How to read and interpret the results of a Bayesian network meta-analysis: a short tutorial

Integration of artificial intelligence in lung cancer: Rise of the machine

The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.

Maximizing value and minimizing waste in clinical trial research in swine: Selecting interventions to build an evidence base

Researchers conduct a trial to compare an intervention of interest to a comparison group. Initially, researchers should determine whether a trial is evaluating superiority, equivalence, or noninferiority. This decision will guide the choice of a placebo versus active comparison group. Interventions, as well as baseline management, should be comprehensively reported to allow replication or clinical application. It is necessary to build a body of evidence across multiple trials to apply evidence-based decision-making. To achieve this, at least one intervention in every trial should be an intervention that has been used in at least one previously published trial.

Levels of Evidence, Quality Assessment, and Risk of Bias: Evaluating the Internal Validity of Primary Research

Clinical decisions in human and veterinary medicine should be based on the best available evidence. The results of primary research are an important component of that evidence base. Regardless of whether assessing studies for clinical case management, developing clinical practice guidelines, or performing systematic reviews, evidence from primary research should be evaluated for internal validity i.e., whether the results are free from bias (reflect the truth). Three broad approaches to evaluating internal validity are available: evaluating the potential for bias in a body of literature based on the study designs employed (levels of evidence), evaluating whether key study design features associated with the potential for bias were employed (quality assessment), and applying a judgement as to whether design elements of a study were likely to result in biased results given the specific context of the study (risk of bias assessment). The level of evidence framework for assessing internal validity assumes that internal validity can be determined based on the study design alone, and thus makes the strongest assumptions. Risk of bias assessments involve an evaluation of the potential for bias in the context of a specific study, and thus involve the least assumptions about internal validity. Quality assessment sits somewhere between the assumptions of these two. Because risk of bias assessment involves the least assumptions, this approach should be used to assess internal validity where possible. However, risk of bias instruments are not available for all study designs, some clinical questions may be addressed using multiple study designs, and some instruments that include an evaluation of internal validity also include additional components (e.g., evaluation of comprehensiveness of reporting, assessments of feasibility or an evaluation of external validity). Therefore, it may be necessary to embed questions related to risk of bias within existing quality assessment instruments. In this article, we overview the approaches to evaluating internal validity, highlight the current complexities, and propose ideas for approaching assessments of internal validity.

Chemotherapy and radiotherapy in locally advanced head and neck cancer: an individual patient data network meta-analysis

BACKGROUND

Randomised, controlled trials and meta-analyses have shown the survival benefit of concomitant chemoradiotherapy or hyperfractionated radiotherapy in the treatment of locally advanced head and neck cancer. However, the relative efficacy of these treatments is unknown. We aimed to determine whether one treatment was superior to the other.

METHODS

We did a frequentist network meta-analysis based on individual patient data of meta-analyses evaluating the role of chemotherapy (Meta-Analysis of Chemotherapy in Head and Neck Cancer [MACH-NC]) and of altered fractionation radiotherapy (Meta-Analysis of Radiotherapy in Carcinomas of Head and Neck [MARCH]). Randomised, controlled trials that enrolled patients with non-metastatic head and neck squamous cell cancer between Jan 1, 1980, and Dec 31, 2016, were included. We used a two-step random-effects approach, and the log-rank test, stratified by trial to compare treatments, with locoregional therapy as the reference. Overall survival was the primary endpoint. The global Cochran Q statistic was used to assess homogeneity and consistency and P score to rank treatments (higher scores indicate more effective therapies).

FINDINGS

115 randomised, controlled trials, which enrolled patients between Jan 1, 1980, and April 30, 2012, yielded 154 comparisons (28 978 patients with 19 253 deaths and 20 579 progression events). Treatments were grouped into 16 modalities, for which 35 types of direct comparisons were available. Median follow-up based on all trials was 6·6 years (IQR 5·0-9·4). Hyperfractionated radiotherapy with concomitant chemotherapy (HFCRT) was ranked as the best treatment for overall survival (P score 97%; hazard ratio 0·63 [95% CI 0·51-0·77] compared with locoregional therapy). The hazard ratio of HFCRT compared with locoregional therapy with concomitant chemoradiotherapy with platinum-based chemotherapy (CLRT_P) was 0·82 (95% CI 0·66-1·01) for overall survival. The superiority of HFCRT was robust to sensitivity analyses. Three other modalities of treatment had a better P score, but not a significantly better HR, for overall survival than CLRT_P (P score 78%): induction chemotherapy with taxane, cisplatin, and fluorouracil followed by locoregional therapy (IC_TaxPF-LRT; 89%), accelerated radiotherapy with concomitant chemotherapy (82%), and IC_TaxPF followed by CLRT (80%).

INTERPRETATION

The results of this network meta-analysis suggest that further intensifying chemoradiotherapy, using HFCRT or IC_TaxPF-CLRT, could improve outcomes over chemoradiotherapy for the treatment of locally advanced head and neck cancer.

FUNDINGS

French Institut National du Cancer, French Ligue Nationale Contre le Cancer, and Fondation ARC.

Editorial: Systematic reviews reveal a need for more, better data to inform antimicrobial stewardship practices in animal agriculture

This editorial summarizes the key observations from a special issue of Animal Health Research Reviews comprising 14 articles related to the efficacy of antimicrobial and non-antimicrobial approaches to reduce disease in beef, dairy cattle, swine, and broiler chickens. The articles used evidence-based methods, including scoping reviews, systematic reviews, meta-analyses, and network meta-analyses. Despite finding evidence of efficacy for some of the interventions examined, across the body of research, there was a lack of replication and inconsistency in outcomes among the included trials, and concerns related to completeness of reporting and trial design and execution. There is an urgent need for more and better data to inform antimicrobial stewardship practices in animal agriculture.

How to Conduct a Bayesian Network Meta-Analysis

Network meta-analysis is a general approach to integrate the results of multiple studies in which multiple treatments are compared, often in a pairwise manner. In this tutorial, we illustrate the procedures for conducting a network meta-analysis for binary outcomes data in the Bayesian framework using example data. Our goal is to describe the workflow of such an analysis and to explain how to generate informative results such as ranking plots and treatment risk posterior distribution plots. The R code used to conduct a network meta-analysis in the Bayesian setting is provided at GitHub.