The effect of omitted covariates in marginal and partially conditional recurrent event analyses.

There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general partially conditional rate-based models involving event-based stratification. The partially conditional model provides protection against extra-Poisson variation as well as event-dependent censoring, but conditioning on outcomes post-randomization can induce confounding and compromise causal inference. The purpose of this article is to examine the consequences of model misspecification in semiparametric marginal and partially conditional rate-based analysis through omission of prognostic variables. We do so using estimating function theory and empirical studies

Life History Analysis with Response-Dependent Observation

This thesis deals with statistical issues in the analysis of dependent failure time data under complex observation schemes. These observation schemes may yield right-censored, interval-censored and current status data and may also involve response-dependent selection of individuals. The contexts in which these complications arise include family studies, clinical trials, and population studies. Chapter 2 is devoted to the development and study of statistical methods for family studies, motivated by work conducted in the Centre for Prognosis Studies in the Rheumatic Disease at the University of Toronto. Rheumatologists at this centre are interested in studying the nature of within-family dependence in the occurrence of psoriatic arthritis (PsA) to gain insight into the genetic basis for this disease. Families are sampled by selecting members from a clinical registry of PsA patients maintained at the centre and recruiting their respective consenting family members; the member of the registry leading to the sampling of the family is called the proband. Information on the disease onset time for non-probands may be collected by recall or a review of medical records, but some non-probands simply provide their disease status at the time of assessment. As a result family members may provide a combination of observed or right-censored onset times, and current status information. Gaussian copula-based models are studied as a means of flexibly characterizing the within-family association in disease onset times. Likelihood and composite likelihood procedures are also investigated where the latter, like the estimating function approach, reduces the need to specify high-order dependencies and computational burden. Valid analysis of this type of data must address the response-biased sampling scheme which renders at least one affected family member (proband) with a right-truncated onset time. This right-truncation scheme, combined with the low incidence of disease among non-probands, means there is little information about the marginal onset time distribution from the family data alone, so we exploit auxiliary data from an independent sample of independent individuals to enhance the information on the parameters in the marginal age of onset distribution. For composite likelihood approaches, we consider simultaneous and two-stage estimation procedures; the latter greatly simplified the computational burden, especially when weakly, semi- or non-parametric marginal models are adopted. The proposed models and methods are examined in simulation studies and are applied to data from the PsA family study yielding important insight regarding the parent of origin hypothesis. Cluster-randomized trials are employed when it is appropriate on ethical, practical, or contextual grounds to assign groups of individuals to receive one of two or more interventions to be compared. This design also offers a way of minimizing contamination across treatment groups and enhancing compliance. Although considerable attention has been directed at the development of sample size formulae for cluster-randomized trials with continuous or discrete outcomes, relatively little work has been done for trials involving censored event times. In Chapter 3, asymptotic theory for sample size calculations for correlated failure time data arising in cluster-randomized trials is explored. When the intervention effect is specified through a semi-parametric proportional hazards model fitted under a working independence assumption, robust variance estimates are routinely used. At the design stage however, some model specification is required for the marginal distributions, and copula models are utilized to accommodate the within-cluster dependence. This method is appealing since the intervention effects are specified in terms of the marginal proportional hazards formulation while the within-cluster dependence is modeled by a separate association parameter. The resulting joint model enabled one to evaluate the robust sandwich variance, based on which the sample size criteria for right censored event times is developed. This approach has also been extended to deal with interval-censored event times and within-cluster dependence in the random right censoring times. The validity of the sample size formula in finite samples was investigated via simulation for a range of cluster sizes, censoring rates and degree of within-cluster association among event times. The power and efficiency implications of copula misspecification are studied, along with the effect of within-cluster dependence in the censoring times. The proposed sample size formula can be applied in a broad range of practical settings, and an application to a study of otitis media is given for illustration. Chapter 4 considers dependent failure time data in a slightly different context where the events correspond to transitions in a multistate model. A central goal in oncology is the reduction of mortality due to cancer. The therapeutic advances in the treatment of many cancers and the increasing pressure to ensure experimental treatments are evaluated in a timely and cost-effective manner, have made it challenging to design feasible trials with adequate power to detect clinically important effects based on the time from randomization to death. This has lead to increased use of the composite endpoint of progression-free survival, defined as the time from randomization to the first of progression or death. While trials may be designed with progression or progression-free survival as the primary endpoint, regulators are interested in statements about the effect of treatment on survival following progression. One approach to investigate this is to estimate the treatment effect on the time from progression to death, but this is not an analysis that benefits from randomization since the only individuals who contribute to this analysis are those that experienced progression. Also assessing the treatment effect on marginal features might lead to dependent censoring for the survival time following progression as other variables which have both effect on progression and post-progression survival time are omitted from the model. In Chapter 4 we consider a classical illness-death model which can be used to characterize the joint distribution of progression and death in this setting. Inverse probability weighting can then be used to address for the observational nature of this improper sub-group analysis and dependent censoring. Such inverse weighted equations yield consistent estimates of the causal treatment effect by accounting for the effect of treatment and any prognostic factors that may be shared between the model for the sojourn time distribution in the progression state and the transition intensity for progression. Due to the non-collapsibility of the Cox regression model we focus here on additive regression models. Chapter 5 discusses prevalent cohort studies and the problem of measurement error in the reported disease onset time along with other topics for further research

CounTR: Transformer-based Generalised Visual Counting

In this paper, we consider the problem of generalised visual object counting, with the goal of developing a computational model for counting the number of objects from arbitrary semantic categories, using arbitrary number of "exemplars", i.e. zero-shot or few-shot counting. To this end, we make the following four contributions: (1) We introduce a novel transformer-based architecture for generalised visual object counting, termed as Counting Transformer (CounTR), which explicitly capture the similarity between image patches or with given "exemplars" with the attention mechanism;(2) We adopt a two-stage training regime, that first pre-trains the model with self-supervised learning, and followed by supervised fine-tuning;(3) We propose a simple, scalable pipeline for synthesizing training images with a large number of instances or that from different semantic categories, explicitly forcing the model to make use of the given "exemplars";(4) We conduct thorough ablation studies on the large-scale counting benchmark, e.g. FSC-147, and demonstrate state-of-the-art performance on both zero and few-shot settings.Comment: Accepted by BMVC202

Zero-Shot Semantic Segmentation with Decoupled One-Pass Network

Recently, the zero-shot semantic segmentation problem has attracted increasing attention, and the best performing methods are based on two-stream networks: one stream for proposal mask generation and the other for segment classification using a pre-trained visual-language model. However, existing two-stream methods require passing a great number of (up to a hundred) image crops into the visuallanguage model, which is highly inefficient. To address the problem, we propose a network that only needs a single pass through the visual-language model for each input image. Specifically, we first propose a novel network adaptation approach, termed patch severance, to restrict the harmful interference between the patch embeddings in the pre-trained visual encoder. We then propose classification anchor learning to encourage the network to spatially focus on more discriminative features for classification. Extensive experiments demonstrate that the proposed method achieves outstanding performance, surpassing state-of-theart methods while being 4 to 7 times faster at inference. We release our code at https://github.com/CongHan0808/DeOP.git.Comment: 13pages, 9 figure

DiP: Learning Discriminative Implicit Parts for Person Re-Identification

In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks

Augmented composite likelihood for copula modeling in family studies under biased sampling

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record Zhong Y and Cook RJ (2016). Biostatistics, 17(3): 437–452. This article is available online at: DOI:10.1093/biostatistics/kxv054.The heritability of chronic diseases can be effectively studied by examining the nature and extent of within-family associations in disease onset times. Families are typically accrued through a biased sampling scheme in which affected individuals are identified and sampled along with their relatives who may provide right-censored or current status data on their disease onset times. We develop likelihood and composite likelihood methods for modeling the within-family association in these times through copula models in which dependencies are characterized by Kendall's τ. Auxiliary data from independent individuals are exploited by augmentating composite likelihoods to increase precision of marginal parameter estimates and consequently increase efficiency in dependence parameter estimation. An application to a motivating family study in psoriatic arthritis illustrates the method and provides some evidence of excessive paternal transmission of risk.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626