Stroke reduction by cerebral embolic protection devices in transcatheter aortic valve implantation: a systematic review and Bayesian meta-analysis

OBJECTIVES

The use of cerebral embolic protection (CEP) during transcatheter aortic valve implantation (TAVI) has been studied in several randomised trials. We aimed to perform a systematic review and Bayesian meta-analysis of randomised CEP trials, focusing on a clinically relevant reduction in disabling stroke.

METHODS

A systematic search was applied to three electronic databases, including trials that randomised TAVI patients to CEP versus standard treatment. The primary outcome was the risk of disabling stroke. Outcomes were presented as relative risk (RR), absolute risk differences (ARDs), numbers needed to treat (NNTs) and the 95% credible intervals (CrIs). The minimal clinically important difference was determined at 1.1% ARD, per expert consensus (NNT 91). The principal Bayesian meta-analysis was performed under a vague prior, and secondary analyses were performed under two informed literature-based priors.

RESULTS

Seven randomised studies were included for meta-analysis (n=3996: CEP n=2126, control n=1870). Under a vague prior, the estimated median RR of CEP use for disabling stroke was 0.56 (95% CrI 0.28 to 1.19, derived ARD 0.56% and NNT 179, I2=0%). Although the estimated posterior probability of any benefit was 94.4%, the probability of a clinically relevant effect was 0-0.1% under the vague and informed literature-based priors. Results were robust across multiple sensitivity analyses.

CONCLUSION

There is a high probability of a beneficial CEP treatment effect, but this is unlikely to be clinically relevant. These findings suggest that future trials should focus on identifying TAVI patients with an increased baseline risk of stroke, and on the development of new generation devices.

PROSPERO REGISTRATION NUMBER

CRD42023407006.

Analysis of core outcome set reporting in coronary intervention trials

BACKGROUND

This paper will focus on outcome reporting within percutaneous coronary intervention (PCI) trials. A core outcome set (COS) is a standardised set of outcomes that are recommended to be reported in every clinical trial. Using a COS can help to ensure that all relevant outcomes are consistently reported across clinical trials. In 2018, the European Society of Cardiology outlined the only COS published for PCI trials.

METHODS

We searched the literature for all randomised controlled trials published between 2014 and 2022. PCI trials included were late-phase trials and must investigate coronary intervention. The primary outcome was the proportion of trials that reported all of the COS-defined outcomes within their publication as either a primary, secondary or safety endpoint. The secondary outcomes included; the number of primary outcomes reported per study, the proportion of studies which use patient and public involvement (PPI) during trial design, outcome variability and outcome consistency.

RESULTS

9580 trials were screened and 115 studies met inclusion/exclusion criteria. Our study demonstrated that 55% (34/62) of PCI trials used a COS when it was available, compared with 40% (21/53) before the availability of a PCI COS set, p=0.121. Fewer primary outcomes were reported after the implementation of the COS, 2 compared with 2.3, p=0.014. There was no difference in the use of PPI between either group. There was a higher level of variability in outcomes reported before the availability of the COS, while the consistency of outcome reporting remained similar.

CONCLUSION

The use of a COS in PCI trials is low. This study provides evidence that there still is a lack of awareness of the COS among those who design clinical trials. We also presented the inconsistency and heterogenicity in reporting clinical trial outcomes. Finally, there was a clear lack of PPI utilisation in PCI trials.

Enhancing biostatistics education for medical students in Poland: factors influencing perception and educational recommendations

BACKGROUND

A number of recommendations for the teaching of biostatistics have been published to date, however, student opinion on them has not yet been studied. For this reason, the aim of the manuscript was to find out the opinions of medical students at universities in Poland on two forms of teaching biostatistics, namely traditional and practical, as well as to indicate, on the basis of the results obtained, the related educational recommendations.

METHODS

The study involved a group of 527 students studying at seven medical faculties in Poland, who were asked to imagine two different courses. The traditional form of teaching biostatistics was based on the standard teaching scheme of running a test from memory in a statistical package, while the practical one involved reading an article in which a particular test was applied and then applying it based on the instruction provided. Other aspects related to the teaching of the subject were assessed.

RESULTS

According to the students of each course, the practical form of teaching biostatistics reduces the stress level associated with teaching and the student exam (p < 0.001), as well as contributing to an increased level of elevated knowledge (p < 0.001), while the degree of satisfaction after passing the exam is higher (p < 0.001). A greater proportion of students (p < 0.001) believe that credit for the course could be given by doing a statistical review of an article or conducting a survey, followed by the tests learned in class. More than 95% also said that the delivery of the courses should be based on the field of study they were taking, during which time they would also like to have the opportunity to take part in optional activities and hear lectures from experts.

CONCLUSION

It is recommended that more emphasis be placed on practical teaching the subject of biostatistics.

Incorporating equity, diversity, and inclusion into the epidemiology and biostatistics curriculum: A workshop report and implementation strategies recommendations

There is an obligation among those teaching epidemiology to incorporate principles of equity, diversity, and inclusion (EDI) into the curriculum. While there is a well-established literature related to teaching epidemiology, this literature rarely addresses critical aspects of EDI. To our knowledge, there is no working group or central point of discussion and learning for incorporating EDI into epidemiology teaching in Canada. To address this gap, we convened a workshop entitled "Incorporating EDI into the epidemiology and biostatistics curriculum and classroom." The workshop discussed nine strategies to incorporate EDI in the epidemiology curriculum: positionality (or reflexivity) statements; opportunities for feedback; land acknowledgements; clarifying the purpose of collecting data on race and ethnicity, sex and gender, Indigeneity; acknowledging that race/ethnicity is a social construct, not a biological variable; describing incidence and prevalence of disease; demonstrating explicit bias using directed acyclic graphs (DAGs); critical appraisal of study population diversity; and admission criteria and considerations. Key take-aways from the workshop were the need to be more intentional when determining the validity of evidence, particularly with respect to historical context and the need to recognize that there is no single solution that will address EDI.

Basic Statistics, Statistical Design, and Critical Analysis of Statistics for Surgeons

Statistics is a set of tools used in medical decision-making no different than how a scalpel or a sagittal saw is used in the operating room. No foot and ankle surgeon is born with the inherent ability to perform, understand, and critically interpret them. Instead, it requires training and practice throughout the course of a career in medicine to develop a working proficiency. This article reviews the basic indications and interpretation of common descriptive and comparative statistical tests in the podiatric literature. Additionally, the concept of which tests are most appropriate for which investigational methodologies is introduced.

A Review of Meta-Analyses in Plastic Surgery: Need for Adequate Assessment of Publication Bias

INTRODUCTION

Publication bias describes a phenomenon in which significant positive results have a higher likelihood of being published compared to negative or nonsignificant results. Publication bias can confound the estimated therapeutic effect in meta-analyses and needs to be adequately assessed in the surgical literature.

METHODS

A review of meta-analyses published in five plastic surgery journals from 2002 to 2022 was conducted. The inclusion criteria for meta-analyses were factors that demonstrated an obligation to assess publication bias, such as interventions with comparable treatment groups and enough power for statistical analysis. Acknowledgment of publication bias risk, quality of bias assessment, methods used in assessment, and individual article factors were analyzed.

RESULTS

318 unique meta-analyses were identified in literature search, and after full-text reviews, 143 met the inclusion criteria for obligation to assess publication bias. 64% of eligible meta-analyses acknowledged the confounding potential of publication bias, and only 46% conducted a formal assessment. Of those who conducted an assessment, 49% used subjective inspection of funnel plots alone, while 47% used any statistical testing in analysis. Overall, only 9/143 (6.3%) assessed publication bias and attempted to correct for its effect. Journals with a higher average impact factor were associated with mention and assessment of publication bias, but more recent publication year and higher number of primary articles analyzed were not.

CONCLUSIONS

This review identified low rates of proper publication bias assessment in meta-analyses published in five major plastic surgery journals. Assessment of publication bias using objective statistical testing is necessary to ensure quality literature within surgical disciplines.

Just how transformative will AI/ML be for immuno-oncology?

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

Looking beyond the mean: quantile regression for comparative physiologists

Statistical analyses that physiologists use to test hypotheses predominantly centre on means, but the tail ends of the response distribution can behave quite differently and underpin important scientific phenomena. We demonstrate that quantile regression (QR) offers a way to bypass some limitations of least squares regression (LSR) by building a picture of independent variable effects across the whole distribution of a dependent variable. We used LSR and QR with simulated and real datasets. With simulated data, LSR showed no change in the mean response but missed significant effects in the tails of the distribution found using QR. With real data, LSR showed a significant change in the mean response but missed a lack of response in the upper quantiles which was biologically revealing. Together, this highlights that QR can help to ask and answer more questions about variation in nature.

Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, apply primarily to Europe, the USA, China and other technologically developed nations. Africa is yet to leverage the potential applications of AI and ML within the medical space. Sub-Saharan African countries are currently disadvantaged economically and infrastructure-wise. Yet precisely, these circumstances create significant opportunities for the deployment of medical AI, which has already been deployed in some places in the continent. However, while AI and ML have come with enormous promises in Africa, there are still challenges when it comes to successfully applying AI and ML designed elsewhere within the African context, especially in diagnosing mental health disorders. We argue, in this paper, that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders. Our claim is grounded on the premise that mental health disorders cannot be diagnosed solely on 'factual evidence' but on both factual evidence and value-laden judgements of what constitutes mental health disorders in sub-Saharan Africa. For ML to play a successful role in diagnosing mental health disorders in sub-Saharan African medical spaces, with a precise focus on South Africa, we allude that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions.

Genome-Related Mechanisms Contributing to Differences in Alzheimer's Disease Incidence Between White and Black Older US Adults

In this manuscript, we leverage a modified GWAS algorithm adapted for use with multidimensional Cox models and data from the Health and Retirement Study to explore how genetic variation influences the size of the disparity in Alzheimer's disease (AD) incidence between older Black and White US adults. We identified four loci that were associated with higher AD incidence levels in older Black adults: (1) rs11077034 (hazard ratio (HR), 4.98) from the RBFOX1 gene; (2) rs7144494 (HR, 1.68) from the HISLA gene; (3) rs7660552 (HR, 3.07) from the SLC25A4 gene; and (4) rs12599485 (HR, 3.181) from the NIP30 gene. The RBFOX1, HISLA, SLC25A4, and NIP30 genes are known to be associated with AD (RBFOX1, NIP30) directly, and also influence the risk of AD risk-related morbidities such as hypertension (RBFOX1, SLC25A4), depression (SLC25A4), and certain cancers (HISLA, SLC25A4). A likely disparity-generating mechanism may lie in endocytosis and abnormal tissue growing mechanisms, depending on inherited gene mutations and the structure of proxies as well as gene-environment interactions with other risk factors such as lifestyle, education level, and access to adequate medical services.

Data Distribution: Normal or Abnormal?

Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

Multiomics profiling reveals the benefits of gamma-delta (γδ) T lymphocytes for improving the tumor microenvironment, immunotherapy efficacy and prognosis in cervical cancer

BACKGROUND

As an unconventional subpopulation of T lymphocytes, γδ T cells can recognize antigens independently of major histocompatibility complex restrictions. Recent studies have indicated that γδ T cells play contrasting roles in tumor microenvironments-promoting tumor progression in some cancers (eg, gallbladder and leukemia) while suppressing it in others (eg, lung and gastric). γδ T cells are mainly enriched in peripheral mucosal tissues. As the cervix is a mucosa-rich tissue, the role of γδ T cells in cervical cancer warrants further investigation.

METHODS

We employed a multiomics strategy that integrated abundant data from single-cell and bulk transcriptome sequencing, whole exome sequencing, genotyping array, immunohistochemistry, and MRI.

RESULTS

Heterogeneity was observed in the level of γδ T-cell infiltration in cervical cancer tissues, mainly associated with the tumor somatic mutational landscape. Definitely, γδ T cells play a beneficial role in the prognosis of patients with cervical cancer. First, γδ T cells exert direct cytotoxic effects in the tumor microenvironment of cervical cancer through the dynamic evolution of cellular states at both poles. Second, higher levels of γδ T-cell infiltration also shape the microenvironment of immune activation with cancer-suppressive properties. We found that these intricate features can be observed by MRI-based radiomics models to non-invasively assess γδ T-cell proportions in tumor tissues in patients. Importantly, patients with high infiltration levels of γδ T cells may be more amenable to immunotherapies including immune checkpoint inhibitors and autologous tumor-infiltrating lymphocyte therapies, than to chemoradiotherapy.

CONCLUSIONS

γδ T cells play a beneficial role in antitumor immunity in cervical cancer. The abundance of γδ T cells in cervical cancerous tissue is associated with higher response rates to immunotherapy.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Peer review of clinical and translational research manuscripts: Perspectives from statistical collaborators

Research articles in the clinical and translational science literature commonly use quantitative data to inform evaluation of interventions, learn about the etiology of disease, or develop methods for diagnostic testing or risk prediction of future events. The peer review process must evaluate the methodology used therein, including use of quantitative statistical methods. In this manuscript, we provide guidance for peer reviewers tasked with assessing quantitative methodology, intended to complement guidelines and recommendations that exist for manuscript authors. We describe components of clinical and translational science research manuscripts that require assessment including study design and hypothesis evaluation, sampling and data acquisition, interventions (for studies that include an intervention), measurement of data, statistical analysis methods, presentation of the study results, and interpretation of the study results. For each component, we describe what reviewers should look for and assess; how reviewers should provide helpful comments for fixable errors or omissions; and how reviewers should communicate uncorrectable and irreparable errors. We then discuss the critical concepts of transparency and acceptance/revision guidelines when communicating with responsible journal editors.

The eye of the beholder: how do public health researchers interpret regression coefficients? A qualitative study

BACKGROUND

Calls for improved statistical literacy and transparency in population health research are widespread, but empirical accounts describing how researchers understand statistical methods are lacking. To address this gap, this study aimed to explore variation in researchers' interpretations and understanding of regression coefficients, and the extent to which these statistics are viewed as straightforward statements about health.

METHODS

Thematic analysis of qualitative data from 45 one-to-one interviews with academics from eight countries, representing 12 disciplines. Three concepts from the sociology of scientific knowledge and science studies aided analysis: Duhem's Paradox, the Agonistic Field, and Mechanical Objectivity.

RESULTS

Some interviewees viewed regression as a process of discovering 'real' relationships, while others indicated that regression models are not direct representations, and others blended these perspectives. Regression coefficients were generally not viewed as being mechanically objective, instead interpretation was described as iterative, nuanced, and sometimes depending on prior understandings. Researchers reported considering numerous factors when interpreting and evaluating regression results, including: knowledge from outside the model, whether results are expected or unexpected, 'common-sense', technical limitations, study design, the influence of the researcher, the research question, data quality and data availability. Interviewees repeatedly highlighted the role of the analyst, reinforcing that it is researchers who answer questions and assign meaning, not models.

CONCLUSIONS

Regression coefficients were generally not viewed as complete or authoritative statements about health. This contrasts with teaching materials wherein statistical results are presented as straightforward representations, subject to rule-based interpretations. In practice, it appears that regression coefficients are not understood as mechanically objective. Attempts to influence conduct and presentation of regression models in the population health sciences should be attuned to the myriad factors which inform their interpretation.

Efficacy and limitations of ChatGPT as a biostatistical problem-solving tool in medical education in Serbia: a descriptive study

PURPOSE

This study aimed to assess the performance of ChatGPT (GPT-3.5 and GPT-4) as a study tool in solving biostatistical problems and to identify any potential drawbacks that might arise from using ChatGPT in medical education, particularly in solving practical biostatistical problems.

METHODS

ChatGPT was tested to evaluate its ability to solve biostatistical problems from the Handbook of Medical Statistics by Peacock and Peacock in this descriptive study. Tables from the problems were transformed into textual questions. Ten biostatistical problems were randomly chosen and used as text-based input for conversation with ChatGPT (versions 3.5 and 4).

RESULTS

GPT-3.5 solved 5 practical problems in the first attempt, related to categorical data, cross-sectional study, measuring reliability, probability properties, and the t-test. GPT-3.5 failed to provide correct answers regarding analysis of variance, the chi-square test, and sample size within 3 attempts. GPT-4 also solved a task related to the confidence interval in the first attempt and solved all questions within 3 attempts, with precise guidance and monitoring.

CONCLUSION

The assessment of both versions of ChatGPT performance in 10 biostatistical problems revealed that GPT-3.5 and 4’s performance was below average, with correct response rates of 5 and 6 out of 10 on the first attempt. GPT-4 succeeded in providing all correct answers within 3 attempts. These findings indicate that students must be aware that this tool, even when providing and calculating different statistical analyses, can be wrong, and they should be aware of ChatGPT’s limitations and be careful when incorporating this model into medical education.

Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group

BACKGROUND

The advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group.

METHODS

This work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE.

RESULTS

Using our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses.

CONCLUSIONS

This new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.

Use of Pearson and Spearman correlation testing in Indian anesthesia journals: An audit

Background and Aims

Correct usage and interpretation of biostatistical tests is imperative. Aim of the present article was to evaluate the use of "correlation test" for biostatistical analysis in two leading Indian journals of anesthesia and sensitize the readers regarding its correct usage.

Material and Methods

A prospective analysis was done for all original articles using the correlation test (Pearson or Spearman) that were published in "Indian Journal of Anaesthesia" (IJA) or "Journal of Anaesthesiology and Clinical Pharmacology" (JOACP) in the years 2019 and 2020.

Results

Amongst all included original studies, correlation test were used in 6% (JOACP) and 6.5% (IJA) respectively (averaged for the years 2019 and 2020). Correlation test was usedinappropriately) for evaluating an aim of prediction/agreement/comparison, rather than association, in 25% and 10% instances each (JOACP and IJA). In both JOACP and IJA, there were high rates of using and interpreting results without citing 95% confidence intervals (CIs) of correlation coefficient (88% and 90%, respectively), P value for significance of the association (50% and 90%, respectively), or coefficient of discrimination (88% and 70%, respectively). In majority of the instances, test to ascertain presence of mandatory prerequisites such as normal distribution of data could not be found (62% and 90%, respectively).

Conclusion

The complete potential of correlation test in exploring research questions is probably underappreciated. Further, even when used, its application and interpretation are prone to errors. We hope that the present analysis and narrative is a well-timed appropriate step in bridging the gaps in existing knowledge regarding use of correlation test in national anesthesia literature.

Radiosensitivity Index is Not Fit to be Used for Dose Adjustments: A Pan-Cancer Analysis

AIMS

To explore the preclinical and latest clinical evidence of the radiation sensitivity signature termed 'radiosensitivity index' (RSI), to assess its suitability as an input into dose-adjustment algorithms.

MATERIALS AND METHODS

The original preclinical test-set data from the publication where RSI was derived were collected and reanalysed by comparing the observed versus predicted survival fraction at 2 Gy (SF2). In addition, the predictive capability of RSI was also compared to random guessing. Clinical data were collected from a recently published dataset that included RSI values, overall survival outcomes, radiotherapy dose and tumour site for six cancers (glioma, triple-negative breast, endometrial, melanoma, pancreatic and lung cancer). Cox proportional hazards models were used to assess: (i) does adjusting for RSI elucidate a dose response and (ii) does an interaction between RSI and dose exist with good precision.

RESULTS

Preclinically, RSI showed a negative correlation (Spearman's rho = -0.61) between observed and predicted SF2, which remained negative after removing leukaemia cell lines. Furthermore, random guesses showed better correlation to SF2 than RSI, 98% of the time on the full dataset and 80% after removing leukaemia cell lines. The preclinical data show that RSI does not explain the variance in SF2 better than random guessing. Clinically, a dose response was not seen after adjusting for RSI (hazard ratio = 1.00, 95% confidence interval 0.97-1.04; P = 0.876) and no evidence of an interaction between RSI and dose was found (P = 0.844).

CONCLUSIONS

These results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.

Improving rigor and reproducibility in western blot experiments with the blotRig analysis software

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

Machine learning for prediction of immunotherapeutic outcome in non-small-cell lung cancer based on circulating cytokine signatures

BACKGROUND

Immune checkpoint inhibitor (ICI) therapy has substantially improved the overall survival (OS) in patients with non-small-cell lung cancer (NSCLC); however, its response rate is still modest. In this study, we developed a machine learning-based platform, namely the Cytokine-based ICI Response Index (CIRI), to predict the ICI response of patients with NSCLC based on the peripheral blood cytokine profiles.

METHODS

We enrolled 123 and 99 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy or combined chemotherapy in the training and validation cohorts, respectively. The plasma concentrations of 93 cytokines were examined in the peripheral blood obtained from patients at baseline (pre) and 6 weeks after treatment (early during treatment: edt). Ensemble learning random survival forest classifiers were developed to select feature cytokines and predict the OS of patients undergoing ICI therapy.

RESULTS

Fourteen and 19 cytokines at baseline and on treatment, respectively, were selected to generate CIRI models (namely preCIRI14 and edtCIRI19), both of which successfully identified patients with worse OS in two completely independent cohorts. At the population level, the prediction accuracies of preCIRI14 and edtCIRI19, as indicated by the concordance indices (C-indices), were 0.700 and 0.751 in the validation cohort, respectively. At the individual level, patients with higher CIRI scores demonstrated worse OS [hazard ratio (HR): 0.274 and 0.163, and p<0.0001 and p=0.0044 in preCIRI14 and edtCIRI19, respectively]. By including other circulating and clinical features, improved prediction efficacy was observed in advanced models (preCIRI21 and edtCIRI27). The C-indices in the validation cohort were 0.764 and 0.757, respectively, whereas the HRs of preCIRI21 and edtCIRI27 were 0.141 (p<0.0001) and 0.158 (p=0.038), respectively.

CONCLUSIONS

The CIRI model is highly accurate and reproducible in determining the patients with NSCLC who would benefit from anti-PD-1/PD-L1 therapy with prolonged OS and may aid in clinical decision-making before and/or at the early stage of treatment.

An electrophysiological substrate of COVID-19

SARS-CoV-2 infection is associated with an increased risk of late cardiovascular (CV) outcomes. However, more data is needed to describe the electrophysiologic (EP) manifestation of post-acute CV sequelae of COVID-19. We compared two cohorts of adult patients with SARS-CoV-2 polymerase chain reaction (PCR) test and an electrocardiogram (ECG) performed between March 1, 2020, and September 13, 2020, in a retrospective double-cohort study, "Cardiovascular Risk Stratification in Covid-19" (CaVaR-Co19; NCT04555187). Patients with positive PCR comprised a COVID-19(+) cohort (n = 41; 61% women; 80% symptomatic), whereas patients with negative tests formed the COVID-19(-) cohort (n = 155; 56% women). In longitudinal analysis, comparing 3 ECGs recorded before, during, and on average 40 days after index COVID-19 episode, after adjustment for demographic and socioeconomic characteristics, baseline CV risk factors and comorbidities, use of prescription medications (including QT-prolonging drugs) before and during index COVID-19 episode, and the longitudinal changes in RR' intervals, heart rhythm, and ventricular conduction type, only in the COVID-19(+) cohort QTc increased by +30.2(95% confidence interval [CI] 0.1-60.3) ms and the spatial ventricular gradient (SVG) elevation increased by +13.5(95%CI 1.2-25.9)°. In contrast, much smaller, statistically nonsignificant changes were observed in the COVID-19(-) cohort. In conclusion, post-acute CV sequelae of SARS-CoV-2 infection manifested on ECG by QTc prolongation and rotation of the SVG vector upward.

Discrimination between Precancerous Gastric Lesions and Gastritis Using a Gastric Cancer Risk Stratification Model

BACKGROUND

Seropositivity to certain Helicobacter pylori proteins may affect development of gastric lesions that could become cancerous. Previously, we developed a model of gastric cancer risk including gender, age, HP0305 sero-positivity, HP1564 sero-positivity, UreA antibody titer and serologically defined chronic atrophic gastritis (termed: "Lasso model").

METHODS

We evaluated the Lasso model's ability to discriminate individuals with precancerous gastric lesions (n=320) from individuals with superficial or mild atrophic gastritis (n=226) in Linqu County, China, a population at high risk for gastric cancer. We also compared its performance to the ABC Method, a gastric cancer risk stratification tool currently used in East Asia.

RESULTS

For distinguishing precancerous lesions from those with gastritis, the receiver operating characteristic curve had an area under the curve (AUC) of 73.41% (95% CI: 69.10%, 77.71%) and, at Youden's Index, a sensitivity of 78.44% (59.38%, 82.50%) and specificity of 64.72% (95% CI: 58.85%, 81.42%). Positive predictive value (PPV) was 75.38% (72.78%, 82.51%). Specificity, AUC and PPV were significantly greater (p < 0.05) than those of the ABC Method. When specificity was held constant, the Lasso model had greater sensitivity, PPV and negative predictive value (NPV) than the ABC Method. However, adjusting the ABC Method for age and gender negated the Lasso model's significant improvement in AUC.

CONCLUSIONS

The Lasso model for gastric cancer risk prediction can classify precancerous lesions with significantly greater AUC than the ABC Method and, at constant specificity, with greater sensitivity, PPV and NPV. However, adding age and gender to the ABC Method, as included in the Lasso model, substantially improved its performance and negated the Lasso model's advantage.

Deconstructing the Kaplan-Meier curve: Quantification of treatment effect using the treatment effect process

In studies of survival and its association with treatment and other prognostic variables, elapsed time alone will often show itself to be among the strongest, if not the strongest, of the predictor variables. Kaplan-Meier curves will show the overall survival of each group and the general differences between groups due to treatment. However, the time-dependent nature of treatment effects is not always immediately transparent from these curves. More sophisticated tools are needed to spotlight the treatment effects. An important tool in this context is the treatment effect process. This tool can be potent in revealing the complex myriad of ways in which treatment can affect survival time. We look at a recently published study in which the outcome was relapse-free survival, and we illustrate how the use of the treatment effect process can provide a much deeper understanding of the relationship between time and treatment in this trial.

Randomized Controlled Trials in Pediatric Cardiology: A Power Struggle?

Sample size and statistical power are often limited in pediatric cardiology studies due to the relative infrequency of specific congenital malformations of the heart and specific circulatory physiologies. The primary aim of this study was to determine what proportion of pediatric cardiology randomized controlled trials achieve an 80% statistical power. Secondary aims included characterizing reporting habits in these studies. A systematic review was performed to identify pertinent pediatric cardiology randomized controlled trials. The following data were collected: publication year, journal, if "power" or "sample size" were mentioned if a discrete, primary endpoint was identified. Power analyses were conducted to assess if the sample size was adequate to demonstrate results at 80% power with a p-value of less than 0.05. A total of 83 pediatric cardiology randomized controlled trials were included. Of these studies, 48% mentioned "power" or "sample size" in the methods, 49% mentioned either in the results, 12% mentioned either in the discussion, and 66% mentioned either at any point in the manuscript. 63% defined a discrete, primary endpoint. 38 studies (45%) had an adequate sample size to demonstrate differences with 80% power at a p-value of less than 0.05. A majority of these are not powered to reach the conventionally accepted 80% power target. Adequately powered studies were found to be more likely to report "power" or "sample size" and have a discrete, primary endpoint.

Master of Science in Medical Statistics Programme at Universiti Sains Malaysia: 20 Years Ongoing

Health and medical research are important parts of the curriculum of medical and health programmes in universities and play an important role in the functioning of organisations related to health care. There is a shortage of well-trained health and medical research statisticians. This article describes the courses and structure of the Master of Science in Medical Statistics programme at Universiti Sains Malaysia (USM), as well as the graduates' achievements. It is a 2-year programme that prepares qualified and competent graduates in statistical methods and data analysis for research in health and medical sciences. The Biostatistics and Research Methodology Unit, School of Medical Sciences, USM has been running the programme since 2003. It is currently the only medical statistics programme available in Malaysia. There have been 97 graduates since 2005, with an employment rate of 96.7% and a successful subsequent doctorate rate of 21.1%. Most of the students returned to their previous employments, mainly with the Ministry of Health of Malaysia and several others became lecturers, statisticians or research officers. The employability of graduates from this programme is very high and their professional future is bright. We hope our graduates will impart their knowledge and skills to the nation.

Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology

Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.

Applying patient-reported outcome measures (PROMs) in physiotherapy: an evaluation based on the QUALITOUCH Activity Index

BACKGROUND

Patient-reported outcome measures (PROMs) are tools to screen a population, to monitor the subjective progress of a therapy, to enable patient-centred care and to evaluate the quality of care. The QUALITOUCH Activity Index (AI) is such a tool, used in physiotherapy. This study aimed to provide reference values for expected AI outcomes.

METHODS

A large data set uniting clinical routine data and AI outcomes was generated; it consisted of data of 11,948 patients. For four defined diagnoses, i.e. chronic lower back pain, tibia posterior syndrome, knee joint osteoarthritis and shoulder impingement, the AI responses related to the dimensions "maximum pain level" and "household activity" were analyzed. Reference corridors for expected AI outcomes were derived as linear trend lines representing the mean, 1st and 3rd quartile.

RESULTS

Reference corridors for expected AI outcomes are provided. For chronic lower back pain, for example, the corridor indicates that the initial average AI value related to maximum pain of 49.3 ± 23.8 points on a visual analogue scale (VAS multiplied by factor 10) should be improved by a therapeutic intervention to 36.9 ± 23.8 points on a first follow-up after four weeks.

CONCLUSIONS

For four exemplary diagnoses and two dimensions of the AI, one related to pain and one related to limitations in daily activities, reference corridors of expected therapeutic progress were established. These reference corridors can be used to compare an individual performance of a patient with the expected progress derived from a large data sample. Data-based monitoring of therapeutic success can assist in different aspects of planning and managing a therapy.

Imaging endpoints for clinical trial use: a RECIST perspective

Twenty years after its initial introduction, Response Evaluation Criteria in Solid Tumors (RECIST) remains today a unique standardized tool allowing uniform objective evaluation of response in solid tumors in clinical trials across different treatment indications. Several attempts have been made to update or replace RECIST, but none have realized the general traction or uptake seen with RECIST. This communication provides an overview of some challenges faced by RECIST in the rapidly changing oncology landscape, including the incorporation of PET with ¹⁸F-fluorodeoxyglucose tracer as a tool for response assessment and the validation of criteria for use in trials involving immunotherapeutics. The latter has mainly been slow due to lack of data sharing. Work is ongoing to try to address this.We also aim to share our view as statistician representatives on the RECIST Working Group on what would be needed to validate new imaging endpoints for clinical trial use, with a specific focus on RECIST. Whether this could lead to an update of RECIST or replace RECIST altogether, depends on the changes being proposed. The ultimate goal remains to have a well defined, repeatable, confirmable and objective standard as provided by RECIST today.

Summer pollen flora in rural and urban central England dominated by nettle, ryegrass and other pollen missed by the national aerobiological network

Abundance and diversity of airborne pollen are important to human health and biodiversity. The UK operational network collects airborne pollen from 8 flowering trees, grasses and three weeds using Hirst traps and microscopic identification from urban areas. Knowledge of total pollen diversity and differences between rural and urban zones is limited. We collect environmental DNA (eDNA) from air during summer and autumn over 3 years with mini cyclones from one urban and one rural site. Data are analysed using next generation sequencing and metabarcoding. We find the most common genus, Urtica (57%), is also identified by the national network. The grasses Lolium (10%), Agrostis (2%) and Holcus (1%) are in the national network grouped at family level, while Brassica (2%), Chenopodium (1%), Impatiens (2%), Plantago (4%) and Tilia (7%) are not part of the UK operational network. DNA from 138 genera was identified, where 2% of the sample could not be associated with specific genera. 40% of the sample was classified better using eDNA methods at the genus level, than by optical methods. We calculate Bray-Curtis dissimilarity for the rural and urban zones and find a systematic difference in biodiversity. Overall, this shows airborne DNA reveals more information than methods based on morphological differences. The results also suggest data from sites located in large urban areas will be less representative for less populated rural areas. This presents a dilemma in balancing a network and the associated costs delivering health relevant information to the most populated areas vs. a nation-wide approach.

Post-hoc power analysis: a conceptually valid approach for power based on observed study data

Power analysis is a key component of planning prospective studies such as clinical trials. However, some journals in biomedical and psychosocial sciences request power analysis for data already collected and analysed before accepting manuscripts for publication. Many have raised concerns about the conceptual basis for such post-hoc power analyses. More recently, Zhang et al showed by using simulation studies that such power analyses do not indicate true power for detecting statistical significance since post-hoc power estimates vary in the range of practical interests and can be very different from the true power. On the other hand, journals’ request for information about the reliability of statistical findings in a manuscript due to small sample sizes is justified since the sample size plays an important role in the reproducibility of statistical findings. The problem is the wording of the journals' request, as the current power analysis paradigm is not designed to address journals’ concerns about the reliability of the statistical findings. In this paper, we propose an alternate formulation of power analysis to provide a conceptually valid approach to the journals’ wrongly worded but practically significant concern.

Development of a curriculum integrating biostatistics and study design with core sciences in an organ system block

INTRODUCTION

The objectives of this study were to develop and evaluate a curriculum that integrated biostatistics and research design content with core sciences content within a pharmacy course.

METHODS

An inquiry curriculum was developed in 2019 and included lectures on biostatistics and research design with small group discussions of clinical research papers directly related to the core sciences content. Students' perceptions and pass rates between students who did (2019 cohort) and did not (2018 cohort) undergo the curriculum were compared. Test scores taken approximately one year after completion of each cohort's course were also compared.

RESULTS

Of 127 students in the 2019 cohort, 120 (94%) responded. Over 90% agreed or strongly agreed that inquiry and core sciences contents were integrated well. The 2019 cohort had a significantly higher pass rate than the 2018 cohort on two of three assessment questions evaluated: one multiple choice question (P = .037) and one short answer question (P = .013). After adjustments for baseline characteristics, retention study volunteers from the 2019 cohort had a significantly higher percent test score than those from the 2018 cohort (parameter estimate = 8.48%; P = .026).

CONCLUSIONS

An inquiry curriculum consisting of select biostatistics and research design topics can be integrated with a core sciences curriculum in a large integrated pharmacy course. Inclusion of this content increased student academic performance and retention of knowledge and skills.

Development of a 5-year risk prediction model for type 2 diabetes in individuals with incident HbA1c-defined pre-diabetes in Denmark

INTRODUCTION

Pre-diabetes increases the risk of type 2 diabetes, but data are sparse on predictors in a population-based clinical setting. We aimed to develop and validate prediction models for 5-year risks of progressing to type 2 diabetes among individuals with incident HbA1c-defined pre-diabetes.

RESEARCH DESIGN AND METHODS

In this population-based cohort study, we used data from the Danish National Health Survey (DNHS; n=486 495), linked to healthcare registries and nationwide laboratory data in 2012-2018. We included individuals with a first HbA1c value of 42-47 mmol/mol (6.0%-6.4%), without prior indications of diabetes. To estimate individual 5-year cumulative incidences of type 2 diabetes (HbA1c ≥48 mmol/mol (6.5%)), Fine-Gray survival models were fitted in random 80% development samples and validated in 20% validation samples. Potential predictors were HbA1c, demographics, prescriptions, comorbidities, socioeconomic factors, and self-rated lifestyle.

RESULTS

Among 335 297 (68.9%) participants in DNHS with HbA1c measurements, 26 007 had pre-diabetes and were included in the study. Median HbA1c was 43.0 mmol/mol (IQR 42.0-44.0 mmol/mol, 6.1% (IQR 6.0%-6.2%)), median age was 69.6 years (IQR 61.0-77.1 years), and 51.9% were women. During a median follow-up of 2.7 years, 11.8% progressed to type 2 diabetes and 10.1% died. The final prediction model included HbA1c, age, sex, body mass index (BMI), any antihypertensive drug use, pancreatic disease, cancer, self-reported diet, doctor's advice to lose weight or change dietary habits, having someone to talk to, and self-rated health. In the validation sample, the 5-year area under the curve was 72.7 (95% CI 71.2 to 74.3), and the model was well calibrated.

CONCLUSIONS

In addition to well-known pre-diabetes predictors such as age, sex, and BMI, we found that measures of self-rated lifestyle, health, and social support are important and modifiable predictors for diabetes. Our model had an acceptable discriminative ability and was well calibrated.

Inter-arm blood pressure difference as a tool for predicting coronary artery disease severity

Background

Patients with severe atherosclerosis have been found to exhibit considerable changes in blood pressure (BP) between arms. The objective of our study was to investigate the predictive value of interarm blood pressure difference (IABPD) for coronary artery disease (CAD) severity.

Methods

It was a cross-sectional study conducted in the Department of Cardiology, Chittagong Medical College Hospital, Chattogram from May 2020 to November 2020. The study conveniently selected 110 individuals who visited the department for a coronary angiography during the study period. The BP of both arms were synchronously measured 1–2 days before the coronary angiography and IABPD were calculated. After coronary angiography, two blinded interventional cardiologists visually estimated the amount of coronary artery obstruction and determined the Gensini score.

Results

Among the participants, more than three-fourths of the patients were above 50 years of age (64.66%), and the majority were male (86.67%). 14.7% of participants had no occlusion in their coronary artery, 38.0% of participants had insignificant occlusion, 26.7% participants had mild occlusion, 10.3% participants had moderate occlusion, 3.3% participants had significant occlusion and 6.0% participants had total occlusion. Corrected pulse IABPD (cIABPDpulse) showed the greatest area under the receiver operating characteristic curve (0.73) for predicting a high Gensini score (>median). Multiple regression analysis revealed a significant relationship between corrected systolic IABPD (cIABPDsys) and the Gensini score (B=0.057, p<0.001).

Conclusion

The differences in BP between the arms were found to be having a strong positive correlation with CAD severity.

How to write statistical analysis section in medical research

Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of future scientific studies, including meta-analyses and medical decisions. Although the biostatistical practice has been improved over the years due to the involvement of statistical reviewers and collaborators in research studies, there remain areas of improvement for transparent reporting of the statistical analysis section in a study. Evidence-based biostatistics practice throughout the research is useful for generating reliable data and translating meaningful data to meaningful interpretation and decisions in medical research. Most existing research reporting guidelines do not provide guidance for reporting methods in the statistical analysis section that helps in evaluating the quality of findings and data interpretation. In this report, we highlight the global and critical steps to be reported in the statistical analysis of grants and research articles. We provide clarity and the importance of understanding study objective types, data generation process, effect size use, evidence-based biostatistical methods use, and development of statistical models through several thematic frameworks. We also provide published examples of adherence or non-adherence to methodological standards related to each step in the statistical analysis and their implications. We believe the suggestions provided in this report can have far-reaching implications for education and strengthening the quality of statistical reporting and biostatistical practice in medical research.

Statistical Analysis in the German National Cohort (NAKO) - Specific Aspects and General Recommendations

The German National Cohort (NAKO) is an ongoing, prospective multicenter cohort study, which started recruitment in 2014 and includes more than 205,000 women and men aged 19-74 years. The study data will be available to the global research community for analyses. Although the ultimate decision about the analytic methods will be made by the respective investigator, in this paper we provide the basis for a harmonized approach to the statistical analyses in the NAKO. We discuss specific aspects of the study (e.g., data collection, weighting to account for the sampling design), but also give general recommendations which may apply to other large cohort studies as well.

Difficulties experienced by endodontics researchers in conducting studies and writing papers

Objectives

The study investigated the difficulties experienced by endodontics researchers around the world in conducting studies and writing papers.

Materials and Methods

A survey consisting of 18 questions on the difficulties experienced by endodontics researchers in performing studies and writing papers was e-mailed to academics in the field of endodontics working at 202 universities. The independent risk factors were analyzed using binary logistic regression at a significance level of 0.05.

Results

A total of 581 individuals (10.7%) agreed to participate in the study. Almost half the participants (48.2%) reported that they had received some type of training in conducting studies and writing papers. In response to the question, “Do you get help from a statistician to perform the statistical analyses of your studies?,” 77.1% answered “yes.” Around 40% of the participants stated that the need to obtain ethical approval negatively affected their desire to conduct studies. The participants’ regions had no effect on the reported difficulties associated with writing papers in English or conducting statistical analyses (p > 0.05). Most participants (81.8%) reported difficulties in writing the Discussion section, regardless of their region, academic degrees, or years of experience.

Conclusions

The participants stated they experienced difficulties in many areas, such as conducting statistical analyses, finding new ideas, and writing in English. Engaging in a detailed examination of ethics committee rules, expanding biostatistics education, increasing the number of institutions providing research funding, and increasing the number of endodontics journals can increase the enthusiasm of endodontics researchers to publish papers.

Mortality after acute kidney injury and acute interstitial nephritis in patients prescribed immune checkpoint inhibitor therapy

BACKGROUND

In patients receiving immune checkpoint inhibitor (ICI) therapy, acute kidney injury (AKI) is common, and can occur either from kidney injury unrelated to ICI use or from immune activation resulting in acute interstitial nephritis (AIN). In this study, we test the hypothesis that occurrence of AIN indicates a favorable treatment response to ICI therapy and therefore among patients who develop AKI while on ICI therapy, those with AIN will demonstrate greater survival compared with others with AKI.

METHODS

In this observational cohort study, we included participants initiated on ICI therapy between 2013 and 2019. We tested the independent association of AKI and estimated AIN (eAIN) with mortality up to 1 year after therapy initiation as compared with those without AKI using time-varying Cox proportional hazard models controlling for demographics, comorbidities, cancer type, stage, and therapy, and baseline laboratory values. We defined eAIN as those with a predicted probability of AIN >90th percentile derived from a recently validated diagnostic model.

RESULTS

Of 2207 patients initiated on ICIs, 617 (28%) died at 1 year and 549 (25%) developed AKI. AKI was independently associated with higher mortality (adjusted HR, 2.28 (95% CI 1.90 to 2.72)). Those AKI patients with eAIN had more severe AKI as reflected by a higher peak serum creatinine (3.3 (IQR 2.1-6.1) vs 1.4 (1.2-1.9) mg/dL, p<0.001) but exhibited lower mortality than those without eAIN in univariable analysis (HR 0.43 (95% CI 0.21 to 0.89)) and after adjusting for demographics, comorbidities, and cancer type and severity (adjusted HR 0.44 (95% CI 0.21 to 0.93)).

CONCLUSION

In patients treated with ICI, mortality was higher in those with AKI unrelated to ICI but lower in those where the underlying etiology was AIN. Future studies could evaluate the association of biopsy-proven or biomarker-proven AIN with mortality in those receiving ICI therapy.

Statistical recomendations for the authors of manuscripts submitted to the Journal of Cancer Research and Clinical Oncology

In recent years, a negative picture of statistical analyses carried out in medicine has been observed around the world. Unfortunately, as it turns out, this also applies to COVID-19. The most important guidelines for the members of the readers and authors of articles submitted to the Journal of Cancer Research and Clinical Oncology, i.e., on numerous factors related to the statistical analysis, are presented.

Quantitative methods for descriptive intersectional analysis with binary health outcomes

Intersectionality recognizes that in the context of sociohistorically shaped structural power relations, an individual's multiple social positions or identities (e.g., gender, ethnicity) can interact to affect health-related outcomes. Despite limited methodological guidance, intersectionality frameworks have increasingly been incorporated into epidemiological studies, both to describe health disparities and to examine their causes. This study aimed to advance methods in intersectional estimation of binary outcomes in descriptive health disparities research through evaluation of 7 potentially intersectional data analysis methods: cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision trees (CART, CTree, CHAID, random forest). Accuracy of estimated intersection-specific outcome prevalence was evaluated across 192 intersections using simulated data scenarios. For comparison we included a non-intersectional main effects regression. We additionally assessed variable selection performance amongst decision trees. Example analyses using National Health and Nutrition Examination Study data illustrated differences in results between methods. At larger sample sizes, all methods except for CART performed better than non-intersectional main effects regression. In smaller samples, MAIHDA was the most accurate method but showed no advantage over main effects regression, while random forest, cross-classification, and saturated regression were the least accurate, and CTree and CHAID performed moderately well. CART performed poorly for estimation and variable selection. Sensitivity analyses examining the bias-variance tradeoff suggest MAIHDA as the preferred unbiased method for accurate estimation of high-dimensional intersections at smaller sample sizes. Larger sample sizes are more imperative for other methods. Results support the adoption of an intersectional approach to descriptive epidemiology.

Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve

BACKGROUND

Advances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFR_CT).

PURPOSE

To assess the interoperator and intraoperator variability of coronary CT angiography-derived FFR_CT using a machine learning-based postprocessing prototype.

MATERIALS AND METHODS

We included 60 symptomatic patients who underwent coronary CT angiography. FFR_CT was calculated by two independent operators after training using a machine learning-based on-site prototype. FFR_CT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate interoperator variability effect in FFR_CT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.

RESULTS

A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFR_CT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 to 0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 to 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared with proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis.

CONCLUSION

A high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFR_CT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFR_CT.

Modelling the association between biomarkers and clinical outcome: an introduction to nonlinear joint models

Nonlinear joint models are a powerful tool to precisely analyze the association between a nonlinear biomarker and a time-to-event process, such as death. Here, we review the main methodological techniques required to build these models and to make inferences and predictions. We describe the main clinical applications and discuss the future developments of such models.

A Comparison of Confounder Selection and Adjustment Methods for Estimating Causal Effects Using Large Healthcare Databases

PURPOSE

Confounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challenges. The objective was to evaluate the capacity of such methods to select confounders and reduce unmeasured confounding bias.

METHODS

A simulation study with known true effects was conducted. Completely synthetic and partially synthetic data incorporating real large healthcare data were generated. We compared Bayesian Adjustment for Confounding, Generalized Bayesian Causal Effect Estimation, Group Lasso and Doubly Robust Estimation, high-dimensional propensity score, and scalable collaborative targeted maximum likelihood algorithms. For the high-dimensional propensity score, two adjustment approaches targeting the effect in the whole population were considered: full matching and inverse probability weighting.

RESULTS

In scenarios without hidden confounders, most methods were essentially unbiased. The bias and variance of the high-dimensional propensity score varied considerably according to the number of variables selected by the algorithm. In scenarios with hidden confounders, substantial bias reduction was achieved by using machine learning methods to identify proxies as compared to adjusting only by observed confounders. High-dimensional propensity score and Group Lasso performed poorly in the partially synthetic simulation. Bayesian Adjustment for Confounding, Generalized Bayesian Causal Effect Estimation, and scalable collaborative targeted maximum likelihood algorithms performed particularly well.

CONCLUSIONS

Machine learning can help to identify measured confounders in large healthcare databases. They can also capitalize on proxies of unmeasured confounders to substantially reduce residual confounding bias. This article is protected by copyright. All rights reserved.

Prediction of post-traumatic neuropathy following impacted mandibular third molar removal

OBJECTIVES

The extraction of impacted mandibular third molars is a common surgical procedure often associated with complications including post-traumatic neuropathy. Previous work has focused on identifying confounding factors, but a robust preoperative risk prediction model remains elusive.

METHODS

Using a dataset of 648 patients and 812 impacted mandibular third molars, we used least absolute shrinkage and selection operator (LASSO) to fit prediction models based on risk factors assessed at both the tooth and patient levels. In addition, we fitted multivariable logistic regression models with the Firth correction for generalized estimating equations (GEE).

RESULTS

The LASSO model for post-traumatic neuropathy identified distoangular impaction of ≥ 45° (odds ratio [OR] = 2.9), proximity to the inferior alveolar nerve of ≤ 3 mm (OR = 1.9), disadvantageous curving (OR = 1.4), and psychiatric conditions (OR = 2.1) as predictors [area under the receiving operator characteristic curve (AUC) = 0.75]. Among other complications analyzed, the LASSO model for bleeding identified deep embedding or full impaction (OR = 1.8), psychiatric conditions (OR = 1.3), and age (OR = 0.9) as predictors (AUC = 0.64). These associations between predictors and postoperative complications were fundamentally reinforced by the corresponding GEE models.

CONCLUSIONS

Our findings point to the predictability of post-traumatic neuropathy and bleeding based on tooth anatomy and patient characteristics, overall suggesting that preoperatively identifiable factors can predict the risk of adverse outcomes in the extraction of impacted mandibular third molars.

CLINICAL SIGNIFICANCE

Mandibular third molar extraction is both a routine procedure and a leading cause of trigeminal neuropathy. Prevention of post-traumatic neuropathy, aided by individualized preoperative risk prediction, is of high clinical relevance.

Measuring the ecological preference for growth of 150 of the most influential weeds in weed community structure associated with agronomic and horticultural crops

The phytosociological researches which intent for studying the performance of weeds and the structure of weed assemblages associated with different crops derives their importance mainly from the adverse effect of weeds on crop productivity. Consequently, it is worth questioning about the ecological preferences of the weed growth in response to three main drivers for weed community structure associated with agronomic, and horticultural crops: crop diversification, crop seasonality, and soil type. A study area was selected comprising farmland of Nile Delta and its adjoining east and west territories, Egypt. A total of 555 species were recorded in 30 agroecosystems monitored and depending on species frequency/abundance values, 150 species were designated as the most influential weeds in weed community structure associated with agronomic and horticultural crops. The ecological preference of species for crop seasonality was evident through the results of Agglomerative hierarchical clustering. Three weed assemblage groups (WAG) identified: WAG A associated with winter agronomic crops, WAG B associated with summer agronomic crops, and WAG C associated with perennial agronomic crops and horticultural crops (orchards). Their diversity evaluated at different levels. The growth preference of the 150 species which were assigned as most influential weeds was gauged in response to the three environmental variables. 61 species were faithful to WAG A, 45 to WAG B, and 44 to WAG C. Concerning crop diversification, 34-species were significantly affected and scored coefficient of variation ≥ 100%. As for soil type, indicator species analysis revealed that 66-species show growth preference in fine grained soil while 84-species prefer coarse grained soil. In the three vegetation units (WAG A - C), 12 within-group associations (alliances) were specified of less-common (differential) species. The record of these alliances match to a specific environmental condition (ecological niche) and in them 29 strong indicators are identified. Redundancy analysis was used to extract and summarize the variation in species records in the response matrix (species vs. sites) that can be explained by the three different types of growth preference (explanatory variables), and the partial linear effect of them was evaluated by variation partitioning.

Seeing the Forest for the Trees: Evaluating Population Data in Allergy-Immunology

A population-level study is essential for understanding treatment effects, epidemiologic phenomena, and health care best practices. Evaluating large populations and associated data requires an analytic framework, which is commonly used by statisticians, epidemiologists, and data scientists. This document will serve to provide an overview of these commonly employed methods in allergy and immunology research. We will draw upon recent examples from the allergy-immunology literature to contextualize discrete principles of relevance to population-level analysis that include statistical features of a study population, elements of statistical inference, regression analysis, and an overview of machine learning practices. Our intent is to guide the reader through a practical description of this important quantitative discipline and facilitate greater understanding about data and result display in the medical literature.