Bivariate random-effects meta-analysis and the estimation of between-study correlation

BACKGROUND: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (ρ(B)). METHODS: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. RESULTS: The normal BRMA model estimates ρ(B )as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on [Formula: see text] (B). Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating ρ(B). CONCLUSION: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners

Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

Background: The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods: This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results: All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion: The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health care.The research in Spain was funded by grants from the Spanish Ministry of Health (grant FIS references: PI04/1980, PI0/41771, PI04/2450, and PI06/1442), Andalusian Council of Health (grant references: 05/403, 06/278 and 08/0194), and the Spanish Ministry of Education and Science (grant reference SAF 2006/07192). The Malaga sample, as part of the predictD-International study, was also funded by a grant from The European Commission (reference QL4-CT2002-00683)

How long did it last? A 10-year reconviction follow-up study of high intensity training for young offenders

Objectives: Most research has suggested that correctional boot camps are not very successful in reducing reoffending, but recent evidence has been more encouraging for programs that include significant rehabilitative components. In line with this, High Intensity Training (HIT) for offenders aged 18–21 at Thorn Cross Young Offender Institution in England was followed by a significant reduction in the number of reconvictions in a 2-year follow up. This article aims to evaluate the impact of the HIT program after 10 years. Methods: The evaluation used a quasi-experimental design in which male young offenders who received HIT were individually matched, on their risk of reconviction, to a comparison group who went to other prisons. Official reconviction data, including the prevalence, frequency, types, and costs of offenses were used as the outcome measures. Results: Offenders who received HIT had a significantly lower prevalence and frequency of reconvictions, but their superiority over the control group reduced over time (after about 4 years). However, the cumulative number of convictions that were saved increased steadily over time, from 1.35 per offender at 2 years to 3.35 per offender at 10 years. The cumulative cost savings also increased over time, and the benefit:cost ratio, based on fewer convictions, increased from 1.13 at 2 years to 3.93 at 10 years. Conclusions: The beneficial effects of the HIT program became more obvious over time. More randomized experiments and long-term follow-up research, including regular interviews, are needed to evaluate the cumulative and persisting effects of correctional interventions more accurately

The cost of frailty in France

International audienceThe objective of the present work is to explore the incremental costs of frailty associated with ambulatory health care expenditures (HCE) among the French population of community-dwellers aged 65 or more in 2012. We make use of a unique dataset that combines nationally representative health survey with respondents’ National Health Insurance data on ambulatory care expenditures. Several econometric specifications of generalized linear models are tested and an exponential model with gamma errors is eventually retained. Because frailty is a distinct health condition, its contribution to HCE was assessed in comparison with other health covariates (including chronic diseases and functional limitations, time-to-death, and a multidimensional composite health index). Results indicate that whatever health covariates are considered, frailty provides significant additional explanative power to the models. Frailty is an important omitted variable in HCE models. It depicts a progressive condition, which has an incremental effect on ambulatory health expenditures of roughly €750 additional euros for pre-frail individuals and €1500 for frail individuals

Novel prediction score including pre- and intraoperative parameters best predicts acute kidney injury after liver surgery

BACKGROUND: A recently published score predicts the occurrence of acute kidney injury (AKI) after liver resection based on preoperative parameters (chronic renal failure, cardiovascular disease, diabetes, and alanine-aminotransferase levels). By inclusion of additional intraoperative parameters we aimed to develop a new prediction model. METHODS: A series of 549 consecutive patients were enrolled. The preoperative score and intraoperative parameters (blood transfusion, hepaticojejunostomy, oliguria, cirrhosis, diuretics, colloids, and catecholamine) were included in a multivariate logistic regression model. We added the strongest predictors that improved prediction of AKI compared to the existing score. An internal validation by fivefold cross validation was performed, followed by a decision curve analysis to evaluate unnecessary special care unit admissions. RESULTS: Blood transfusions, hepaticojejunostomy, and oliguria were the strongest intraoperative predictors of AKI after liver resection. The new score ranges from 0 to 64 points predicting postoperative AKI with a probability of 3.5-95 %. Calibration was good in both models (15 % predicted risk vs. 15 % observed risk). The fivefold cross-validation indicated good accuracy of the new model (AUC 0.79 (95 % CI 0.73-0.84)). Discrimination was substantially higher in the new model (AUCnew 0.81 (95 % CI 0.76-0.86) versus AUCpreoperative 0.60 (95 % CI 0.52-0.69), p < 0.001). The new score could reduce up to 84 unnecessary special care unit admissions per 100 patients depending on the decision threshold. CONCLUSIONS: By combining three intraoperative parameters with the existing preoperative risk score, a new prediction model was developed that more accurately predicts postoperative AKI. It may reduce unnecessary admissions to the special care unit and support management of patients at higher risk