Stable computational methods for additive binomial models with application to adjusted risk differences

Risk difference is an important measure of effect size in biostatistics, for both randomised and observational studies. The natural way to adjust risk differences for potential confounders is to use an additive binomial model, which is a binomial generalised linear model with an identity link function. However, implementations of the additive binomial model in commonly used statistical packages can fail to converge to the maximum likelihood estimate (MLE), necessitating the use of approximate methods involving misspecified or inflexible models. A novel computational method is proposed, which retains the additive binomial model but uses the multinomial–Poisson transformation to convert the problem into an equivalent additive Poisson fit. The method allows reliable computation of the MLE, as well as allowing for semi-parametric monotonic regression functions. The performance of the method is examined in simulations and it is used to analyse two datasets from clinical trials in acute myocardial infarction. Source code for implementing the method in R is provided as supplementary material (see Appendix A).Australian Research Counci

Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach

Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a disparate body of evidence available to inform disease incidence and prevalence estimation. MPES is the statistical methodology adopted by the Health Protection Agency in the UK for its annual national assessment of the HIV epidemic, and is acknowledged by the World Health Organization and UNAIDS as a valuable technique for the estimation of adult HIV prevalence from surveillance data. This paper describes the results of utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands at the end of 2007, using an array of field data from different study designs on various population risk subgroups and with a varying degree of regional coverage. Auxiliary data and expert opinion were additionally incorporated to resolve issues arising from biased, insufficient or inconsistent evidence. This case study offers a demonstration of the ability of MPES to naturally integrate and critically reconcile disparate and heterogeneous sources of evidence, while producing reliable estimates of HIV prevalence used to support public health decision-making.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS488 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model

Relative risk regression using a log-link binomial generalized linear model (GLM) is an important tool for the analysis of binary outcomes. However, Fisher scoring, which is the standard method for fitting GLMs in statistical software, may have difficulties in converging to the maximum likelihood estimate due to implicit parameter constraints. logbin is an R package that implements several algorithms for fitting relative risk regression models, allowing stable maximum likelihood estimation while ensuring the required parameter constraints are obeyed. We describe the logbin package and examine its stability and speed for different computational algorithms. We also describe how the package may be used to include flexible semi-parametric terms in relative risk regression models

Piloting a parent and patient decision aid to support clinical trial decision making in childhood cancer

Objective: Families of a child with cancer can find the decision to enrol in a clinical trial challenging and often misunderstand key concepts that underpin trials. We pilot tested “Delta,” an online and booklet decision aid for parents with a child with cancer, and adolescents with cancer, deciding whether or not to enrol in a clinical trial. Methods: We developed Delta in accordance with the International Patient Decision Aid Standards. We conducted a pre-post pilot with parents with a child, and adolescents, who had enrolled in a paediatric phase III clinical trial for newly diagnosed acute lymphoblastic leukaemia. Parents (n = 37) and adolescents (n = 3) completed a questionnaire before and after using Delta (either the website or booklet, based on their preference). Results: Twenty-three parents (62.2%) and three adolescents (100%) reviewed the Delta website. Parents rated Delta as highly acceptable in regard to being clearly presented, informative, easy to read, useful, visually appealing, and easy to use. All participants reported that they would recommend Delta to others and that it would have been useful when making their decision. Parents' subjective (Mdiff=10.8, SDdiff = 15.69, P <.001) and objective (OR = 2.25, 95% CI, 1.66-3.04; P <.001) clinical trial knowledge increased significantly after reviewing Delta. Conclusions: To our knowledge, Delta is the first reported decision aid, available online and as a booklet, for parents and adolescents deciding whether or not to enrol in a paediatric oncology clinical trial. Our study suggests that Delta is acceptable, feasible, and potentially useful

Patterns and Predictors of Healthcare Use among Adolescent and Young Adult Cancer Survivors versus a Community Comparison Group.

Healthcare use (HCU) during survivorship can mitigate adolescent and young adult (AYA) cancer survivors' (aged 15-39 years) risk of medical and psychosocial late effects, but this is understudied. We surveyed 93 Australian AYA post-treatment cancer survivors (Mage = 22.0 years, SD = 3.5; 55.9% female) and a comparison sample of 183 non-matched AYAs (Mage = 19.7, SD = 3.2; 70.5% female) on their HCU, medication use, depression/anxiety, and general functioning. Relative to our comparison AYAs, a higher proportion of our survivor group reported medical HCU (community-delivered: 65.6% versus 47.0%, p = 0.003; hospital-delivered: 31.2% versus 20.3%, p = 0.044) and mental HCU (53.8% vs. 23.5%; p < 0.0001) in the past six months. A higher proportion of our survivors reported taking medications within the past six months than our comparison AYAs (61.3% vs. 42.1%, p = 0.003) and taking more types (p < 0.001). Vitamin/supplement use was most common followed by psychotropic medications. Our survivor group reported lower depression (p = 0.001) and anxiety symptoms (p = 0.003), but similar work/study participation (p = 0.767) to our comparison AYAs. Across groups, psychological distress was associated with higher mental HCU (p = 0.001). Among survivors, those who were female, diagnosed with brain/solid tumors and who had finished treatment more recently reported greater HCU. Future research should establish whether this level of HCU meets AYAs' survivorship needs

Impact of metabolic syndrome and its components on cardiovascular disease event rates in 4900 patients with type 2 diabetes assigned to placebo in the field randomised trial

<p>Abstract</p> <p>Background</p> <p>Patients with the metabolic syndrome are more likely to develop type 2 diabetes and may have an increased risk of cardiovascular disease (CVD) events.We aimed to establish whether CVD event rates were influenced by the metabolic syndrome as defined by the World Health Organisation (WHO), the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and the International Diabetes Federation (IDF) and to determine which component(s) of the metabolic syndrome (MS) conferred the highest cardiovascular risk in in 4900 patients with type 2 diabetes allocated to placebo in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial.</p> <p>Research design and methods</p> <p>We determined the influence of MS variables, as defined by NCEP ATPIII, IDF and WHO, on CVD risk over 5 years, after adjustment for CVD, sex, HbA_1c, creatinine, and age, and interactions between the MS variables in a Cox proportional-hazards model.</p> <p>Results</p> <p>About 80% had hypertension, and about half had other features of the metabolic syndrome (IDF, ATPIII). There was no difference in the prevalence of metabolic syndrome variables between those with and without CVD at study entry. The WHO definition identified those at higher CVD risk across both sexes, all ages, and in those without prior CVD, while the ATPIII definition predicted risk only in those aged over 65 years and in men but not in women. Patients meeting the IDF definition did not have higher risk than those without IDF MS.</p> <p>CVD risk was strongly influenced by prior CVD, sex, age (particularly in women), baseline HbA1_c, renal dysfunction, hypertension, and dyslipidemia (low HDL-c, triglycerides > 1.7 mmol/L). The combination of low HDL-c and marked hypertriglyceridemia (> 2.3 mmol/L) increased CVD risk by 41%. Baseline systolic blood pressure increased risk by 16% per 10 mmHg in those with no prior CVD, but had no effect in those with CVD. In those without prior CVD, increasing numbers of metabolic syndrome variables (excluding waist) escalated risk.</p> <p>Conclusion</p> <p>Absence of the metabolic syndrome (by the WHO definition) identifies diabetes patients without prior CVD, who have a lower risk of future CVD events. Hypertension and dyslipidemia increase risk.</p