In observational cohort studies with complex sampling schemes, truncation arises when the time to the event of interest is observed only when it falls below or exceeds another random time, i.e., the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this extension of the traditional paradigm as sequential truncation. Nonparametric and semiparametric maximum likelihood estimators have been developed recently to estimate the distribution of the event time of interest in the presence of sequential truncation. In this paper, we develop methods for regression modeling in this complex setting using the tool of pseudo-observations. Pseudo-observations are jackknife-like constructs that estimate an individual's contribution to an estimand. They are convenient as they are based on an estimator for the unconditional distribution of the event time. However, the simple pseudo-observation method may not be valid when the truncation depends on the covariates that also explain the time to event of interest, among other constraints. To address this limitation, we also consider a modified pseudo-observation approach. We develop both simple and modified pseudo-observation methods for Cox and accelerated failure time (AFT) models. We evaluate the proposed methods in simulation studies and apply them to an Alzheimer's disease cohort study.

Scientific theories of consciousness are typically evaluated by comparing empirical findings across experimental paradigms. Such comparisons implicitly assume that different paradigms provide commensurable evidence and do not systematically favour particular theoretical interpretations. Here, we test this assumption using the updated ConTraSt database, comprising 518 published consciousness experiments, and Bayesian regression models relating methodological characteristics to theoretical interpretations. We find decisive evidence that experimental methodology predicts whether studies support, challenge, or do not mention major theories of consciousness. These associations are robust across multiple Bayesian model comparison approaches and remain evident across a range of prior specifications. Methodological effects differ substantially across theories, indicating that theories occupy partially distinct methodological niches rather than a common empirical space. While these findings do not imply that any individual theory is more or less valid, they suggest that empirical support is systematically shaped by methodological tradition. Ultimately, it can be challenged if neural correlates of consciousness are “studying the same things” – and if not, what they in that case are actually about. Consequently, direct comparisons between theories may be more constrained than generally assumed. Our results highlight the need for greater methodological diversity in theory testing and suggest that future progress in consciousness science may require approaches that explicitly address the relationship between experimental methodology and theoretical interpretation.

Weighting with the inverse probability of censoring is an approach to deal with censoring in regression analyses where the outcome may be missing due to right-censoring. In this paper, three separate approaches involving this idea in a setting where the Kaplan–Meier estimator is used for estimating the censoring probability are compared. In more detail, the three approaches involve weighted regression, regression with a weighted outcome, and regression of a jack-knife pseudo-observation based on a weighted estimator. Expressions of the asymptotic variances are given in each case and the expressions are compared to each other and to the uncensored case. In terms of low asymptotic variance, a clear winner cannot be found. Which approach will have the lowest asymptotic variance depends on the censoring distribution. Expressions of the limit of the standard sandwich variance estimator in the three cases are also provided, revealing an overestimation under the implied assumptions.

Which of the many available theories of consciousness should a newcomer to the field choose? We consider possible ways to deal with this conundrum. We argue that convergence of theories is unlikely. Next, we consider ways comparing theories highlighting significant issues with existing endeavors in this regard. Given the nature of the field, presumably empirical support has a critical role to play when assessing theories. We examine a selection of hot topics—widely debated cases—and conclude that despite these supposedly exemplifying the best possible conditions for progress, they all struggle to move forward debates between theories. This leaves the large amounts of proposed evidence that never became hot topics, the so-called cold cases as a candidate to guide us in the conundrum. However, the lack of insight into the number of these and the lack of quality control as to whether each was in fact applicable to any given theory, is akin to a replication crisis. Irrespective of the conundrum, this looms large over any attempt to assess and compare theories according to empirical plausibility. There is a simple remedy for this: reduce the number of cold cases through independent assessment. Finally, we explore if a way out of the conundrum is to reject the need to choose between theories and consider proposals that reject the “theory-based” approach to consciousness studies.

We demonstrate that the usual Huber-White estimator is not consistent for the limiting covariance of parameter estimates in pseudo-observation regression approaches. By confirming that a plug-in estimator can be used instead, we obtain asymptotically exact and consistent tests for general linear hypotheses in the parameters of the model. Additionally, we confirm that naive bootstrapping can not be used for covariance estimation in the pseudo-observation model either. However, it can be used for hypothesis testing by applying a suitable studentization. Simulations illustrate the good performance of our proposed methods in many scenarios. Finally, we obtain a general uniform law of large numbers for U- and V-statistics, as such statistics are central in the mathematical analysis of the inference procedures developed in this work.

The validity of subjective reports in the measurement of mental content is a historical debate that keeps resurfacing. There are however reasons of principle why psychology can never get rid of reports without ending in circularity. The study of how to improve reports and how they work is an important and overlooked area of research.

I argue that cognitive neurorehabilitation is currently faced with important challenges for its progress. Whereas most challenges are already well-known and debated in the field, a relatively overlooked challenge is whether cognitive functions are multiply realized. I argue that this debate is central to progress in cognitive neurorehabilitation. I conclude arguing that progress is possible but requires methodological improvements to determine how and not just if a function is rehabilitated, and methods to decide whether two instances of a cognitive function are identical. The aim of this article is therefore twofold: first, to demonstrate why the question of multiple realization is not an abstract philosophical curiosity but a methodological bottleneck for research in cognitive neurorehabilitation, and-second - to suggest directions for empirical innovation that can help resolve whether observed recovery reflects restoration, compensation, or genuine multiple realization.

Consciousness research has long been dominated by competing grand theories, yet consensus remains elusive. We propose shifting focus toward construct-based, data-driven, and iterative approaches that identify the empirical building blocks of conscious experience and provide a more cumulative, integrative path forward for the field.

The average treatment effect is used to evaluate effects of interventions in a population. Under certain causal assumptions, such an effect may be estimated from observational data using the g-computation technique. The asymptotic properties of this estimator appears not to be well-known and hence bootstrapping has become the preferred method for estimating its variance. Bootstrapping is, however, not an optimal choice for multiple reasons; it is a slow procedure and, if based on too few bootstrap samples, results in a highly variable estimator of the variance. In this paper, we consider estimators of potential outcome means and average treatment effects using g-computation. We consider these parameters for the entire population but also in subgroups, for example, the average treatment effect among the treated. We derive their asymptotic distributions in a general framework. An estimator of the asymptotic variance is proposed and shown to be consistent when g-computation is used in conjunction with the M-estimation technique. The proposed estimator is shown to be superior to the bootstrap technique in a simulation study. Robustness against model misspecification is also demonstrated by means of simulations.

Abstract Introduction Cognitive rehabilitation for brain injury using hypnosis has received little attention. Methods Here, we report on self-defined treatment goals and sleep-related outcomes from a randomized actively controlled trial of 49 patients with chronic cognitive sequelae following acquired brain injury. Patients were randomized to two groups, who initially received hypnotic suggestions either from a classical hypnosis tradition (“targeted”) or from a mindfulness tradition (“non-targeted”). Patients set self-defined goals for their everyday lives. Results After eight sessions, patients reported outcomes (PROs) indicated large improvements with “same” (13%), “better” (44%), “much better” (18%), or “not a problem anymore” (25%). The reported “not a problem anymore” was exclusively reported following hypnosis, not mindfulness. After a 7-week follow-up period both groups experienced a decrease in their need for sleep (~ 55 min/day). Exploratory factor analysis showed that only improvement on objective but not subjective measures (e.g. Working Memory Index, Trail Making Test, and the European Brain Injury Questionnaire scored by a relative) reflected a latent improvement factor. This indicates that subjective reports following hypnotic suggestions should be interpreted cautiously. Conclusion Based on our findings and converging evidence, we conclude that hypnosis is a promising method in cognitive neurorehabilitation following acquired brain injury, although further high-quality randomized controlled trials are required.

Sensory interplay typically refers to cases where the content of one sensory modality affects the content of another sensory modality. Although there is much evidence today of such interplay, it is unknown how to interpret them. Is it a one-way causal function between two separate states (e.g., a visual process that influences an auditory process), a bidirectional relation between two separate states, or a new kind of state-as a sensory integration-that cannot be reduced to interactions between separate states? Despite these questions, the first-person perspective offers the insight that sensory modalities often integrate and that conscious content is seldom composed exclusively of one sense. Here, we explore how to understand sensory interplay and integration and how this analysis may provide a tool to analyze and investigate theories of consciousness. We argue that multisensory interplay leads the way to new empirical approaches to theory comparison that are not derivable from research in single perceptual systems, which may suggest new potential avenues of progress in consciousness science.

The recent “Conscious Turing Machine” (CTM) proposal offered by Manuel and Lenore Blum aims to define and explore consciousness, contribute to the solution of the hard problem, and demonstrate the value of theoretical computer science with respect to the study of consciousness. Surprisingly, given the ambitiousness and novelty of the proposal (and the prominence of its creators), CTM has received relatively little attention. We here seek to remedy this by offering an exhaustive evaluation of CTM. Our evaluation considers the explanatory power of CTM in three different domains of interdisciplinary consciousness studies: the philosophy of mind, cognitive neuroscience, and computation. Based on our evaluation in each of the target domains, at present, any claim that CTM constitutes progress is premature. Nevertheless, the model has potential, and we highlight several possible avenues of future research which proponents of the model may pursue in its development.

With incredible speed Large Language Models (LLMs) are reshaping many aspects of society. This has been met with unease by the public, and public discourse is rife with questions about whether LLMs are or might be conscious. Because there is widespread disagreement about consciousness among scientists, any concrete answers that could be offered the public would be contentious. This paper offers the next best thing: charting the possibility of consciousness in LLMs. So, while it is too early to judge concerning the possibility of LLM consciousness, our charting of the possibility space for this may serve as a temporary guide for theorizing about it.

The win ratio has in the recent decade gained popularity for analyzing prioritized multiple event data in clinical cohort studies, in particular within cardiovascular research. The literature on estimation of the win ratio using censored event data is however sparse. The methods that have been suggested have either an insufficient adjustment of the censoring or by assuming the the win and loss probabilities are proportional over time. The assumption of proportional win and loss probabilities will often in practice not be satisfied. In this paper, we present estimates for the win ratio, and win and loss probabilities, under independent right-censoring and derive the asymptotic distribution of the estimates. The proposed win ratio estimate does not require the assumption of proportional win and loss probabilities. The small sample properties of the proposed method are studied in a simulation study showing that the variance formula is accurate even for small samples. The method is applied on two data sets.

Background: Family histories of different mental and non-mental conditions have often been associated with autism spectrum disorder (ASD) but the restricted scope of conditions and family members that have been investigated limits etiologic understanding. We aimed to perform a comprehensive assessment of ASD associations with 3-generation family histories of 90 mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. The assessment comprised separate estimates of association with ASD overall; separate estimates by sex and intellectual disability (ID) status; as well as separate estimates of the co-occurrence of each of the 90 disorders in autistic persons. Additionally, we aimed to provide interactive catalogues of results to facilitate results visualization and further hypothesis-generation. Methods: We conducted a population-based, registry cohort study comprised of all live births in Denmark, 1980-2012, of Denmark-born parents, and with birth registry information (1,697,231 births), and their 3-generation family member types (20 types). All cohort members were followed from birth through April 10, 2017 for an ASD diagnosis. All participants (cohort members and each family member) were followed from birth through April 10, 2017 for each of 90 diagnoses, emigration or death. Adjusted hazard ratios (aHR) were estimated for ASD overall; by sex; or accounting for ID via separate Cox regression models for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person. aHRs were also calculated for sex-specific co-occurrence of each disorder, for ASD overall and considering ID. A catalogue of all results is displayed via interactive heat maps here: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries of results are here: https://public.tableau.com/views/ASDPlots_16918786403110/e-Figure5 . Results: Increased aHRs for ASD (26,840 cases; 1.6% of births) were observed for almost all individual mental disorder-family member type combinations yet for fewer non-mental disorder-family member type combinations. aHRs declined with diminishing degree of relatedness between the index person and family member for some disorders, especially mental disorders. Variation in aHR magnitude by family member sex (e.g., higher maternal than paternal aHRs) or side of the family (e.g., higher maternal versus paternal half sibling aHRs) was more evident among non-mental than mental disorders. Co-occurring ID in the family member or the index person impacted aHR variation. Conclusion: Our approach revealed considerable breadth and variation in magnitude of familial health history associations with ASD by type of condition, sex of the affected family member, side of the family, sex of the index person, and ID status which is indicative of diverse genetic, familial, and non-genetic ASD etiologic pathways. More careful attention to identifying sources of autism likelihood encompassed in family medical history, in addition to genetics, may accelerate understanding of factors underlying neurodiversity.

The win ratio has become a popular method for comparing multiple event data between two groups in clinical cohort studies. The win ratio compares the event data in prioritized order, where the first prioritized event is death and a typical example for the second prioritized event is hospitalization. Literature is sparse on inference for win and loss parameters, including the win ratio, for censored event data. Inference for two prioritized censored event times has been developed for independent right‐censoring. Many clinical studies include recurrent event data such as hospitalizations. In this article, we suggest inference for win‐loss parameters for death and a recurrent event outcome under independent right‐censoring. The small sample properties of the proposed method are studied in a simulation study showing that the variance formula is accurate even for small samples. The method is applied on a data set from a randomized clinical trial.

Regression analyses of how state occupation probabilities or expected lengths of stay depend on covariates in multistate settings can be performed using the pseudo-observation method, which involves calculating jackknife pseudo-observations based on some estimator of the expected value of the outcome. In this article, we present a new command, stpmstate, that calculates such pseudo-observations based on the Aalen–Johansen estimator. We give examples of use of the command, and we conduct a small simulation study to offer insights into the pseudo-observation regression approach.

Jack-knife pseudo-observations have in recent decades gained popularity in regression analysis for various aspects of time-to-event data. A limitation of the jack-knife pseudo-observations is that their computation is time consuming, as the base estimate needs to be recalculated when leaving out each observation. We show that jack-knife pseudo-observations can be closely approximated using the idea of the infinitesimal jack-knife residuals. The infinitesimal jack-knife pseudo-observations are much faster to compute than jack-knife pseudo-observations. A key assumption of the unbiasedness of the jack-knife pseudo-observation approach is on the influence function of the base estimate. We reiterate why the condition on the influence function is needed for unbiased inference and show that the condition is not satisfied for the Kaplan–Meier base estimate in a left-truncated cohort. We present a modification of the infinitesimal jack-knife pseudo-observations that provide unbiased estimates in a left-truncated cohort. The computational speed and medium and large sample properties of the jack-knife pseudo-observations and infinitesimal jack-knife pseudo-observation are compared and we present an application of the modified infinitesimal jack-knife pseudo-observations in a left-truncated cohort of Danish patients with diabetes.