ObjectiveTo determine whether a change in editorial policy, including the implementation of a checklist, has been associated with improved reporting of measures which might reduce the risk of bias.MethodsThe study protocol has been published at doi: 10.1007/s11192-016-1964-8.DesignObservational cohort study.PopulationArticles describing research in the life sciences published in Nature journals, submitted after 1 May 2013.InterventionMandatory completion of a checklist during manuscript revision.Comparators(1) Articles describing research in the life sciences published in Nature journals, submitted before May 2013; and (2) similar articles in other journals matched for date and topic.Primary outcomeThe primary outcome is change in the proportion of Nature articles describing in vivo research published before and after May 2013 reporting the ‘Landis 4’ items (randomisation, blinding, sample size calculation and exclusions). We included 448 Nature Publishing Group (NPG) articles (223 published before May 2013, and 225 after) identified by an individual hired by NPG for this specific task, working to a standard procedure; and an independent investigator used PubMed ‘Related Citations’ to identify 448 non-NPG articles with a similar topic and date of publication from other journals; and then redacted all articles for time-sensitive information and journal name. Redacted articles were assessed by two trained reviewers against a 74-item checklist, with discrepancies resolved by a third.Results394 NPG and 353 matching non-NPG articles described in vivo research. The number of NPG articles meeting all relevant Landis 4 criteria increased from 0/203 prior to May 2013 to 31/181 (16.4%) after (two-sample test for equality of proportions without continuity correction, Χ²=36.2, df=1, p=1.8×10−9). There was no change in the proportion of non-NPG articles meeting all relevant Landis 4 criteria (1/164 before, 1/189 after). There were more substantial improvements in the individual prevalences of reporting of randomisation, blinding, exclusions and sample size calculations for in vivo experiments, and less substantial improvements for in vitro experiments.ConclusionThere was an improvement in the reporting of risks of bias in in vivo research in NPG journals following a change in editorial policy, to a level that to our knowledge has not been previously observed. However, there remain opportunities for further improvement.
Network hyperexcitability is a feature of Alzheimer’ disease (AD) as well as numerous transgenic mouse models of AD. While hyperexcitability in AD patients and AD animal models share certain features, the mechanistic overlap remains to be established. We aimed to identify features of network hyperexcitability in AD models that can be related to epileptiform activity signatures in AD patients. We studied network hyperexcitability in mice expressing amyloid precursor protein (APP) with mutations that cause familial AD, and compared a transgenic model that overexpresses human APP (hAPP) (J20), to a knock-in model expressing APP at physiological levels (APPNL/F). We recorded continuous long-term electrocorticogram (ECoG) activity from mice, and studied modulation by circadian cycle, behavioral, and brain state. We report that while J20s exhibit frequent interictal spikes (IISs), APPNL/F mice do not. In J20 mice, IISs were most prevalent during daylight hours and the circadian modulation was associated with sleep. Further analysis of brain state revealed that IIS in J20s are associated with features of rapid eye movement (REM) sleep. We found no evidence of cholinergic changes that may contribute to IIS-circadian coupling in J20s. In contrast to J20s, intracranial recordings capturing IIS in AD patients demonstrated frequent IIS in non-REM (NREM) sleep. The salient differences in sleep-stage coupling of IIS in APP overexpressing mice and AD patients suggests that different mechanisms may underlie network hyperexcitability in mice and humans. We posit that sleep-stage coupling of IIS should be an important consideration in identifying mouse AD models that most closely recapitulate network hyperexcitability in human AD.
Instrumental variable is an essential tool for addressing unmeasured confounding in observational studies. Two‐stage predictor substitution (2SPS) estimator and two‐stage residual inclusion (2SRI) are two commonly used approaches in applying instrumental variables. Recently, 2SPS was studied under the additive hazards model in the presence of competing risks of time‐to‐events data, where linearity was assumed for the relationship between the treatment and the instrument variable. This assumption may not be the most appropriate when we have binary treatments. In this paper, we consider the 2SRI estimator under the additive hazards model for general survival data and in the presence of competing risks, which allows generalized linear models for the relation between the treatment and the instrumental variable. We derive the asymptotic properties including a closed‐form asymptotic variance estimate for the 2SRI estimator. We carry out numerical studies in finite samples and apply our methodology to the linked Surveillance, Epidemiology and End Results (SEER)–Medicare database comparing radical prostatectomy versus conservative treatment in early‐stage prostate cancer patients.
A moment function is called doubly robust if it is comprised of two nuisance functions and the estimator based on it is a consistent estimator of the target parameter even if one of the nuisance functions is misspecified. In this paper, we consider a class of doubly robust moment functions originally introduced in (Robins et al., 2008). We demonstrate that this moment function can be used to construct estimating equations for the nuisance functions. The main idea is to choose each nuisance function such that it minimizes the dependency of the expected value of the moment function to the other nuisance function. We implement this idea as a minimax optimization problem. We then provide conditions required for asymptotic linearity of the estimator of the parameter of interest, which are based on the convergence rate of the product of the errors of the nuisance functions, as well as the local ill-posedness of a conditional expectation operator. The convergence rates of the nuisance functions are analyzed using the modern techniques in statistical learning theory based on the Rademacher complexity of the function spaces. We specifically focus on the case that the function spaces are reproducing kernel Hilbert spaces, which enables us to use its spectral properties to analyze the convergence rates. As an application of the proposed methodology, we consider the parameter of average causal effect both in presence and absence of latent confounders. For the case of presence of latent confounders, we use the recently proposed proximal causal inference framework of Tchetgen Tchetgen et al., 2020), and hence our results lead to a robust non-parametric estimator for average causal effect in this framework.
A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values. Skepticism about the exchangeability assumption in observational studies is often warranted because it hinges on investigators' ability to accurately measure covariates capturing all potential sources of confounding. Realistically, confounding mechanisms can rarely if ever, be learned with certainty from measured covariates. One can therefore only ever hope that covariate measurements are at best proxies of true underlying confounding mechanisms operating in an observational study, thus invalidating causal claims made on basis of standard exchangeability conditions. Causal learning from proxies is a challenging inverse problem which has to date remained unresolved. In this paper, we introduce a formal potential outcome framework for proximal causal learning, which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. Sufficient conditions for nonparametric identification are given, leading to the proximal g-formula and corresponding proximal g-computation algorithm for estimation. These may be viewed as generalizations of Robins' foundational g-formula and g-computation algorithm, which account explicitly for bias due to unmeasured confounding. Both point treatment and time-varying treatment settings are considered, and an application of proximal g-computation of causal effects is given for illustration.
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