Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.Comment: Published in at http://dx.doi.org/10.1214/14-STS499 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The impact of a postgraduate learning experience on the confidence of General Dental Practitioners

This study aimed to explore the relationship between participating in a learning experience and the ensuing changes in confidence. A self-selected group of General Dental Practitioners (GDPs) entered a five-year, part-time postgraduate master’s training programme in restorative dentistry. Confidence in communication with patients and technical skills were measured at the start of the programme by questionnaire and at the conclusion of the programme by questionnaire and personal interview. A total of 72 clinicians started the programme; 27% (n = 20) completed the master’s degree. Assessment of confidence revealed a spread from 4/10 to 10/10 for communication with patients and clinical skills in restorative dentistry before the programme started. A total of 15% (n = 11) volunteered for interview. Analysis of qualitative data revealed (i) a perceived increase in confidence from all clinicians; (ii) a perceived greater ability to treat patients; (iii) an increase in treatment options being offered to patients; (iv) a perceived increase in treatment uptake by patients; and (v) greater job opportunities. The study showed a positive relationship between the learning experience and the perceived increase in confidence of clinicians. The increase in confidence manifested itself in better communication and clinical skills

Practical recommendations for reporting Fine-Gray model analyses for competing risk data

In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause‐specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event‐free. Subdistribution hazard ratios obtained from the Fine‐Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine‐Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this model. An incorrect and inconsistent interpretation of regression coefficients may lead to confusion when comparing results across different studies. Furthermore, an incorrect interpretation of estimated regression coefficients can result in an incorrect understanding about the magnitude of the association between exposure and the incidence of the outcome. The objective of this article is to clarify how these regression coefficients should be reported and to propose suggestions for interpreting these coefficients

Accounting for competing risks in randomized controlled trials: a review and recommendations for improvement

In studies with survival or time-to-event outcomes, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Specialized statistical methods must be used to analyze survival data in the presence of competing risks. We conducted a review of randomized controlled trials with survival outcomes that were published in high-impact general medical journals. Of 40 studies that we identified, 31 (77.5%) were potentially susceptible to competing risks. However, in the majority of these studies, the potential presence of competing risks was not accounted for in the statistical analyses that were described. Of the 31 studies potentially susceptible to competing risks, 24 (77.4%) reported the results of a Kaplan-Meier survival analysis, while only five (16.1%) reported using cumulative incidence functions to estimate the incidence of the outcome over time in the presence of competing risks. The former approach will tend to result in an overestimate of the incidence of the outcome over time, while the latter approach will result in unbiased estimation of the incidence of the primary outcome over time. We provide recommendations on the analysis and reporting of randomized controlled trials with survival outcomes in the presence of competing risks. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd

Inducing Liquidity in Thin Financial Markets through Combined-Value Trading Mechanisms

Previous experimental research has shown that thin financial markets fail to fully equilibrate, in contrast with thick markets. A specific type of market risk is conjectured to be the reason, namely, the risk of partial execution of desired portfolio rearrangements in a system of parallel, unconnected double auction markets. This market risk causes liquidity to dry up before equilibrium is reached. To verify the conjecture, we organized markets directly as a portfolio trading mechanism, allowing agents to better coordinate their orders across securities. The mechanism is an implementation of the combined-value trading (CVT) system. We present evidence that our portfolio trading mechanism facilitates equilibration to the same extent as thick markets do. Like in thick markets, the emergence of equilibrium pricing cannot be attributed to chance. Inspection of order submission and trade activity reveals that subjects manage to exploit the direct linkages between markets presented by the CVT system

The use of picture cards to elicit postgraduate dental student feedback

The aim of this study was to elicit information about the use of picture cards to stimulate student feedback following a postgraduate dental course. Twenty-nine general dental practitioners (GDPs) volunteered for the study. Following an explanation of how this style of feedback worked, students were asked to choose a picture card from those available and use that image to stimulate their feedback. An independent interviewer introduced the topic and gathered comments in the form of qualitative data, generated by pre-formed questions. 93% (n = 26) questionnaires were completed and returned. 77% (n = 20) GDPs reported enjoying giving their feedback by ‘picture card’ technique; 83% (n = 20) reported this form of feedback stimulated their thoughts. Qualitative data were analysed thematically. Some GDPs felt the picture cards helped them formulate their feedback, others felt being able to give feedback to a third party they did not know was beneficial and giving feedback as a group was helpful. This novel approach of using picture cards to stimulate feedback was well received by these GDPs. A mixed result as to the value of this style of feedback was evident. A group feedback session facilitated by a stranger was considered to be a valuable approach to take

The number of primary events per variable affects estimation of the subdistribution hazard competing risks model

AbstractObjectivesTo examine the effect of the number of events per variable (EPV) on the accuracy of estimated regression coefficients, standard errors, empirical coverage rates of estimated confidence intervals, and empirical estimates of statistical power when using the Fine–Gray subdistribution hazard regression model to assess the effect of covariates on the incidence of events that occur over time in the presence of competing risks.Study Design and SettingMonte Carlo simulations were used. We considered two different definitions of the number of EPV. One included events of any type that occurred (both primary events and competing events), whereas the other included only the number of primary events that occurred.ResultsThe definition of EPV that included only the number of primary events was preferable to the alternative definition, as the number of competing events had minimal impact on estimation. In general, 40–50 EPV were necessary to ensure accurate estimation of regression coefficients and associated quantities. However, if all of the covariates are continuous or are binary with moderate prevalence, then 10 EPV are sufficient to ensure accurate estimation.ConclusionAnalysts must base the number of EPV on the number of primary events that occurred

Dental Teacher Feedback and Student Learning: A Qualitative Study

Feedback is essential to improve student learning and motivation and to encourage curriculum development by teachers. This study looked at feedback to and from dental students from a qualitative perspective. Methods: Dental teachers were recruited exclusively to this study from the membership of the Association for Dental Education in Europe (ADEE). Delegates from each of the four annual ADEE conferences were invited to attend focus groups to discuss aspects of feedback. Focus groups established an individual theme for the respective conferences: (i) the role of the teacher in delivering feedback; (ii) feedback from the students’ perspective; (iii) changes to feedback due to the COVID-19 pandemic; and (iv) integrating feedback with assessments. Results: Qualitative data collected from the conference delegates were diverse and thought provoking. Delegates reported different styles of feedback varying from individual, personal feedback to no feedback at all. An enforced and mostly positive adaptation to online delivery during the COVID-19 pandemic was reported. A partial return to pre-pandemic practices was described. Conclusions: Feedback is well recognized by students and teachers as contributing to learning. A universal approach to delivering feedback as part of the student learning process can be challenging due to a multitude of variables. Many aspects of changes in dental education, teaching, and feedback practices adopted as a result of the COVID-19 pandemic have been retained post-pandemic, thereby accelerating the anticipated progression to online teaching

Introduction to the Analysis of Survival Data in the Presence of Competing Risks

Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. The former family of models may be better suited for addressing etiologic questions, whereas the latter model may be better suited for estimating a patient’s clinical prognosis. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. Statistical software code in both R and SAS is provided

An investigation into the management of the deep carious lesion by general dental practitioners in the UK

Objectives: As techniques and dental materials have evolved, the management of deep carious lesions has also changed. This study investigated how UK-based general dental practitioners (GDPs) managed deep carious lesions in permanent, vital teeth and factors that influence their choices. / Methods: This mixed-method study employed an online questionnaire as its primary source of quantitative data collection. The questionnaire enquired about GDPs': a) demographics; b) working environment; and c) whether they had postgraduate training that covered caries management/minimally invasive dentistry (MID). Respondents were presented with a clinical case to elicit qualitative data. Relevant questions were asked in order to examine current practice and explore treatment among the respondents. / Results: In total, 239 responses were received. Overall, 168 (70% [95% CI 64%, 76%]) of the respondents chose a partial caries removal technique, 155 (69.3% [95% CI 60%, 72%]) used an adhesive restorative material and 205 (85.8% [95% CI 81%, 89%]) advised fluoride adjuncts. However, rubber dam (75; 31.4% [95% CI 26%, 38%]) and saliva testing (17; 7.1% [95% CI 4%, 11%]) were not routinely used. A significant relationship between those who had postgraduate training and those who chose partial caries removal as their treatment choice (χ2 = 6.27; p = 0.01) was noted. Respondents working in an NHS-based practice were significantly (χ2 = 34.98; p <0.001) more likely to restore teeth with amalgam. / Conclusions: There is an inconsistent management protocol when presented with a deep carious lesion, but partial caries removal is more widely adopted than previously reported. Rubber dam isolation was not routinely used when choosing to restore a deep carious lesion. Those who have had postgraduate training felt more confident in offering MID, so there is a clear need for further education to ensure its engagement