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Basic Science and Molecular Oncology
Published in Manit Arya, Taimur T. Shah, Jas S. Kalsi, Herman S. Fernando, Iqbal S. Shergill, Asif Muneer, Hashim U. Ahmed, MCQs for the FRCS(Urol) and Postgraduate Urology Examinations, 2020
Paul Cleaveland, Vijay Sangar, Noel Clarke
Prevalence is the number of people with a given disease, including new and previously diagnosed, in a specific population, at a given time point. Incidence is the number of events in a defined population over a given time period. NNT – number needed to treat, is the number of people needed to be treated to prevent one bad outcome. This is defined as 1/Absolute risk reduction or (events in control group) – (events in intervention group). Absolute risk reduction is the difference in the rate of events between the control and intervention group. Relative risk reduction is the proportion by which the intervention reduces the event rate. It tells you by how much the treatment reduced the risk of unfavourable outcomes compared to the group that did not have treatment.
The practice of medicine
Published in Joachim P Sturmberg, Carmel M Martin, The Foundations of Primary Care, 2018
Joachim P Sturmberg, Carmel M Martin
Statistical numbers are frequently abused, and commonly not well understood among clinicians, and how easily clinicians can be misled on the basis of quoting a ‘big number’ is illustrated in Figure 123.54,56 Many studies do not report the prevalence of the condition in the community, and instead use relative risk reduction (RR) as the number to justify the introduction of change to clinical practice, without considering the absolute risk reduction (AR) – or the number needed to treat (NNT) – for one benefit to occur, which in fact may be very small.
Biostatistics: Issues in study design, analysis, and reporting
Published in Stephen W. Gutkin, Writing High-Quality Medical Publications, 2018
Relative risk reduction is considered to be a reliable measure of treatment effect because it does not vary substantially in patients at distinct levels of baseline absolute risk.132–134 The lower the disease event rate in the control (or unexposed) group, the higher the disparity between RRR and ARR.135
Clinical effectiveness of a cardiology outpatient management plan to reduce inefficiency in consultations
Published in Postgraduate Medicine, 2021
Francisco González-Llopis, Antonio Palazón-Bru, Emma Mares-García, María De Los Ángeles Carbonell-Torregrosa, Vicente Bertomeu-Martínez, Vicente Francisco Gil-Guillén
Qualitative variables were described using absolute and relative frequencies, while quantitative variables were described with means and standard deviations. To determine differences between the pre- and post-intervention groups, Student’s t-test, and Pearson’s Chi-square test were used, according to the typology of each variable. To determine the effectiveness of the intervention, all the indicators of clinical relevance were calculated, i.e., relative risk (RR), absolute risk reduction (ARR), relative risk reduction (RRR), and number needed to treat (NNT). This was done using raw data and propensity score adjustment as a population overlap weight. As the allocation of the intervention was not randomized, the application of this technique achieves results similar to those of a randomized clinical trial [13]. The adjustment variables should be those associated with the outcome being assessed (tests performed). These were the variables that showed a p-value <0.25 when differences were determined between patients with and without the tests performed. This same methodology was applied to determine the effect of having the tests performed on post-consultation clinical actions (treatment modification, request for new tests, hospital discharges, and new diagnoses). All the procedures were performed with a significance of 5% and for each relevant parameter its associated confidence interval (CI) was calculated. The statistical package used was IBM SPSS Statistics 25.
Are SGLT2 inhibitors or GLP-1 receptor agonists more appropriate as a second-line therapy in type 2 diabetes?
Published in Expert Opinion on Pharmacotherapy, 2018
Kashif M. Munir, Stephen N. Davis
Two large cardiovascular outcome trials have been completed to date with SGLT2 inhibitors, with several more trials nearing completion in the next few years. In 7020 patients with DM and at high cardiovascular risk treated with either empagliflozin 10 mg/day, 25/day, or placebo, pooled results of empagliflozin users showed a significant 14% reduction in the composite MACE-3 end point over a median 3.1 year period of observation [12]. There was also shown to be a significant 38% relative risk reduction in death from cardiovascular causes, 35% relative risk reduction in hospitalization for heart failure, and 32% relative risk reduction in all-cause mortality. Empagliflozin users experienced a significant 39% reduction in incident or worsening nephropathy, and a significant 55% relative risk reduction in initiation of renal-replacement therapy, as well [13]. In a similar large outcome trial of 10,142 patients with DM and high cardiovascular risk receiving either canagliflozin or placebo (65.6% of whom had previous cardiovascular disease), canagliflozin users showed a significant 14% reduction in the composite MACE-3 end point [14]. Improvements in progression of albuminuria, and the composite outcome of 40% reduction in estimated glomerular filtration rate, need for renal-replacement therapy, and death from renal causes were also observed in canagliflozin-treated patients. However, no significant difference was shown in all-cause mortality or death from cardiovascular cause. The main adverse effects in both trials were genitourinary infections. Also, in canagliflozin-treated patients, there was a higher rate of lower limb amputation.
Using copulas for rating weather index insurance contracts
Published in Journal of Applied Statistics, 2018
The risk reduction for single insurance contracts was evaluated by measuring the relative risk reductions in the lower tail of the yield unconditional and conditional distribution. Accordingly, the relative risk reduction was determined (i) by detecting the years with the lowest yield observations based on the farm yield distribution and (ii) using the weather index to identify drought years. Then, the risk measures were calculated for 10%, 20%, and 30% left-tail realizations of the unconditional and conditional farm yield distributions. Finally, to form a basis for statistical inferences, we applied bootstrapping to approximate the true distribution of the relative risk-reduction estimates.