Outcome Reporting Bias in COVID-19 mRNA Vaccine Clinical Trials.

Relative risk reduction and absolute risk reduction measures in the evaluation of clinical trial data are poorly understood by health professionals and the public. The absence of reported absolute risk reduction in COVID-19 vaccine clinical trials can lead to outcome reporting bias that affects the interpretation of vaccine efficacy. The present article uses clinical epidemiologic tools to critically appraise reports of efficacy in Pfzier/BioNTech and Moderna COVID-19 mRNA vaccine clinical trials. Based on data reported by the manufacturer for Pfzier/BioNTech vaccine BNT162b2, this critical appraisal shows: relative risk reduction, 95.1%; 95% CI, 90.0% to 97.6%; p = 0.016; absolute risk reduction, 0.7%; 95% CI, 0.59% to 0.83%; p < 0.000. For the Moderna vaccine mRNA-1273, the appraisal shows: relative risk reduction, 94.1%; 95% CI, 89.1% to 96.8%; p = 0.004; absolute risk reduction, 1.1%; 95% CI, 0.97% to 1.32%; p < 0.000. Unreported absolute risk reduction measures of 0.7% and 1.1% for the Pfzier/BioNTech and Moderna vaccines, respectively, are very much lower than the reported relative risk reduction measures. Reporting absolute risk reduction measures is essential to prevent outcome reporting bias in evaluation of COVID-19 vaccine efficacy.

The optimal cut-off value in fit-based colorectal cancer screening: An observational study.

BACKGROUND: Colorectal cancer (CRC) screening programs using fecal immunochemical test (FIT) have to choose a cut-off value to decide which citizens to recall for colonoscopy. The evidence on the optimal cut-off value is sparse and based on studies with a low number of cancer cases. METHODS: This observational study used data from the Danish Colorectal Cancer Screening Database. Sensitivity and specificity were estimated for various cut-off values based on a large number of cancers. Traditionally optimal cut-off values are found by weighting sensitivity and specificity equally. As this might result in too many unnecessary colonoscopies we also provide optimal cut-off values for different weighting of sensitivity and specificity/number of needed colonoscopies to detect one cancer. RESULTS: Weighting sensitivity and specificity equally gives an optimal cut-off value of 45 ng Hb/ml. This, however, means making 24 colonoscopies to detect one cancer. Weighting sensitivity lower and for example, aiming at making about 16 colonoscopies to detect one cancer, gives an optimal cut-off value of 125 ng Hb/ml. CONCLUSIONS: The optimal cut-off value in an FIT population-based screening program is 45 ng Hb/ml, when as traditionally sensitivity and specificity are weighted equally. If, however, 24 colonoscopies needed to detect one cancer is too huge a burden on the health care system and the participants, 80, 125, 175, and 350 ng Hb/ml are optimal cut-off values when only 19/16/14/10 colonoscopies are accepted to find one cancer.

Number (of Whom?) Needed to Treat (with What?): Exposures, Population Interventions, and the Number Needed to Treat.

The number needed to treat (NNT) is a widely used measure of the potential impact of a treatment or intervention, but it is often calculated and discussed in ways which oversimplify critical issues. Specifically, the NNT itself depends on the population under study and the specific form that "treatment" would take in that population. We discuss how understanding the difference between the effect of removing a harmful exposure and the effect of deploying a specific intervention to remove that harmful exposure can affect the calculation and interpretation of an NNT. Our discussion extends a previously described framework distinguishing exposure effects from population intervention effects.

Years of Life Lost for Older Patients After Colorectal Cancer Diagnosis.

BACKGROUND: An aging population combined with an increased colorectal cancer (CRC) incidence in the older population will increase its prevalence in the elderly, questioning how many years of life are lost (YLLs) in these patients. PATIENTS AND METHODS: Data from 32,568 Dutch CRC patients ≥ 80 years were used to estimate the number of YLLs after diagnosis, using a reference age-, sex- and year-of-onset-matched cohort derived from national life tables. YLLs were additionally adjusted by comorbidities. Number needed to treat (NNT) was used as measure of surgical effect size. RESULTS: Surgery was applied in 74.9% of patients leading to 1.3 YLLs, being superior in 86.1% of cases with respect to alternative therapies (YLLs 4.8 years) and resulting in a number of two patients needed to operate to achieve one positive outcome. YLLs and NNTs depended on CRC stage, patient' age and comorbidities. For Stage I-II patients in the best clinical conditions (80-85 years without comorbidities), YLLs increased up to 4.1 years after surgery and up to 8.8 years without surgery (NNT 3). For Stage III patients, the NNT of surgery varied between 2 when they were in the best clinical conditions and 4 when they were older with high comorbidities. In Stage IV patients, the NNT ranged between 6 and 31. CONCLUSIONS: YLLs represents a novel approach to evaluate CRC prognosis. Stage I-III surgical patients can have a life expectancy similar to that of general population, being the NNT of surgery reasonably small compared with alternatives. Personalized comorbidity data are needed to confirm present findings.

The number needed to treat in pairwise and network meta-analysis and its graphical representation.

OBJECTIVE: The objective of this study was to present ways to graphically represent a number needed to treat (NNT) in (network) meta-analysis (NMA). STUDY DESIGN AND SETTING: A barrier to using NNT in NMA when an odds ratio (OR) or risk ratio (RR) is used is the determination of a single control event rate (CER). We discuss approaches to calculate a CER, and illustrate six graphical methods for NNT from NMA. We illustrate the graphical approaches using an NMA of cognitive enhancers for Alzheimer's dementia. RESULTS: The NNT calculation using a relative effect measure, such as OR and RR, requires a CER value, but different CERs, including mean CER across studies, pooled CER in meta-analysis, and expert opinion-based CER may result in different NNTs. An NNT from NMA can be presented in a bar plot, Cates plot, or forest plot for a single outcome, and a bubble plot, scatterplot, or rank-heat plot for ≥2 outcomes. Each plot is associated with different properties and can serve different needs. CONCLUSION: Caution is needed in NNT interpretation, as considerations such as selection of effect size and CER, and CER assumption across multiple comparisons, may impact NNT and decision-making. The proposed graphs are helpful to interpret NNTs calculated from (network) meta-analyses.

Personalized Statin Therapy and Coronary Atherosclerotic Plaque Burden in Asymptomatic Low/Intermediate-Risk Individuals.

BACKGROUND: Current guidelines for the primary prevention of atherosclerotic cardiovascular disease are based on the estimation of a predicted 10-year cardiovascular disease risk and the average relative risk reduction estimates from statin trials. In the clinical setting, however, decision-making is better informed by the expected benefit for the individual patient, which is typically lacking. Consequently, a personalized statin benefit approach based on absolute risk reduction over 10 years (ARR10 benefit threshold ≥2.3%) has been proposed as a novel approach. However, how this benefit threshold relates with coronary plaque burden in asymptomatic individuals with low/intermediate cardiovascular disease risk is unknown. AIMS: In this study, we compared the predicted ARR10 obtained in each individual with plaque burden detected by coronary computed tomography angiography. METHODS AND RESULTS: Plaque burden (segment volume score, segment stenosis score, and segment involvement score) was assessed in prospectively recruited asymptomatic subjects (n = 70; 52% male; median age 56 years [interquartile range 51-64 years]) with low/intermediate Framingham risk score (< 20%). The expected ARR10 with statin in the entire cohort was 2.7% (1.5-4.6%) with a corresponding number needed to treat over 10 years of 36 (22-63). In subjects with an ARR10 benefit threshold ≥2.3% (vs. < 2.3%), plaque burden was significantly higher (p = 0.02). CONCLUSION: These findings suggest that individuals with higher coronary plaque burden are more likely to get greater benefit from statin therapy even among asymptomatic individuals with low cardiovascular risk.

Likelihood of Being Helped or Harmed as a Measure of Clinical Outcomes in Psychopharmacology.

The likelihood of being helped or harmed (LHH) ratio is an indirect measure of effect size. It tells the reader how much as likely a patient is to benefit from a treatment as to suffer from an adverse outcome with that treatment; larger values for LHH indicate more favorable treatment outcomes. The numerator for LHH is usually a measure of response or remission with a treatment, and the denominator is usually a measure of all-cause discontinuation or discontinuation due to adverse events; so, there can be more than 1 LHH statistic for a study. As an example, an LHH of 5 could indicate that after removal of placebo effects a patient is 5 times as likely to respond to a treatment as to drop out of treatment because of the experience of an adverse event. This article explains the LHH with the help of a worked example, shows how the LHH can be derived from the numbers needed to treat and harm (NNT, NNH) statistics, discusses practical issues related to the concept, and considers its limitations. The LHH is little used in clinical psychopharmacology, and authors who report or review clinical trial data should consider presenting all the LHH information that is clinically relevant in addition to NNT, NNH, and other information. Because LHH statistics present the results of risk-benefit trade-off analyses, they can help clinicians and patients more easily evaluate potential treatments during decision-making processes.

Financial Impact of Direct-Acting Oral Anticoagulants in Medicaid: Budgetary Assessment Based on Number Needed to Treat.

BACKGROUND: Faced with rising healthcare costs, state Medicaid programs need short-term, easily calculated budgetary estimates for new drugs, accounting for medical cost offsets due to clinical advantages. OBJECTIVE: To estimate the budgetary impact of direct-acting oral anticoagulants (DOACs) compared with warfarin, an older, lower-cost vitamin K antagonist, on 12-month Medicaid expenditures for nonvalvular atrial fibrillation (NVAF) using number needed to treat (NNT). METHOD: Medicaid utilization files, 2009 through second quarter 2015, were used to estimate OAC cost accounting for generic/brand statutory minimum (13/23%) and assumed maximum (13/50%) manufacturer rebates. NNTs were calculated from clinical trial reports to estimate avoided medical events for a hypothetical population of 500,000 enrollees (approximate NVAF prevalence × Medicaid enrollment) under two DOAC market share scenarios: 2015 actual and 50% increase. Medical service costs were based on published sources. Costs were inflation-adjusted (2015 US$). RESULTS: From 2009-2015, OAC reimbursement per claim increased by 173 and 279% under maximum and minimum rebate scenarios, respectively, while DOAC market share increased from 0 to 21%. Compared with a warfarin-only counterfactual, counts of ischemic strokes, intracranial hemorrhages, and systemic embolisms declined by 36, 280, and 111, respectively; counts of gastrointestinal hemorrhages increased by 794. Avoided events and reduced monitoring, respectively, offset 3-5% and 15-24% of increased drug cost. Net of offsets, DOAC-related cost increases were US$258-US$464 per patient per year (PPPY) in 2015 and US$309-US$579 PPPY after market share increase. CONCLUSIONS: Avoided medical events offset a small portion of DOAC-related drug cost increase. NNT-based calculations provide a transparent source of budgetary-impact information for new medications.

Meta-analysis for orthodontists: Part I--How to choose effect measure and statistical model.

Systematic reviews ideally provide a comprehensive and unbiased summary of existing evidence from clinical studies, whilst meta-analysis combines the results of these studies to produce an overall estimate. Collectively, this makes them invaluable for clinical decision-making. Although the number of published systematic reviews and meta-analyses in orthodontics has increased, questions are often raised about their methodological soundness. In this primer, the first steps of meta-analysis are discussed, namely the choice of an effect measure to express the results of included studies, and the choice of a statistical model for the meta-analysis. Clinical orthodontic examples are given to explain the various options available, the thought process behind the choice between them and their interpretation.

[Minimum thresholds under scrutiny]. / Mindestmengen auf dem Prüfstand.

Minimum volume thresholds for specific medical treatments have been implemented in Germany since 2004. In the last 9 years the catalogue of procedures, which is determined by the Federal Joint Committee, has changed continuously and currently consists of 8 procedures. In this article the basis of decision making for the enrolment in the catalogue of procedures and the determination of minimum volume thresholds are examined. An overview of systematic reviews was published in 2012 outlining the correlation between the volume components and medical outcome. The body of evidence identified is compared to the current regulatory conditions of the Federal Joint Committee.

[Number needed to treat: Interpretation and estimation in multivariable analyses and censored data]. / Número necesario de tratamientos: interpretación y estimación en análisis multivariables y con datos censurados.

Number needed to treat has been recommended as an easy way to transmit results from a trial, especially controlled clinical trials. Most articles estimate it from a 2×2 table, as the inverse of the absolute risk reduction. However, some limitations have been pointed out: The interpretation is not as easy as claimed, confidence intervals are frequently not estimated, and the estimation from 2×2 tables is inadequate when the main effect measure has been estimated adjusting for confounding factors. In this paper, we revise how to obtain point estimations and confidence intervals of number needed to treat in 4 situations: 2×2tables, logistic regression, Kaplan-Meier method, and Cox regression.

When does a difference make a difference? Interpretation of number needed to treat, number needed to harm, and likelihood to be helped or harmed.

Although great effort is made in clinical trials to demonstrate statistical superiority of one intervention vs. another, insufficient attention is paid regarding the clinical relevance or clinical significance of the observed outcomes. Effect sizes are not always reported. Available absolute effect size measures include Cohen's d, area under the curve, success rate difference, attributable risk and number needed to treat (NNT). Of all of these measures, NNT is arguably the most clinically intuitive and helps relate effect size difference back to real-world concerns of clinical practice. This commentary reviews the formula for NNT, and proposes acceptable values for NNT and its analogue, number needed to harm (NNH), using examples from the medical literature. The concept of likelihood to be helped or harmed (LHH), calculated as the ratio of NNH to NNT, is used to illustrate trade-offs between benefits and harms. Additional considerations in interpreting NNT are discussed, including the importance of defining acceptable response, adverse outcomes of interest, the effect of time, and the importance of individual baseline characteristics.

Use of number needed to treat in cost-effectiveness analyses.

OBJECTIVE: To review the use of number needed to treat (NNT) and/or number needed to harm (NNH) values to determine their relevance in helping clinicians evaluate cost-effectiveness analyses (CEAs). DATA SOURCES: PubMed and EconLit were searched from 1966 to September 2012. STUDY SELECTION AND DATA EXTRACTION: Reviews, editorials, non-English-language articles, and articles that did not report NNT/NNH or cost-effectiveness ratios were excluded. CEA studies reporting cost per life-year gained, per quality-adjusted life-year (QALY), or other cost per effectiveness measure were included. Full texts of all included articles were reviewed for study information, including type of journal, impact factor of the journal, focus of study, data source, publication year, how NNT/NNH values were reported, and outcome measures. DATA SYNTHESIS: A total of 188 studies were initially identified, with 69 meeting our inclusion criteria. Most were published in clinician-practice-focused journals (78.3%) while 5.8% were in policy-focused journals, and 15.9% in health-economics-focused journals. The majority (72.4%) of the articles were published in high-impact journals (impact factor >3.0). Many articles focused on either disease treatment (40.5%) or disease prevention (40.5%). Forty-eight percent reported NNT as a part of the CEA ratio per event. Most (53.6%) articles used data from literature reviews, while 24.6% used data from randomized clinical trials, and 20.3% used data from observational studies. In addition, 10% of the studies implemented modeling to perform CEA. CONCLUSIONS: CEA studies sometimes include NNT ratios. Although it has several limitations, clinicians often use NNT for decision-making, so including NNT information alongside CEA findings may help clinicians better understand and apply CEA results. Further research is needed to assess how NNT/NNH might meaningfully be incorporated into CEA publications.

[Use and interpretation of the number needed to treat in urological practice]. / Verwendung und Bedeutung der "number needed to treat" in der urologischen Praxis.

The number needed to treat (NNT) is a useful way for clinicans to describe the benefit or harm of a treatment as well as the costs involved. When interpreting the NNT it is essential to use the NNT in a clinically equivalent and appropriate setting. When evaluating the consequences of a treatment clinicians should make sure that the patients being treated have the same risk profile asthe study patients. Differences in duration of follow-up and baseline risks can cause significant changes in the NNT; therefore, NNT should be evaluated in addition to relative risk differences and baseline risk to reduce any ambivalence in the assessment of a treatment. This review provides insights into the assessment and clinical use of NNT in the practice.