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Aims: To measure the impact of Canada's recreational cannabis legalization (RCL) in October 2018 and the subsequent impact of the coronavirus disease 2019 (COVID‐19) lockdowns from March 2020 on rates of emergency department (ED) visits and hospitalizations for traffic injury. Design: An interrupted time series analysis of rates of ED visits and hospitalizations in Canada recorded in population‐based databases from January/April 2010 to March 2021. Setting: ED visits in Ontario and Alberta and hospitalizations in Ontario, Alberta, British Columbia, the Prairies (Manitoba and Saskatchewan) and the Maritimes (Nova Scotia, New Brunswick, Newfoundland and Prince Edward Island). Participants: Monthly counts of presentations to the ED or hospital for motor vehicle injury or pedestrian/cyclist injury, used to calculate monthly rates per 100 000 population. Measurements An occurrence of one or more International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Canada (ICD‐10‐CA) code for motor vehicle injury (V20–V29, V40–V79, V30–V39 and V86) and pedestrian/cyclist injury (V01–V09 and V10–V19) within the National Ambulatory Care Reporting System and Discharge Abstract Database. Findings There were no statistically significant changes in rates of ED visits and hospitalizations for motor vehicle or pedestrian/cyclist injury after RCL after accounting for multiple testing. After COVID‐19, there was an immediate decrease in the rate of ED visits for motor vehicle injury that was statistically significant only in Ontario (level change β = −16.07 in Ontario, 95% CI = −20.55 to −11.60, P = 0.000; β = −10.34 in Alberta, 95% CI = −17.80 to −2.89, P = 0.008; α of 0.004) and no changes in rates of hospitalizations. Conclusions: Canada's recreational cannabis legalization did not notably impact motor vehicle and pedestrian/cyclist injury. The rate of emergency department visits for motor vehicle injury decreased immediately after COVID‐19 lockdowns, resulting in rates below post‐recreational cannabis legalization levels in the year after COVID‐19. [ABSTRACT FROM AUTHOR]

In comparing quality of care between hospitals, disease-specific quality indicators measure structural, process, or outcome elements related to the care of a particular condition. Such comparisons can be framed in terms of causal contrasts, answering the question of whether a patient (or a population of patients on average) would receive different care if treated at the care level of a different hospital. Fair comparisons have to be adjusted for patient case-mix, which is equivalent to controlling for confounding by the patient-level factors, including demographic factors, comorbidities, and disease progression. The methodological choice for such comparisons is usually between direct and indirect standardization methods. In this article, we discuss the alternative of inverse probability weighting as a tool for standardization in hospital comparisons. This involves fitting multinomial logistic hospital assignment models and using these to construct the inverse probability weights. The challenge in the present context is the presence of large number of hospitals being compared, many of which have a small patient volume. We propose methods to include small categories in the weighted analysis, as well as metrics and visualizations for checking the positivity/overlap and covariate balance in constructing such weights. The methods are illustrated in a running example using linked administrative data on surgical treatment of kidney cancer patients in Ontario. [ABSTRACT FROM AUTHOR]

Objective To validate, in a multi-institution review, the safety, accuracy and reliability of renal tumour biopsy ( RTB) and its role in decreasing unnecessary treatment. Materials and Methods We conducted a multi-institution retrospective study of patients who underwent RTB to characterize a small renal mass ( SRM) between 2011 and May 2015. Patients were identified using the prospectively maintained Canadian Kidney Cancer information system. Diagnostic and concordance rates were presented using proportions, whereas factors associated with a diagnostic RTB were identified using a logistic regression model. Results Of the 373 biopsied SRMs, the initial biopsy was diagnostic in 87% of cases. Of the 47 non-diagnostic biopsies, 15 had a repeat biopsy of which, 80% were diagnostic. When both were combined, therefore, a diagnosis was obtained in 91% of SRMs. Of these, 18% were benign. Size was the only factor found to be associated with achieving a diagnostic biopsy. RTB histology and nuclear grade (high or low) were found to be highly concordant with surgical pathology (86 and 81%, respectively). Of the discordant tumours ( n = 16), all were upgraded from low to high grade on surgical pathology. Adverse events were rare (<1% of cases). Conclusion The present multi-institution study confirms that RTB of SRMs is safe, accurate and reliable across institutions, while decreasing unnecessary treatment. Given our findings, RTBs may be a helpful tool with which to triage SRMs and guide appropriate management. [ABSTRACT FROM AUTHOR]

Marginal structural Cox models are used for quantifying marginal treatment effects on outcome event hazard function. Such models are estimated using inverse probability of treatment and censoring (IPTC) weighting, which properly accounts for the impact of time-dependent confounders, avoiding conditioning on factors on the causal pathway. To estimate the IPTC weights, the treatment assignment mechanism is conventionally modeled in discrete time. While this is natural in situations where treatment information is recorded at scheduled follow-up visits, in other contexts, the events specifying the treatment history can be modeled in continuous time using the tools of event history analysis. This is particularly the case for treatment procedures, such as surgeries. In this paper, we propose a novel approach for flexible parametric estimation of continuous-time IPTC weights and illustrate it in assessing the relationship between metastasectomy and mortality in metastatic renal cell carcinoma patients. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this study, experimental test equipment developed in-house was used to study the compaction behavior of stitched quasi-unidirectional (UD) non-crimp fabrics (NCF) during the pre-filling, filling, and post-filling stages of the vacuum infusion (VI) process. The effects of the stitch pattern, stitch tension, and fiber sizing of reinforcements, as well as the effect of nesting of fiber bundles in neighboring layers, were studied. Moreover, the effects of cyclic compaction, resin viscosity, and different post-filling strategies were studied. The developed experimental test equipment provided an applicable measuring method for characterizing the compaction behavior of both the dry and resin-impregnated reinforcements. The effects of the stitching parameters and fiber nesting of reinforcements were found. The stitch pattern and post-filling strategies were noted to have an effect on the preform and laminate thickness. POLYM. COMPOS., 37:2692-2704, 2016. © 2015 Society of Plastics Engineers [ABSTRACT FROM AUTHOR]

The prevailing lack of consensus about the comparative harms and benefits of cancer screening stems, in part, from the inappropriate calculations of the expected mortality impact of a sustained screening programme. There is an inherent, and often substantial, time lag from the time of screening until the resulting mortality reductions begin, reach their maximum and ultimately end. However, the cumulative mortality reduction reported in a randomised screening trial is typically calculated over an arbitrarily defined follow-up period, including follow-up time where the mortality impact is yet to realise or where it has already been exhausted. Because of this, the cumulative reduction cannot be used for projecting the mortality impact expected from a sustained screening programme. For this purpose, we propose a new measure, the time-specific probability of being helped by screening, given that the cancer would have proven fatal otherwise. This can be decomposed into round-specific impacts, which in turn can be parametrised and estimated from the trial data. This represents a major shift in quantifying the benefits due to a sustained screening programme, based on statistical evidence extracted from existing trial data. We illustrate our approach using data from screening trials in lung and colorectal cancers. [ABSTRACT FROM AUTHOR]

While optimal dynamic treatment regimes (DTRs) can be estimated without specification of a predictive model, a model-based approach, combined with dynamic programming and Monte Carlo integration, enables direct probabilistic comparisons between the outcomes under the optimal DTR and alternative (dynamic or static) treatment regimes. The Bayesian predictive approach also circumvents problems related to frequentist estimators under the nonregular estimation problem. However, the model-based approach is susceptible to misspecification, in particular of the 'null-paradox' type, which is due to the model parameters not having a direct causal interpretation in the presence of latent individual-level characteristics. Because it is reasonable to insist on correct inferences under the null of no difference between the alternative treatment regimes, we discuss how to achieve this through a 'null-robust' reparametrization of the problem in a longitudinal setting. Since we argue that causal inference can be entirely understood as posterior predictive inference in a hypothetical population without covariate imbalances, we also discuss how controlling for confounding through inverse probability of treatment weighting can be justified and incorporated in the Bayesian setting. [ABSTRACT FROM AUTHOR]

ABSTRACT Assessing the absolute risk for a future disease event in presently healthy individuals has an important role in the primary prevention of cardiovascular diseases (CVD) and other chronic conditions. In this paper, we study the use of non-parametric Bayesian hazard regression techniques and posterior predictive inferences in the risk assessment task. We generalize our previously published Bayesian multivariate monotonic regression procedure to a survival analysis setting, combined with a computationally efficient estimation procedure utilizing case-base sampling. To achieve parsimony in the model fit, we allow for multidimensional relationships within specified subsets of risk factors, determined either on a priori basis or as a part of the estimation procedure. We apply the proposed methods for 10-year CVD risk assessment in a Finnish population. © 2014 Board of the Foundation of the Scandinavian Journal of Statistics [ABSTRACT FROM AUTHOR]

Under cohort sampling designs, additional covariate data are collected on cases of a specific type and a randomly selected subset of noncases, primarily for the purpose of studying associations with a time-to-event response of interest. With such data available, an interest may arise to reuse them for studying associations between the additional covariate data and a secondary non-timeto-event response variable, usually collected for the whole study cohort at the outset of the study. Following earlier literature, we refer to such a situation as secondary analysis. We outline a general conditional likelihood approach for secondary analysis under cohort sampling designs and discuss the specific situations of case-cohort and nested case-control designs. We also review alternative methods based on full likelihood and inverse probability weighting. We compare the alternative methods for secondary analysis in two simulated settings and apply them in a real-data example. [ABSTRACT FROM AUTHOR]

. When applicable, an assumed monotonicity property of the regression function w.r.t. covariates has a strong stabilizing effect on the estimates. Because of this, other parametric or structural assumptions may not be needed at all. Although monotonic regression in one dimension is well studied, the question remains whether one can find computationally feasible generalizations to multiple dimensions. Here, we propose a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure. The monotonic construction is based on marked point processes, where the random point locations and the associated marks (function levels) together form piecewise constant realizations of the regression surfaces. The actual inference is based on model-averaged results over the realizations. The monotonicity of the construction is enforced by partial ordering constraints, which allows it to asymptotically, with increasing density of support points, approximate the family of all monotonic bounded continuous functions. [ABSTRACT FROM AUTHOR]

Suppose a nested case-control design has been applied for collecting covariate data when studying a specific disease. With possible new outcomes of interest it would be sensible to utilize the previously selected control group instead of (or in addition to) a new control selection, given that the same covariate data were relevant and available, and that their measurements had adequate stability and quality. We formulate this problem in the framework of the competing risks survival model. In this approach covariate information collected for all outcomes can be utilized in the analysis. We not only propose likelihood-based parameter estimation but we also review alternative methods based on weighted partial/pseudolikelihoods. The methods discussed here are closely related to the analysis of a case-cohort design, where the control group is not tied to cases of a specific disease. The different methods are compared in a simulation study. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We introduce an importance sampling derivation of the average treatment effect on the treated and extend this to incorporate an augmentation term to allow doubly robust estimation of the average treatment effect on the treated. Unlike the matching estimator of the average treatment effect on the treated, the augmented inverse weighted estimator that results from the importance sampling approach has regular asymptotic properties and does not result in any datapoints being excluded from the estimation. Following simulations, we apply the doubly robust, augmented weighted estimator to a U.S. national survey of health to examine the impact of smoking on sleep, and we use techniques developed for other doubly robust estimators to demonstrate model validity. [ABSTRACT FROM AUTHOR]