Department of Public Health Sciences Faculty Publications

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    Penalized Likelihood Ratio Test for a Biomarker Threshold Effect in Clinical Trials Based on Generalized Linear Models
    (Wiley, 2022-02-24) Gavanji, Parisa; Jiang, Wenyu; Chen, Bingshu
    In a clinical trial, the responses to the new treatment may vary among patient subsets with different characteristics in a biomarker. It is often necessary to examine if there is a cutpoint for the biomarker that divides the patients into two subsets of those with more-favourable and less-favourable responses. More generally, we approach this problem as a test of homogeneity in the effects of a set of covariates in generalized linear regression models. The unknown cutpoint results in a model with nonidentifiability and a nonsmooth likelihood function to which the ordinary likelihood methods do not apply. We first use a smooth continuous function to approximate the indicator function defining the patient subsets. We then propose a penalized likelihood ratio test to overcome the model irregularities. Under the null hypothesis, we prove that the asymptotic distribution of the proposed test statistic is a mixture of chi-squared distributions. Our method is based on established asymptotic theory, is simple to use, and works in a general framework that includes logistic, Poisson, and linear regression models. In extensive simulation studies, we find that the proposed test works well in terms of size and power. We further demonstrate the use of the proposed method by applying it to clinical trial data from the Digitalis Investigation Group (DIG) on heart failure.
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    Joint Modeling of Binary Response and Survival for Clustered data in Clinical Trials
    (Wiley, 2020-02-10) Chen, Bingshu; Wang, Jia
    In clinical trials, it is often desirable to evaluate the effect of a prognostic factor such as a marker response on a survival outcome. However, the marker response and survival outcome are usually associated with some potentially unobservable factors. In this case, the conventional statistical methods that model these two outcomes separately may not be appropriate. In this paper, we propose a joint model for marker response and survival outcomes for clustered data, providing efficient statistical inference by considering these two outcomes simultaneously. We focus on a special type of marker response: a binary outcome, which is investigated together with survival data using a cluster-specific multivariate random effect variable. A multivariate penalized likelihood method is developed to make statistical inference for the joint model. However, the standard errors obtained from the penalized likelihood method are usually underestimated. This issue is addressed using a Jackknife resampling method to obtain a consistent estimate of standard errors. We conduct extensive simulation studies to assess the finite sample performance of the proposed joint model and inference methods in different scenarios. The simulation studies show that the proposed joint model has excellent finite sample properties compared to the separate models when there exists an underlying association between the marker response and survival data. Finally, we apply the proposed method to a symptom control study conducted by Canadian Cancer Trials Group to explore the prognostic effect of covariates on pain control and overall survival.
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    Reduction in Mortality Risk With Opioid Agonist Therapy: a Systematic Review and Meta‐Analysis
    (Wiley, 2019) Bahji, A.; Cheng, B.; Gray, S.; Stuart, H.
    Introduction Opioid agonist therapies are effective medications that can greatly improve the quality of life of individuals with opioid use disorder. However, there is significant uncertainty about the risks of cause‐specific mortality in and out of treatment. Objective This systematic review and meta‐analysis explored the association between methadone and buprenorphine with cause‐specific mortality among opioid‐dependent persons. Methods We searched six online databases to identify relevant cohort studies, calculating all‐cause and overdose‐specific mortality rates during periods in and out of treatment. We pooled mortality estimates using multivariate random effects meta‐analysis of the crude mortality rate per 1000 person‐years of follow‐up as well as relative risks comparing mortality in vs. out of treatment. Results A total of 32 cohort studies (representing 150 235 participants, 805 423.6 person‐years, and 9112 deaths) met eligibility criteria. Crude mortality rates were substantially higher among methadone cohorts than buprenorphine cohorts. Relative risk reduction was substantially higher with methadone relative to buprenorphine when time in‐treatment was compared to time out‐of‐treatment. Furthermore, the greatest mortality reduction was conferred during the first 4 weeks of treatment. Mortality estimates were substantially heterogeneous and varied significantly by country, region, and by the nature of the treatment provider. Conclusion Precautions are necessary for the safer implementation of opioid agonist therapy, including baseline assessments of opioid tolerance, ongoing monitoring during the induction period, education of patients about the risk of overdose, and coordination within healthcare services.