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

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

Opposite associations between alanine aminotransferase and γ-glutamyl transferase levels and all-cause mortality in type 2 diabetes: analysis of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study

Aims Reported associations between liver enzymes and mortality may not hold true in type 2 diabetes, owing to a high prevalence of non-alcoholic fatty liver disease, which has been linked to cardiovascular disease and mortality in its own right. Our study aimed to determine whether alanine aminotransferase (ALT) or γ-glutamyl transferase (GGT) levels predict mortality in type 2 diabetes, and to examine possible mechanisms. Methods Data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study were analysed to examine the relationship between liver enzymes and all-cause and cause-specific mortality over 5 years. Results Over 5 years, 679 (6.9%) individuals died. After adjustment, for every standard deviation increase in ALT (13.2U/L), the HR for death on study was 0.85 (95% CI 0.78-0.93), p70 U/L, compared with GGT ≤70 U/L, had HR 1.82 (1.48−2.24), p70 U/L was associated with higher risks of death due to cardiovascular disease, cancer and non-cancer/non-cardiovascular causes. The relationship for ALT persisted after adjustment for indirect measures of frailty but was attenuated by elevated hsCRP. Conclusions As in the general population, ALT has a negative, and GGT a positive, correlation with mortality in type 2 diabetes when ALT is less than two times the upper limit of normal. The relationship 4 for ALT appears specific for death due to cardiovascular disease. Links of low ALT with frailty, as a potential mechanism for relationships seen, were neither supported nor conclusively refuted by our analysis and other factors are also likely to be important in those with type 2 diabetes

Simulated data used in "The importance of censoring in competing risks analysis of the subdistribution hazard"

The simulated data used for analysis in "The importance of censoring in competing risks analysis of the subdistribution hazard". Simulated using the method described in Additional file 1. Warning: Large file. Contains 1000 datasets of 300 observations each, for each of 105 parameter combinations (31,500,000 rows). Some programs (e.g. Excel) will not be able to open it in full. csv file with columns: p.comp: risk of the competing event in exposure group A for this scenario [0 to 0.30 in increments of 0.05] lnb.cens: log(hazard ratio) for loss to follow-up in old versus young individuals for this scenario [0 to 1 in increments of 0.25] lnb.evt: log(subdistribution hazard ratio) for the event of interest in exposure group B vs group A for this scenario [0, 0.5, 1] sim: ID of the simulated dataset for this scenario [1-1000] exposure: exposure group (0 = A, 1 = B) of this individual age: age group (0 = young, 1 = old) of this individual time: time-to-event or censoring for this individual evtcode: event type (0 = censoring, 1 = event of interest, 2 = competing event) censcode: type of censoring (1 = end-of-study, 2 = loss to follow-up

Flexible regression models for rate differences, risk differences and relative risks

Generalized additive models (GAMs) based on the binomial and Poisson distributions can be used to provide flexible semi-parametric modelling of binary and count outcomes. When used with the canonical link function, these GAMs provide semi-parametrically adjusted odds ratios and rate ratios. For adjustment of other effect measures, including rate differences, risk differences and relative risks, non-canonical link functions must be used together with a constrained parameter space. However, the algorithms used to fit these models typically rely on a form of the iteratively reweighted least squares algorithm, which can be numerically unstable when a constrained non-canonical model is used. We describe an application of a combinatorial EM algorithm to fit identity link Poisson, identity link binomial and log link binomial GAMs in order to estimate semi-parametrically adjusted rate differences, risk differences and relative risks. Using smooth regression functions based on B-splines, the method provides stable convergence to the maximum likelihood estimates, and it ensures that the estimates always remain within the parameter space. It is also straightforward to apply a monotonicity constraint to the smooth regression functions. We illustrate the method using data from a clinical trial in heart attack patients.18 page(s

The Clinical Relevance of p16 and p53 Status in Patients with Squamous Cell Carcinoma of the Vulva

Objective. To investigate the prognostic significance of HPV status in vulvar squamous cell carcinomas (VSCC) and to determine whether preoperative determination of p16 or p53 status would have clinical relevance. Methods. Patients treated for VSCC at a tertiary hospital in Sydney, Australia, from 2002 to 2014, were retrospectively evaluated (n = 119). Histological specimens were stained for p53 and p16 expression, and HPV status was determined by PCR detection of HPV DNA. Results. HPV DNA was detected in 19%, p16 expression in 53%, and p53 expression in 37% of patients. Kaplan–Meier survival estimates indicated that p16/HPV-positive patients had superior five-year disease-free survival (76% versus 42%, resp., p=0.004) and disease-specific survival (DSS) (89% versus 75% resp., p=0.05) than p53-positive patients. In univariate analysis, nodal metastases (p4 cm (p=0.03), and perineural invasion (p=0.05) were associated with an increased risk of disease progression and p16 expression with a decreased risk (p=0.03). In multivariable analysis, only nodal metastases remained independent for risk of disease progression (p=0.01). For DSS, lymph node metastases (p<0.001) and tumor size (p=0.008) remained independently prognostic. Conclusion. The p16/HPV and p53 status of VSCC allows separation of patients into two distinct clinicopathological groups, although 10% of patients fall into a third group which is HPV, p16, and p53 negative. p16 status was not independently prognostic in multivariable analysis. Treatment decisions should continue to be based on clinical indicators rather than p16 or p53 status

Quality of Life (QoL) of Children and Adolescents Participating in a Precision Medicine Trial for High-Risk Childhood Cancer

Precision medicine is changing the treatment of childhood cancer globally, however little is known about quality of life (QoL) in children and adolescents participating in precision medicine trials. We examined QoL among patients enrolled in PRISM, the Zero Childhood Cancer Program&rsquo;s precision medicine trial for high-risk childhood cancer. We assessed patient QoL via self-report (aged 12&ndash;17 years) and parent-proxy (aged 4&ndash;17 years) completion of the EQ-5D-Y. We analysed data using descriptive statistics and regression models. Patients (n = 23) and parents (n = 136) provided data after trial enrolment and following receipt of trial results and treatment recommendations (n = 8 patients, n = 84 parents). At enrolment, most patients were experiencing at least some difficulty across more than one QoL domain (81% patient self-report, 83% parent report). We did not find strong evidence of a change in QoL between timepoints, or of demographic or disease factors that predicted parent-reported patient QoL (EQ-VAS) at enrolment. There was strong evidence that receiving a treatment recommendation but not a change in cancer therapy was associated with poorer parent-reported patient QoL (EQ-VAS; Mdiff = &minus;22.5, 95% CI: &minus;36.5 to &minus;8.5, p = 0.006). Future research needs to better understand the relationship between treatment decisions and QoL and would benefit from integrating assessment of QoL into routine clinical care

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 &lt; 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 &lt; 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

Opposite associations between alanine aminotransferase and γ-glutamyl transferase levels and all-cause mortality in type 2 diabetes: analysis of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study

Aims Reported associations between liver enzymes and mortality may not hold true in type 2 diabetes, owing to a high prevalence of non-alcoholic fatty liver disease, which has been linked to cardiovascular disease and mortality in its own right. Our study aimed to determine whether alanine aminotransferase (ALT) or γ-glutamyl transferase (GGT) levels predict mortality in type 2 diabetes, and to examine possible mechanisms. Methods Data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study were analyzed to examine the relationship between liver enzymes and all-cause and cause-specific mortality over 5 years. Results Over 5 years, 679 (6.9%) individuals died. After adjustment, for every standard deviation increase in ALT (13.2 U/L), the HR for death on study was 0.85 (95% CI 0.78-0.93), p 70 U/L, compared with GGT ≤ 70 U/L, had HR 1.82 (1.48-2.24), p 70 U/L was associated with higher risks of death due to cardiovascular disease, cancer and non-cancer/non-cardiovascular causes. The relationship for ALT persisted after adjustment for indirect measures of frailty but was attenuated by elevated hsCRP. Conclusions As in the general population, ALT has a negative, and GGT a positive, correlation with mortality in type 2 diabetes when ALT is less than two times the upper limit of normal. The relationship for ALT appears specific for death due to cardiovascular disease. Links of low ALT with frailty, as a potential mechanism for relationships seen, were neither supported nor conclusively refuted by our analysis and other factors are also likely to be important in those with type 2 diabetes