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BACKGROUND: Cluster randomized trials (CRTs) are randomized trials where randomization takes place at an administrative level (e.g., hospitals, clinics, or schools) rather than at the individual level. When the number of available clusters is small, researchers may not be able to rely on simple randomization to achieve balance on cluster-level covariates across treatment conditions. If these cluster-level covariates are predictive of the outcome, covariate imbalance may distort treatment effects, threaten internal validity, lead to a loss of power, and increase the variability of treatment effects. Covariate-constrained randomization (CR) is a randomization strategy designed to reduce the risk of imbalance in cluster-level covariates when performing a CRT. Existing methods for CR have been developed and evaluated for two- and multi-arm CRTs but not for factorial CRTs.
METHODS: Motivated by the BEGIN study-a CRT for weight loss among patients with pre-diabetes-we develop methods for performing CR in 2 × 2 factorial cluster randomized trials with a continuous outcome and continuous cluster-level covariates. We apply our methods to the BEGIN study and use simulation to assess the performance of CR versus simple randomization for estimating treatment effects by varying the number of clusters, the degree to which clusters are associated with the outcome, the distribution of cluster level covariates, the size of the constrained randomization space, and analysis strategies.
RESULTS: Compared to simple randomization of clusters, CR in the factorial setting is effective at achieving balance across cluster-level covariates between treatment conditions and provides more precise inferences. When cluster-level covariates are included in the analyses model, CR also results in greater power to detect treatment effects, but power is low compared to unadjusted analyses when the number of clusters is small.
CONCLUSIONS: CR should be used instead of simple randomization when performing factorial CRTs to avoid highly imbalanced designs and to obtain more precise inferences. Except when there are a small number of clusters, cluster-level covariates should be included in the analysis model to increase power and maintain coverage and type 1 error rates at their nominal levels.

From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing-RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS-CoV-2 infection status and extensive study participant meta-data. This allowed us to rigorously assess state-of-the-art machine learning techniques to predict SARS-CoV-2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks.

OBJECTIVE: The prior event rate ratio is a recently developed approach for controlling confounding by measured and unmeasured covariates in real-world evidence research and observational studies. Despite its rising popularity in studies of safety and effectiveness of biopharmaceutical products, there is no guidance on how to empirically evaluate its model assumptions. We propose two methods to evaluate two of the assumptions required by the prior event rate ratio, specifically, the assumptions that occurrence of outcome events does not alter the likelihood of receiving treatment, and that earlier event rate does not affect later event rate.
METHODS: We propose using self-controlled case series (SCCS) and dynamic random intercept modelling (DRIM) respectively, to evaluate the two aforementioned assumptions. A non-mathematical introduction of the methods and their application to evaluate the assumptions are provided. We illustrate the evaluation with secondary analysis of de-identified data on pneumococcal vaccination and clinical pneumonia in The Gambia, West Africa.
RESULTS: SCCS analysis of data on 12,901 vaccinated Gambian infants did not reject the assumption of clinical pneumonia episodes had no influence on the likelihood of pneumococcal vaccination. DRIM analysis of 14,325 infants with a total of 1,719 episodes of clinical pneumonia did not reject the assumption of earlier episodes of clinical pneumonia had no influence on later incidence of the disease.
CONCLUSIONS: The SCCS and DRIM methods can facilitate appropriate use of the prior event rate ratio approach to control confounding.

OBJECTIVE: Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of quantitative bias analysis (QBA) methods for summary level data published in the peer-reviewed literature.
METHODS: We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was pre-registered on the Open Science Framework (https://osf.io/ue6vm/).
RESULTS: Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. 38 (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. 22 (39%) articles provided code or online tools to implement the QBA methods.
CONCLUSIONS: In this systematic review, we identified a total of 57 QBA methods for summary level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary level epidemiologic data.

The test-negative design (TND) is a popular method for evaluating vaccine effectiveness (VE). A \"classical\" TND study includes symptomatic individuals tested for the disease targeted by the vaccine to estimate VE against symptomatic infection. However, recent applications of the TND have attempted to estimate VE against infection by including all tested individuals, regardless of their symptoms. In this article, we use directed acyclic graphs and simulations to investigate potential biases in TND studies of COVID-19 VE arising from the use of this \"alternative\" approach, particularly when applied during periods of widespread testing. We show that the inclusion of asymptomatic individuals can potentially lead to collider stratification bias, uncontrolled confounding by health and healthcare-seeking behaviors (HSBs), and differential outcome misclassification. While our focus is on the COVID-19 setting, the issues discussed here may also be relevant in the context of other infectious diseases. This may be particularly true in scenarios where there is either a high baseline prevalence of infection, a strong correlation between HSBs and vaccination, different testing practices for vaccinated and unvaccinated individuals, or settings where both the vaccine under study attenuates symptoms of infection and diagnostic accuracy is modified by the presence of symptoms.

OBJECTIVE: To quantify the ability of two new comorbidity indices to adjust for confounding, by benchmarking a target trial emulation against the randomized controlled trial result.
METHODS: Observational study including 18 316 men from Prostate Cancer data Base Sweden 5.0, diagnosed with prostate cancer between 2008 and 2019 and treated with primary radical prostatectomy (n=14 379) or radiotherapy (n=3937). The effect on adjusted risk of death from any cause after adjustment for comorbidity by use of two new comorbidity indices, the Multidimensional Diagnosis-based Comorbidity Index (MDCI) and the Drug Comorbidity Index (DCI), were compared to adjustment for the Charlson Comorbidity Index (CCI).
RESULTS: Risk of death was higher after radiotherapy than radical prostatectomy (HR=1.94; 95% CI: 1.70 - 2.21). The difference decreased when adjusting for age, cancer characteristics, and CCI (HR=1.32, 95% CI: 1.06 - 1.66). Adjustment for the two new comorbidity indices further attenuated the difference (HR 1.14, 95% CI 0.91 - 1.44). Emulation of a hypothetical pragmatic trial where also older men with any type of baseline comorbidity were included, largely confirmed these results (HR 1.10; 95% CI 0.95 - 1.26).
CONCLUSIONS: Adjustment for comorbidity using two new indices provided comparable risk of death from any cause in line with results of a randomized controlled trial. Similar results were seen in a broader study population, more representative of clinical practice.

Oncologists are faced with choosing the best treatment for each patient, based on the available evidence from randomized controlled trials (RCTs) and observational studies. RCTs provide estimates of the average effects of treatments on groups of patients, but they may not apply in many real-world scenarios where for example patients have different characteristics than the RCT participants, or where different treatment variants are considered. Causal inference defines what a treatment effect is and how it may be estimated with RCTs or outside of RCTs with observational - or \'real-world\' - data. In this review, we introduce the field of causal inference, explain what a treatment effect is and what important challenges are with treatment effect estimation with observational data. We then provide a framework for conducting causal inference studies and describe when in oncology causal inference from observational data may be particularly valuable. Recognizing the strengths and limitations of both RCTs and observational causal inference provides a way for more informed and individualized treatment decision-making in oncology.

Recently, a bespoke instrumental variable method was proposed, which, under certain assumptions, can eliminate bias due to unmeasured confounding when estimating the causal exposure effect among the exposed. This method uses data from both the study population of interest, and a reference population in which the exposure is completely absent. In this paper, we extend the bespoke instrumental variable method to allow for a non-ideal reference population that may include exposed subjects. Such an extension is particularly important in randomized trials with nonadherence, where even subjects in the control arm may have access to the treatment under investigation. We further scrutinize the assumptions underlying the bespoke instrumental method, and caution the reader about the potential non-robustness of the method to these assumptions.

BACKGROUND: Randomized controlled trials are the gold standard for determining treatment efficacy in medicine. To deter harmful practices such as p-hacking and hypothesizing after the results are known, any analysis of subgroups and secondary outcomes must be documented and pre-specified. However, they can still introduce bias (and routinely do) if they are not treated with the same consideration as the primary analysis.
METHODS: We describe several sources of bias that affect subgroup and secondary outcome analyses using published randomized trials and causal directed acyclic graphs (DAGs).
RESULTS: We use the RECOVERY and START trials to elucidate sources of bias in analyses of subgroups and secondary outcomes. Chance imbalance can occur if the distribution of prognostic variables is not sought for any given subgroup analysis as for the main analysis. This differential distribution of prognostic variables can also occur in analyses of secondary outcomes. Selection bias can occur if the subgroup variable is causally related to staying in the trial. Given loss to follow up is not normally addressed in subgroups, attrition bias can pass unnoticed in these cases. In every case, the solution is to take the same considerations for these analyses as we do for primary analyses.
CONCLUSIONS: Approval of treatments and clinical decisions can occur based on results from subgroup or secondary outcome analyses. Thus, it is important to give them the same treatment as primary analyses to avoid preventable biases.