1,721,140 research outputs found
Clinical trials and haemophilia : does the Bayesian approach make the ideal and desirable good friends?
No full textWhen the disease is rare and/or the outcome is uncommon the trial design does not warrant precise and unbiased estimates due to a lack of power or the expected length of recruitment and observation periods. Is there any reliable method to control for bias and consequently achieve an advantage from estimates generated by different study designs? An interesting statistical approach suitable to solve this problem has been theorized by Thomas Bayes. A Bayesian analysis is aimed at answering the question 'How this trial will modify our belief about that treatment effect?' In summary, the Bayesian approach can be defined as the explicit and quantitative use of any kind of external evidence in the design, analysis, and interpretation of an experimental trial. The results of a Bayesian analysis is the 95% credible interval in which we believe the estimate to lie with a probability of 95%, or the estimate of the probability that the quantity of interest is less than a specific value. The principal advantages of the Bayesian approach are that it allows to directly make probability statements about quantities of interest; it allows to easily make predictive statements, conditional on the current state of knowledge; it enables evidence from a variety of sources to be taken into account within a coherent modelling framework; it requires the investigator to explicit prior beliefs and demands. Exemplifications of the advantages of the Bayesian approach will be given discussing some papers published in Haemophilia
In anticoagulated patients with af, has-bled predicted major bleeding better than chads2 and cha2ds2-vasc
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The HAS-BLED score has better prediction accuracy for major bleeding than CHADS2 or CHA2DS2-VASc scores in anticoagulated patients with atrial fibrillation. [J Am Coll Cardiol. 201
A generalised model for individualising a treatment recommendation based on group-level evidence from randomised clinical trials
Full text linkObjectives: Randomised controlled trials report group-level treatment effects. However, an individual patient confronting a treatment decision needs to know whether that person's expected treatment benefit will exceed the expected treatment harm. We describe a flexible model for individualising a treatment decision. It individualises group-level results from randomised trials using clinical prediction guides. Methods: We constructed models that estimate the size of individualised absolute risk reduction (ARR) for the target outcome that is required to offset individualised absolute risk increase (ARI) for the treatment harm. Inputs to the model include estimates for the individualised predicted absolute treatment benefit and harm, and the relative value assigned by the patient to harm/benefit. A decision rule recommends treatment when the predicted benefit exceeds the predicted harm, value-adjusted. We also derived expressions for the maximum treatment harm, or the maximum relative value for harm/benefit, above which treatment would not be recommended. Results: For the simpler model, including one kind of benefit and one kind of harm, the individualised ARR required to justify treatment was expressed as required ARRtarget(i)=ARIharm(i) × RVharm/target(i). A complex model was also developed, applicable to treatments causing multiple kinds of benefits and/or harms. We demonstrated the applicability of the models to treatments tested in superiority trials (either placebo or active control, either fixed harm or variable harm) and non-inferiority trials. Conclusions: Individualised treatment recommendations can be derived using a model that applies clinical prediction guides to the results of randomised trials in order to identify which individual patients are likely to derive a clinically important benefit from the treatment. The resulting individualised prediction-based recommendations require validation by comparison with strategies of treat all or treat none
Approccio odontoiatrico al paziente Down
No full textThe muscolar hypotonus is one of main causes of the Down patient characteristic aspect. This problem, togheter with the rnental deficit, makes tnore difficult his involvement in the social thread. The Authors, alter a review of dental and orofacial conditions related to the Down's syndrome, suggest a preventive and interceptive treatment protocol pr early correction of malocclusions and muscular disfunctions in young patients with trisomv 2
Studio epidemiologico sulle lesioni precancerose del cavo orale riscontrate nell’attività assistenziale della nostra clinica dal 1985 al 1994.
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