The term "attention placebo" has been used in an imprecise and often seemingly thoughtless way for decades, and it is often confusing to encounter "attention placebo group" in outcome studies. The purpose of this study was to investigate attention placebo empirically, focusing on anxiety or phobia related problems. The two aims were (1) to describe and summarize how researchers define attention placebo in empirical studies and the intervention procedures involved in these studies; and (2) to explore the possibility of estimating the magnitude of the attention placebo effects on anxiety or phobia related problems.A thorough literature search was carried out with the keywords of attention placebo synonyms and the resulting list of 1304 articles was screened for unique empirical articles on anxiety or phobia related problems. A total of 545 articles were identified as attention placebo empirical studies, and 83 involved anxiety or phobia related problems. These 83 constituent articles were coded for the purpose of qualitative overview, and 63 articles with sufficient data were included in the meta-analysis of attention placebo effects.The results of the qualitative overview of attention placebo on anxiety or phobia related problems showed that most articles did not discuss explicitly any rationale for using an attention placebo group, but simply included one as a comparison group. The arrangements for attention placebo interventions were diverse and often reflected little concern for emulating the nonspecific features of the treatment(s) of interest. Two meta-analyses were performed based on the comparison of attention placebo and a no-treatment control, and attention placebo and the verum. The overall estimated effect of attention placebo is Hedges's g=0.32, a small effect, significantly different from the effect size of the other control groups. Compared to the verum, the effects of attention placebo are not consistent in magnitude but were nearly all smaller than the effects of the verum treatments. Meta-regression and subgroup analyses indicated that estimating attention placebo effects is a sensible and meaningful research activity.In conclusion, attention placebo is not so much about attention, and it is possible to estimate the effects of attention placebo.

This dissertation makes use of data from three large and detailed rural household surveys conducted in Haiti to examine elements of economic behavior in poor rural households. We use the earliest survey to formulate a set of hypotheses and use statistical meta-analysis to test them against all three surveys. Results in the areas of household vulnerability, form, migration, and education contribute to theory-refinement in Economic Anthropology.

This thesis consists of two sections, the first of which is a systematic review of selfreport measures of mindfulness. The review aims were to evaluate the psychometric properties of each of the identified measures and examine their utility for research and clinical practice. Definitions of mindfulness were central to the differences found between measures, and as such this review also provides an overview of how mindfulness has been conceptualised in the literature. This review has been presented in the format required by the journal, Clinical Psychology Review. The second section is a meta-analysis which examines the efficacy of mindfulnessbased stress reduction (MBSR). Mindfulness-based interventions are increasingly being applied in a range of settings and the evidence base is growing. Specifically, this review aimed to determine the effectiveness of MBSR on quality of life for people suffering from chronic physical health conditions. The methods and results of the meta-analysis are described in detail, followed by a discussion of the findings. A more concise overview is then provided as a journal article, in the format required by the British Journal of Clinical Psychology. The guidelines for submission to both journals are included in Appendix 1 and 2 respectively.

Aims: To examine the efficacy of CBT for late-life depression in older adults with co-morbid physical illness and to review what has been revealed by meta-analytic studies with regards moderators of treatment in psychological approaches for late-life depression. Method: Systematic literature search and meta-analysis of randomised controlled trials (RCT) evaluating CBT for depression in older adults with co-morbid physical illness and systematic review of meta-analyses examining psychological therapies for late-life depression. Results: Nine papers met inclusion criteria for meta-analysis. CBT was superior to waiting list and treatment as usual control conditions, showing a statistically significant pooled standardised mean difference (SMD) of 0.63 (95 per cent CI, 0.29 to 0.97, p = 0.0003). This was largely maintained at follow up (SMD 0.5, 95 per cent CI, 0.08 to 0.92). Sensitivity analysis showed individual CBT yielded a large, statistically significant summary effect size of 0.80 (95 per cent CI, 0.45 to 1.16), but that group CBT did not show statistical superiority over controls. Clinician-rated measures of depression yielded larger effect sizes, with a SMD of 1.57 (95 per cent CI, 0.56 to 2.59, p = 0.002) as compared with patientrated measures: 1.03 (95 per cent CI, 0.75 to 1.31, p = 0.0001). Fourteen meta-analyses met inclusion criteria for systematic review. More recent publication was significantly correlated with increased reporting quality and reduced analysis of moderating factors. Duration of treatment, treatment setting and gender of participants showed no moderating impact on outcome. Depression severity, participant age, treatment modality, and study quality showed no consistent relationship with outcomes. Active or placebo controls were associated with reduced effect sizes when compared with no treatment or waiting list controls. Patient-rated outcome measures were associated with reduced effect sizes as compared with clinician-rated measures. Conclusions: When compared with treatment as usual and waiting list controls Individual CBT is effective in reducing depressive symptoms for depressed older adults with an underlying physical illness. Meta-analytic studies of late-life depression show variable results regarding moderators of treatment efficacy. More high quality studies examining the effectiveness of psychological therapies are needed with clinically representative older populations, particularly, the older-old and those with co-morbid physical illnesses.

Background: Despite substantial research and development investment in Alzheimer's disease (AD), effective therapeutics remain elusive. Significant emerging evidence has linked cholesterol, beta-amyloid and AD, and several studies have shown a reduced risk for AD and dementia in populations treated with statins. However, while some clinical trials evaluating statins in general AD populations have been conducted, these resulted in no significant therapeutic benefit. By focusing on subgroups of the AD population, it may be possible to detect endotypes responsive to statin therapy. Methods: Here we investigate the possible protective and therapeutic effect of statins in AD through the analysis of datasets of integrated clinical trials, and prospective observational studies. Results: Re-analysis of AD patient-level data from failed clinical trials suggested by trend that use of simvastatin may slow the progression of cognitive decline, and to a greater extent in ApoE4 homozygotes. Evaluation of continual long-term use of various statins, in participants from multiple studies at baseline, revealed better cognitive performance in statin users. These findings were supported in an additional, observational cohort where the incidence of AD was significantly lower in statin users, and ApoE4/ApoE4-genotyped AD patients treated with statins showed better cognitive function over the course of 10-year follow-up. Conclusions: These results indicate that the use of statins may benefit all AD patients with potentially greater therapeutic efficacy in those homozygous for ApoE4.

Mental health professionals are routinely called upon to assess the violence risk of their patients. An increasingly common method for conducting such assessments is the use of structured risk assessment tools. The aim of this thesis was to investigate the utility of such instruments: to identify and explore current uncertainties concerning their applicability and to design a novel measure that could be used as part of a stepped strategy to risk assessment. Though a number of risk assessment tools have been developed and there is a considerable literature concerning their psychometric properties, uncertainty remains regarding their effective use. In order to identify key contemporary uncertainties, a metareview of the forensic assessment literature was conducted. The metareview found that previous systematic reviews and meta-analyses of the risk assessment literature have come to conflicting conclusions on a number of issues, including the comparative predictive validity of risk assessment tools, the efficacy of actuarial tools versus clinical judgement, and the influence of demographic factors and study design characteristics on predictive accuracy. These uncertainties were then investigated in a comprehensive meta-analysis of nine commonly used risk measures. The meta-analysis concluded that there were significant differences between the predictive validity of the risk assessment tools, with instruments designed for more specific purposes performing better than those designed for more general use. Tools performed best when administered to samples demographically similar to their calibration sample. Actuarial instruments and structured clinical judgement were found to perform comparably. The final study presented in this thesis explored the feasibility of a stepped approach to risk assessment in which individuals at very low risk of future violence are screened out prior to resource-intensive clinically based assessment. High-quality national registers were used to construct a simple tool to identify patients with schizophrenia at very low risk of violent conviction after being discharged from hospital. The tool was found to produce high rates of sensitivity as well as high negative predictive values at 1, 2, and 5 years follow.up. In light of the findings of these three studies, risk assessment procedures and guidelines by mental health services and criminal justice systems may need review.

A review of the literature reveals a difference of opinion regarding whether open or closed reduction of condylar fractures produces the best results. It would be beneficial to critically analyze past studies that have directly compared the two methods in an attempt to answer this question. A Medline search for articles using the key words 'mandibular condyle fractures' and 'mandibular condyle fractures surgery' was performed. The articles chosen for the meta-analysis contained data on at least one of the following: postoperative maximum mouth opening, lateral excursion, protrusion, deviation on opening, asymmetry, and joint pain or muscle pain. Several common statistical methods were used to test for differences between open and closed surgery, including the weighted average method for fixed and random effects as well as the Mantel-Haenszel method for fixed effects. Some of the outcome variables were found to be statistically significant but were interpreted with caution because of the poor quality of the studies assessed. There is a need for more standardized data collection as well as patient randomization to treatment groups.

Clustered data often feature nested structures and repeated measures. If coupled with binary outcomes and large samples (>10,000), this complexity can lead to non-convergence problems for the desired model especially if random effects are used to account for the clustering. One way to bypass the convergence problem is to split the dataset into small enough sub-samples for which the desired model convergences, and then recombine results from those sub-samples through meta-analysis. We consider two ways to generate sub-samples: the K independent samples approach where the data are split into k mutually-exclusive sub-samples, and the cluster-based approach where naturally existing clusters serve as sub-samples. Estimates or test statistics from either of these sub-sampling approaches can then be recombined using a univariate or multivariate meta-analytic approach. We also provide an innovative approach for simulating clustered and dependent binary data by simulating parameter templates that yield the desired cluster behavior. This approach is used to conduct simulation studies comparing the performance of the K independent samples and cluster-based approaches to generating sub-samples, the results from which are combined either with univariate and multivariate meta-analytic techniques. These studies show that using natural clusters leaded to lower biased test statistics when the number of clusters and treatment effect were large, as compared to the K independent samples approach for both the univariate and multivariate meta-analytic approaches. And the independent samples approach was preferred when the number of clusters and treatment effect were small. We also apply these methods to data on cancer screening behaviors obtained from electronic health records of n=15,652 individuals and showed that these estimated results support the conclusions from the simulation studies.

Background: Multiple sclerosis (MS) is the most common cause of disability in young people and yet there are no interventions available which reliably alter disease progression. This is despite several decades of research using the most common animal model of multiple sclerosis, experimental autoimmune encephalomyelitis (EAE). There is now emerging evidence across the neurosciences to suggest that limited internal validity (measures to reduce bias) and external validity (e.g. using a clinically relevant animal model) may influence the translational success. Aim and objectives: To provide an unbiased summary of the scope of the literature on candidate drugs for MS tested in EAE to identify potential reasons for the failures to translate efficacy to clinical trials. My objectives were, across all of the identified publications, to: (1) describe the reporting of measures to reduce bias and to assess their impact on measures of drug efficacy; (2) assess the relationship between treatment related effects measured using different outcome measures; (3) assess the prevalence and impact of any publication bias; (4) compare findings from the above with another disease with limited translational success (Parkinson’s disease; PD). Methods: I used systematic searches of three online databases to identify relevant publications. Estimates of efficacy were extracted for neurobehavioural scores, inflammation, demyelination and axon loss. For PD experiments, we searched for dopamine agonists tested in animal models of PD with outcome assessed as change in neurobehavioural scores. I calculated normalised mean difference or standardised mean difference effect sizes and combined these in a meta-analysis using a random effects model. I used stratified meta-analysis or meta-regression to assess the extent to which different study design characteristics explained differences in reported efficacies. These characteristics included: measures to reduce bias (random allocation to group and blinded assessment of outcome), the animal species, sex, time of drug administration, route of drug administration and the number of animals per group. Publication bias was assessed using funnel plotting, Egger regression and “trim and fill”. Results: I identified 1464 publications reporting drugs tested in EAE. Reported study quality was poor: 11% reported random allocation to group, 17% reported blinded assessment of neurobehavioural outcomes, 28% reported blinded assessment of histological outcomes, and < 1% reported a sample size calculation. Estimates of efficacy measured as the reduction in inflammation were substantially higher in unblinded studies (47.1% reduction (95% CI 41.8-52.4)) versus blinded studies (33.1% (25.8-40.4). Moreover, the same finding was identified for 121 publications on dopamine agonists tested in experimental PD models where efficacy was measured as change in neurobehavioural outcomes. For EAE studies we were unable to include data from 631 publications describing original research. Usually this was because the publication did not include basic details such as the number of animals in each group (115 publications), the observed variance (592) or suitable control data (49). For each category of outcome I found evidence of a substantial publication bias. Interventions were most commonly administered on or before the induction of EAE with shorter times to treatment associated with higher estimates of efficacy for the reduction in mean severity scores (a neurobehavioural outcome). Treatment related effects were found to vary across different outcome measures with the largest effect being for the reduction in axon loss. Where neurobehavioural scores and axon loss were measured in the same cohort of animals, the concordance between efficacies in these increased with later times to treatment. Conclusions: In this, the largest systematic review and meta-analysis of animal studies in any domain, I have found that a large number of publications present incomplete data. In addition, measures to reduce bias are seldom reported, the lack of which is associated with overstatements of efficacy for both a measure of drug efficacy in EAE and experimental PD studies. Translational success may have also been affected by the majority of studies administering drugs on or before EAE induction which is of limited relevance in the clinical setting where patients do not present at that stage of disease. Moreover, my analysis of the relationship between outcome measures provides empirical evidence from systematically identified studies to suggest that targeting axon loss as later time points is most strongly associated with improvements in neurobehavioural scores. Therefore drugs which are successfully able to target axon loss at these time points may offer substantial hope for clinical success. Overall, improvements in the conduct and reporting of preclinical studies are likely to improve their utility, and the prospects for translational success. While my findings relate predominately to the animal modelling of MS and PD it is likely that they also hold for other animal research.

Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays using melanoma as a test cancer has uncovered significant inconsistences that hinder advances in clinical practice. In this study a computational model for the integrated analysis of microarray datasets is proposed in order to provide a robust ranking of genes in terms of their relative significance; both genome-wide relative significance (GWRS) and genome-wide global significance (GWGS). When applied to five melanoma microarray datasets published between 2000 and 2011, a new 12-gene diagnostic biomarker signature for melanoma was defined (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). Of these, CXCL13, COL11A1, PTPRF and SHC4 are components of the MAPK pathway and were validated by immunocyto- and immunohisto-chemistry. These proteins were found to be overexpressed in metastatic and primary melanoma cells in vitro and in melanoma tissue in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. One challenge for the integrated analysis of microarray data is that the microarray data are produced using different platforms and bio-samples, e.g. including both cell line- and biopsy-based microarray datasets. In order to address these challenges, the computational model was further enhanced the stratification of datasets into either biopsy or cell line derived datasets, and via the weighting of microarray data based on quality criteria of data. The methods enhancement was applied to 14 microarray datasets of three cancers (breast, prostate, and melanoma) based on classification accuracy and on the capability to identify predictive biomarkers. Four novel measures for evaluating the capability to identify predictive biomarkers are proposed: (1) classifying independent testing data using wrapper feature selection with machine leaning, (2) assessing the number of common genes with the genes retrieved in independent testing data, (3) assessing the number of common genes with the genes retrieved in across multiple training datasets, (4) assessing the number of common genes with the genes validated in the literature. This enhancement of computational approach (i) achieved reliable classification performance across multiple datasets, (ii) recognized more significant genes into the top-ranked genes as compared to the genes detected by the independent test data, and (iii) detected more meaningful genes than were validated in previous melanoma studies in the literature.