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The impact of treatment and other clinical and community health interventions: A ‘does it work?’ evaluation
Published in Milos Jenicek, Foundations of Evidence-Based Medicine, 2019
Relative risk of events, absolute risk reduction or benefit increase (ARR or ABI), and relative risk increase (RRI) or relative benefit increase (RBI) are increasingly used in evidence-based medicine and beyond. The original EBM contribution to the evaluation of treatment impact is the development and use of the ‘number needed to treat’ or NNT.25–32 As defined in Table 9.1, NNT allows the clinician an additional meaningful expression of clinical or preventive intervention impact. The lesser the NNT, the more effective the drug. A ‘number needed to harm’ or NNH, developed and used originally in the study of undesirable (adverse) effects of treatment, may also be used (perhaps after other methodological refinements) in studies of different harmful factors in the etiology of disease. For now, clinicians seem to prefer NNTs, while biostatisticians prefer more traditional measures of impact such as attributable risk and etiologic fraction.32 Journals devoted to EBM33–36 regularly update the repertory of the most important treatment impact indexes. Both intervention efficacy or effectiveness (as discussed later) are measured the same way; only the nature of the clinical trial will decide what is measured (ideal or field conditions).
The practicalities of evidence-based medicine
Published in Tony Lockett, Evidence-based and Cost-effective Medicine for the Uninitiated, 2018
This is the crucial question. A 25% reduction in mortality may sound impressive – but is it worth the total ‘cost’ of treatment? A useful concept to assist this evaluation is the number needed to treat (NNT) and the number needed to harm (NNH).
Epidemiology and its uses
Published in Liam J. Donaldson, Paul D. Rutter, Donaldsons' Essential Public Health, 2017
Liam J. Donaldson, Paul D. Rutter
Most measures reported in randomized controlled trials are the same as those reported for the study types already described. One additional measure that arises particularly in randomized controlled trials is the number needed to treat (NNT). This reports the number of people who must be given the intervention under study in order for one life to be saved. The calculation is made by comparing mortality in the intervention and control groups. For example, aspirin is an effective treatment for myocardial infarction. But not everybody who is treated with aspirin lives, and neither does everybody who is not given aspirin die. The studies have shown that for every 25 people treated with aspirin, 1 more person survives. This is the number needed to treat. When an intervention causes harm, rather than benefit, an equivalent calculation can be made of the number needed to harm (NNH).
Hepatotoxicity associated with ribociclib among breast cancer patients
Published in Acta Oncologica, 2021
Stefania Finnsdottir, Asgerdur Sverrisdottir, Einar S. Björnsson
In the current report, two clinically apparent cases of grade 4 hepatotoxicity were observed among 43 patients treated with ribociclib. In the MONALEESA-2, 3, and 7 clinical trials, grade 3 or 4 ALT elevation occurred in 1 out of 11 (9.3%), 12 (8.5%), and 19 (5.0%) patients, respectively [1,7,8]. In a recently published study on the efficacy and safety of CDK4/6 inhibitors in 88 patients in Sweden, 10 patients (11.4%) discontinued treatment due to toxicity, thereof one due to hepatotoxicity after receiving ribociclib [2]. Clinically apparent liver injury due to drugs is rare. For example, approximately 1 out of 2300 who were treated with amoxicillin–clavulanate developed DILI and among most other potentially hepatotoxic drugs the risk of DILI was less frequent [9]. Thus, two out of 43 patients treated is a low number needed to harm.
Pembrolizumab plus axitinib and nivolumab plus ipilimumab as first-line treatments of advanced intermediate- or poor-risk renal-cell carcinoma: a number needed to treat analysis from the Brazilian private perspective
Published in Journal of Medical Economics, 2021
Tobias Engel Ayer Botrel, Márcia Datz Abadi, Laura Chabrol Haas, Cássia Rita Pereira da Veiga, Dominihemberg de Vasconcelos Ferreira, Denis Leonardo Jardim
A study conducted from the Brazilian private healthcare system perspective calculated a monthly average adverse event costs per patient for nivolumab of 199.83 BRL, representing only 0.5% of total treatment costs46. The available data suggest that adverse event management costs may not significantly impact the conclusion. On the other hand, the difference in the tolerability profile between pembrolizumab plus axitinib and nivolumab plus ipilimumab can cause different economic burdens for the Brazilian health system. Given the importance of the topic, future research should address this issue. Moreover, future research can evaluate the number needed to harm (NNH), which indicates how many people need to be treated for one patient to have an adverse event. NNH analysis evaluates the risk associated with the treatments and can inform the decision making from a different perspective.
Number needed to treat analysis applied to pembrolizumab plus chemotherapy for first-line treatment of non-squamous non-small cell lung cancer
Published in Journal of Medical Economics, 2021
Luciano Paladini, Cássia Rita Pereira da Veiga, Érica Cerqueira, Laura Chabrol Haas, Márcia Datz Abadi, Clarissa Seródio Baldotto
Although the COPE conceptualization18 based on the costs assessment related to drug use provides a quick and simple view on the intervention cost at a population level, many other direct medical costs are impacted when using intervention in real-life practice. In this sense, the limitations related to the COPE in the present study include costs related to drug use only in the calculation while excluding drug administration, patient follow-up and non-drug disease management, adverse event management, and subsequent therapy use. Finally, since it was not this study objective, the number needed to harm (NNH) was not assessed, making it impossible to analyze the harm-benefit balance. Further studies should be conducted to provide different estimates such as NNH and use the NNT in a cost-effectiveness analysis.