This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Clinical relevance and statistical significance are different concepts, linked via the sample size calculation. Threshold values for detecting a minimal important change over time are frequently (mis)interpreted as a threshold for the clinical relevance of a difference between groups. The magnitude of a difference between groups that is considered clinically relevant directly impacts the sample size calculation, and thereby the statistical significance in clinical study outcomes. Especially in non-inferiority trials the threshold for clinical relevance, i.e. the predefined margin for non-inferiority, is a crucial choice. A truly inferior treatment will be accepted as non-inferior when this margin is chosen too large. The magnitude of a clinically relevant difference between groups should be carefully considered, by determining the smallest effect for each specific study that is considered worthwhile. This means taking into account the (dis)advantages of both study interventions in terms of benefits, harms, costs, and potential side effects. This article clarifies common sources of confusion, illustrates the implications for clinical research with an example and provides specific suggestions to improve the design and interpretation of clinical research.

The term \"statistical significance,\" ubiquitous in the medical literature, is often misinterpreted, as is the \"p-value\" from which it stems. This article explores the implications of results that are numerically positive (e.g., those in the treatment arm do better on average) but not statistically significant. This lack of statistical significance is sometimes interpreted as strong, even decisive, evidence against an effect without due consideration of other factors. Three influential articles on hydroxychloroquine (HCQ) as a treatment for COVID-19 are illustrative. They all involve numerically positive results that were not statistically significant that were misinterpreted as strong evidence against HCQ\'s efficacy. These and related considerations raise concerns regarding the reliability of academic/medical reasoning around COVID-19 treatments, as well as more generally, and regarding the potential for bias stemming from conflicts of interest.

We apply a general case replacement framework for quantifying the robustness of causal inferences to characterize the uncertainty of findings from clinical trials.
We express the robustness of inferences as the amount of data that must be replaced to change the conclusion and relate this to the fragility of trial results used for dichotomous outcomes. We illustrate our approach in the context of an RCT of hydroxychloroquine on pneumonia in COVID-19 patients and a cumulative meta-analysis of the effect of antihypertensive treatments on stroke.
We developed the Robustness of an Inference to Replacement (RIR), which quantifies how many treatment cases with positive outcomes would have to be replaced with hypothetical patients who did not receive a treatment to change an inference. The RIR addresses known limitations of the Fragility Index by accounting for the observed rates of outcomes. It can be used for varying thresholds for inference, including clinical importance.
Because the RIR expresses uncertainty in terms of patient experiences, it is more relatable to stakeholders than P-values alone. It helps identify when results are statistically significant, but conclusions are not robust, while considering the rareness of events in the underlying data.