35% in <it>Academic Medicine </it>and 73% in the <it>Journal of General Internal Medicine</it>.</p> <p>Conclusion</p> <p>Educational enumeration surveys frequently report statistical tests. Until a better case can be made for doing so, a simple rule can be proffered to researchers. When studying an entire population (e.g., all program directors, all deans, and all medical schools) for factual information, do not perform statistical tests. Reporting percentages is sufficient and proper.</p

A Machine Learning Trainable Model to Assess the Accuracy of Probabilistic Record Linkage

Record linkage (RL) is the process of identifying and linking data that relates to the same physical entity across multiple heterogeneous data sources. Deterministic linkage methods rely on the presence of common uniquely identifying attributes across all sources while probabilistic approaches use non-unique attributes and calculates similarity indexes for pair wise comparisons. A key component of record linkage is accuracy assessment — the process of manually verifying and validating matched pairs to further refine linkage parameters and increase its overall effectiveness. This process however is time-consuming and impractical when applied to large administrative data sources where millions of records must be linked. Additionally, it is potentially biased as the gold standard used is often the reviewer’s intuition. In this paper, we present an approach for assessing and refining the accuracy of probabilistic linkage based on different supervised machine learning methods (decision trees, naïve Bayes, logistic regression, random forest, linear support vector machines and gradient boosted trees). We used data sets extracted from huge Brazilian socioeconomic and public health care data sources. These models were evaluated using receiver operating characteristic plots, sensitivity, specificity and positive predictive values collected from a 10-fold cross-validation method. Results show that logistic regression outperforms other classifiers and enables the creation of a generalized, very accurate model to validate linkage results

Imatinib administration in two patients with liver metastases from GIST and severe jaundice

Imatinib is the only effective and approved systemic therapy for the treatment of patients with advanced gastrointestinal stromal tumours (GISTs). Although metastases from GISTs most commonly involve the liver, yielding hyperbilirubinaemia, very few data on imatinib administration in subjects with jaundice are available. We provide evidence that imatinib tolerability was not adversely affected by jaundice in two patients with advanced GIST

Multiple imputation approaches for epoch-level accelerometer data in trials.

Clinical trials that investigate physical activity interventions often use accelerometers to measure step count at a very granular level, for example in 5-second epochs. Participants typically wear the accelerometer for a week-long period at baseline, and for one or more week-long follow-up periods after the intervention. The data is aggregated to provide daily or weekly step counts for the primary analysis. Missing data are common as participants may not wear the device as per protocol. Approaches to handling missing data in the literature have defined missingness on the day level using a threshold on daily weartime, which leads to loss of information on the time of day when data are missing. We propose an approach to identifying and classifying missingness at the finer epoch-level and present two approaches to handling missingness using multiple imputation. Firstly, we present a parametric approach which accounts for the number of missing epochs per day. Secondly, we describe a non-parametric approach where missing periods during the day are replaced by donor data from the same person where possible, or data from a different person who is matched on demographic and physical activity-related variables. Our simulation studies show that the non-parametric approach leads to estimates of the effect of treatment that are least biased while maintaining small standard errors. We illustrate the application of these different multiple imputation strategies to the analysis of the 2017 PACE-UP trial. The proposed framework is likely to be applicable to other digital health outcomes and to other wearable devices

A framework for handling missing accelerometer outcome data in trials.

Accelerometers and other wearable devices are increasingly being used in clinical trials to provide an objective measure of the impact of an intervention on physical activity. Missing data are ubiquitous in this setting, typically for one of two reasons: patients may not wear the device as per protocol, and/or the device may fail to collect data (e.g. flat battery, water damage). However, it is not always possible to distinguish whether the participant stopped wearing the device, or if the participant is wearing the device but staying still. Further, a lack of consensus in the literature on how to aggregate the data before analysis (hourly, daily, weekly) leads to a lack of consensus in how to define a "missing" outcome. Different trials have adopted different definitions (ranging from having insufficient step counts in a day, through to missing a certain number of days in a week). We propose an analysis framework that uses wear time to define missingness on the epoch and day level, and propose a multiple imputation approach, at the day level, which treats partially observed daily step counts as right censored. This flexible approach allows the inclusion of auxiliary variables, and is consistent with almost all the primary analysis models described in the literature, and readily allows sensitivity analysis (to the missing at random assumption) to be performed. Having presented our framework, we illustrate its application to the analysis of the 2019 MOVE-IT trial of motivational interviewing to increase exercise

The COMET Handbook: version 1.0

The selection of appropriate outcomes is crucial when designing clinical trials in order to compare the effects of different interventions directly. For the findings to influence policy and practice, the outcomes need to be relevant and important to key stakeholders including patients and the public, health care professionals and others making decisions about health care. It is now widely acknowledged that insufficient attention has been paid to the choice of outcomes measured in clinical trials. Researchers are increasingly addressing this issue through the development and use of a core outcome set, an agreed standardised collection of outcomes which should be measured and reported, as a minimum, in all trials for a specific clinical area. Accumulating work in this area has identified the need for guidance on the development, implementation, evaluation and updating of core outcome sets. This Handbook, developed by the COMET Initiative, brings together current thinking and methodological research regarding those issues. We recommend a four-step process to develop a core outcome set. The aim is to update the contents of the Handbook as further research is identified

Long-term Energy Supply Contracts in European Competition Policy: Fuzzy not Crazy

Long-term supply contracts often have ambiguous effects on the competitive structure, investment and consumer welfare in the long term. In a context of market building, these effects are likely to be worsened and thus even harder to assess. Since liberalization and especially since the release of the Energy Sector Enquiry in early 2007, the portfolio of long-term supply contracts of the former incumbents have become a priority for review by the European Commission and the national competition authorities. It is widely believed that European Competition authorities take a dogmatic view on these contracts and systemically emphasize the risk of foreclosure over their positive effects on investment and operation. This paper depicts the methodology that has emerged in the recent line of cases and argues that this interpretation is largely misguided. It shows that a multiple-step approach is used to reduce regulation costs and balance anti-competitive effects with potential efficiency gains. However, if an economic approach is now clearly implemented, competition policy is constrained by the procedural aspect of the legal process and the remedies imposed remain open for discussion.Massachusetts Institute of Technology. Center for Energy and Environmental Policy Research

The LKB1-salt-inducible kinase pathway functions as a key gluconeogenic suppressor in the liver

LKB1 is a master kinase that regulates metabolism and growth through adenosine monophosphate-activated protein kinase (AMPK) and 12 other closely related kinases. Liver-specific ablation of LKB1 causes increased glucose production in hepatocytes in vitro and hyperglycaemia in fasting mice in vivo. Here we report that the salt-inducible kinases (SIK1, 2 and 3), members of the AMPK-related kinase family, play a key role as gluconeogenic suppressors downstream of LKB1 in the liver. The selective SIK inhibitor HG-9-91-01 promotes dephosphorylation of transcriptional co-activators CRTC2/3 resulting in enhanced gluconeogenic gene expression and glucose production in hepatocytes, an effect that is abolished when an HG-9-91-01-insensitive mutant SIK is introduced or LKB1 is ablated. Although SIK2 was proposed as a key regulator of insulin-mediated suppression of gluconeogenesis, we provide genetic evidence that liver-specific ablation of SIK2 alone has no effect on gluconeogenesis and insulin does not modulate SIK2 phosphorylation or activity. Collectively, we demonstrate that the LKB1-SIK pathway functions as a key gluconeogenic gatekeeper in the liver