Amblyopia treatment : an evidence-based approach to maximising treatment outcome

The basis of treatment for amblyopia (poor vision due to abnormal visual experience early in life) for 250 years has been patching of the unaffected eye for extended times to ensure a period of use of the affected eye. Over the last decade randomised controlled treatment trials have provided some evidence on how to tailor amblyopia therapy more precisely to achieve the best visual outcome with the least negative impact on the patient and the family. This review highlights the expansion of knowledge regarding treatment for amblyopia and aims to provide optometrists with a summary of research evidence to enable them to better treat amblyopia. Treatment for amblyopia is effective, as it reduces overall prevalence and severity of visual loss in this population. Correction of refractive error alone significantly improves visual acuity, sometimes to the point where further amblyopia treatment is not required. Atropine penalisation and patch occlusion are effective in treating amblyopia. Lesser amounts of occlusion or penalisation have been found to be just as effective as greater amounts. Recent evidence has highlighted that occlusion or penalisation in amblyopia treatment can create negative changes in behaviour in children and impact on family life. These complications should be considere when prescribing treatment because they can negatively affect compliance. Studies investigating the maximum age at which treatment of amblyopia can still be effective and the importance of near activities during occlusion are ongoing.

Alcohol expectancy changes over a 12-week cognitive–behavioral therapy program are predictive of treatment success

This study examines if outcome expectancies (perceived consequences of engaging in certain behavior) and self- efficacy expectancies (confidence in personal capacity to regulate behavior) contribute to treatment outcome for alcohol dependence. Few clinical studies have examined these constructs. The Drinking Expectancy Profile (DEP), a psychometric measure of alcohol expectancy and drinking refusal selfefficacy, was administered to 298 alcohol-dependent patients (207 males) at assessment and on completion of a 12-week cognitive–behavioral therapy alcohol abstinence program. Baseline measures of expectancy and self-efficacy were not strong predictors of outcome. However, for the 164 patients who completed treatment, all alcohol expectancy and self-efficacy factors of the DEP showed change over time. The DEP scores approximated community norms at the end of treatment. Discriminant analysis indicated that change in social pressure drinking refusal self-efficacy, sexual enhancement expectancies, and assertion expectancies successfully discriminated those who successfully completed treatment from those who did not. Future research should examine the basis of expectancies related to social functioning as a possible mechanism of treatment response and a means to enhance treatment outcome.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

Transforming the future of treatment for ovarian cancer

As for many other cancers, metastasis is the leading cause of death of patients with ovarian cancer. Vigorous basic and clinical research is being performed to initiate more efficacious treatment strategies to improve the poor outcome of women with this cancer. Current treatment for ovarian cancer includes advanced cyto-reductive surgery and traditional platinum and taxane combined chemotherapy. Clinical trials using novel cytotoxic reagents and tyrosine kinase inhibitors have also been progressing. In parallel, the application of robust unbiased high throughput research platforms using transcriptomic and proteomic approaches has identified that not only individual cell signalling pathways, but a network of molecular pathways, play an important role in the biology of ovarian cancer. Furthermore, intensive genomic and epigenetic analyses have also revealed single nucleotide polymorphisms associated with risk and/or aetiology of this cancer including patient response to treatment. Taken together, these approaches, that are advancing our understanding, will have an impact on the generation of new therapeutic approaches and strategies for improving the outcome and quality of life of patients with ovarian cancer in the near future.

A comparison of treatment options for management of end stage kidney disease in elderly patients : a systematic review

Background Chronic kidney disease is a global public health problem of increasing prevalence. There are five stages of kidney disease, with Stage 5 indicating end stage kidney disease (ESKD) requiring dialysis or death will eventually occur. Over the last two decades there have been increasing numbers of people commencing dialysis. A majority of this increase has occurred in the population of people who are 65 years and over. With the older population it is difficult to determine at times whether dialysis will provide any benefit over non-dialysis management. The poor prognosis for the population over 65 years raises issues around management of ESKD in this population. It is therefore important to review any research that has been undertaken in this area which compares outcomes of the older ESKD population who have commenced dialysis with those who have received non-dialysis management. Objective The primary objective was to assess the effect of dialysis compared with non-dialysis management for the population of 65 years and over with ESKD. Inclusion criteria Types of participants This review considered studies that included participants who were 65 years and older. These participants needed to have been diagnosed with ESKD for greater than three months and also be either receiving renal replacement therapy (RRT) (hemodialysis [HD] or peritoneal dialysis [PD]) or non-dialysis management. The settings for the studies included the home, self-care centre, satellite centre, hospital, hospice or nursing home. Types of intervention(s)/phenomena of interest This review considered studies where the intervention was RRT (HD or PD) for the participants with ESKD. There was no restriction on frequency of RRT or length of time the participant received RRT. The comparator was participants who were not undergoing RRT. Types of studies This review considered both experimental and epidemiological study designs including randomized controlled trials, non-randomized controlled trials, quasi-experimental, before and after studies, prospective and retrospective cohort studies, case control studies and analytical cross sectional studies. This review also considered descriptive epidemiological study designs including case series, individual case reports and descriptive cross sectional studies for inclusion. This review included any of the following primary and secondary outcome measures: •Primary outcome – survival measures •Secondary outcomes – functional performance score (e.g. Karnofsky Performance score) •Symptoms and severity of end stage kidney disease •Hospital admissions •Health related quality of life (e.g. KDQOL, SF36 and HRQOL) •Comorbidities (e.g. Charlson Comorbidity index).