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Objectives: To determine if enteral feeding with donor human milk compared with formula milk reduces the incidence of necrotising enterocolitis (NEC) in preterm or low birthweight infants. Methods: Systematic review and meta-analysis of randomised controlled trials. Results: Four small trials, all initiated more than 20 years ago, fulfilled the prespecified inclusion criteria. None of the trials individually found any statistically significant difference in the incidence of NEC. However, meta-analysis found that feeding with donor human milk was associated with a significantly reduced relative risk (RR) of NEC. Infants who received donor human milk were three times less likely to develop NEC (RR 0.34; 95% confidence interval (CI) 0.12 to 0.99), and four times less likely to have confirmed NEC (RR 0.25; 95% CI 0.06 to 0.98) than infants who received formula milk. Conclusions: It may be appropriate to consider further larger trials to compare growth, development, and the incidence of adverse outcomes, including NEC, in preterm infants who receive donor human milk versus formula milk.

In clinical trials, the relative risk or risk ratio (RR) is a mainstay of reporting of the effect magnitude for an intervention. The RR is the ratio of the probability of an outcome in an intervention group to its probability in a control group. Thus, the RR provides a measure of change in the likelihood of an event linked to a given intervention. This measure has been widely used because it is today considered a measure with “portability” across varying outcome prevalence, especially when the outcome is rare. It turns out, however, that there is a much more important problem with this ratio, and this paper aims to demonstrate this problem. We used mathematical derivation to determine if the RR is a measure of effect magnitude alone (i.e., a larger absolute value always indicating a stronger effect) or not. We also used the same derivation to determine its relationship to the prevalence of an outcome. We confirm the derivation results with a follow-up analysis of 140,620 trials scraped from the Cochrane. We demonstrate that the RR varies for reasons other than the magnitude of the effect because it is a ratio of two posterior probabilities, both of which are dependent on baseline prevalence of an outcome. In addition, we demonstrate that the RR shifts toward its null value with increasing outcome prevalence. The shift toward the null happens regardless of the strength of the association between intervention and outcome. The odds ratio (OR), the other commonly used ratio, measures solely the effect magnitude and has no relationship to the prevalence of an outcome in a study nor does it overestimate the RR as is commonly thought. The results demonstrate the need to (1) end the primary use of the RR in clinical trials and meta-analyses as its direct interpretation is not meaningful, (2) replace the RR by the OR, and (3) only use the postintervention risk recalculated from the OR for any expected level of baseline risk in absolute terms for purposes of interpretation such as the number needed to treat. These results will have far-reaching implications such as reducing misleading results from clinical trials and meta-analyses and ushering in a new era in the reporting of such trials or meta-analyses in practice.

Background In the U.S., the prevalence of diabetes and hypertension are higher among African American/Black (Black), Latinx, and Filipino adults than non-Hispanic White (White) and Chinese adults. We compared the racial/ethnic-specific prevalence of several modifiable cardiometabolic risks in an insured adult population to identify behaviors that may drive racial/ethnic differences in cardiometabolic health. Methods This cross-sectional study used data for middle-aged (35–64) and older (65–79) Kaiser Permanente Northern California (KPNC) adult health plan members. Smoking status and BMI were derived from electronic health record data. Weighted pooled self-reported data from the 2014/2015 and 2017 KPNC Member Health Survey cycles were used to estimate daily number of servings of fruits/vegetables, general sodium avoidance, sugar-sweetened beverage (SSB) consumption frequency, alcohol use within daily recommended limit, weekly exercise frequency, and number of hours of sleep daily. Age-standardized estimates of all cardiometabolic risks were produced for middle-aged and older-aged women and men in the five racial/ethnic groups. Analyses focused on racial/ethnic differences within age-gender groups and gender and age group differences within racial/ethnic groups. Results In both age groups, Black, Latinx, and Filipino adults were more likely than White and Chinese adults to have overweight and obesity and were less likely to engage in health promoting dietary (fruit/vegetable and SSB consumption, sodium avoidance (women only)) and sleep behaviors. Middle-aged Black and Filipino men were more likely than White men to be current smokers. Less racial/ethnic variation was seen in exercise frequency. Significant gender differences were observed for dietary behaviors overall and within racial/ethnic groups, especially among middle-aged adults; however, these gender differences were smaller for sleep and exercise. Age differences within gender and racial/ethnic groups were less consistent. Racial/ethnic and gender differences in these behaviors were also seen in the subsample of adults with diabetes and/or hypertension and in the subsample of adults who reported they were trying to engage in health promoting behaviors. Conclusions Black, Latinx, and Filipino adults were more likely than White and Chinese adults to report dietary and sleep behaviors associated with development and worsening of cardiometabolic conditions, with men exhibiting poorer dietary behaviors than women.

This paper focuses on a situation in which a set of treatments is associated with a response through a set of supplementary variables in linear models as well as discrete models. Under the situation, we demonstrate that the causal effect can be estimated more accurately from the set of supplementary variables. In addition, we show that the set of supplementary variables can include selection variables and proxy variables as well. Furthermore, we propose selection criteria for supplementary variables based on the estimation accuracy of causal effects. From graph structures based on our results, we can judge certain situations under which the causal effect can be estimated more accurately by supplementary variables and reliably evaluate the causal effects from observed data.

In a recent paper we suggest that the relative risk (RR) be replaced with the odds ratio (OR) as the effect measure of choice in clinical epidemiology. In response, Chu, and colleagues raise several points that argue for the status quo. In this paper, we respond to their response. We use the same examples given by Chu and colleagues to recompute estimates of effect and demonstrate the problem with the RR. We reaffirm the following findings: a) the OR and RR measure different things and their numerical difference is only important if misinterpreted b) this potential misinterpretation is a trivial issue compared to the lack of portability of the RR c) the same examples reaffirm non-portability of the RR and demonstrate how misleading the results might be in contrast to the OR, which is independent of the baseline risk d) the concept of non-collapsibility for the OR should be expected in the presence of a non-confounding risk factor, and is not a bias e) the log link in regression models that generate RRs as well as the use of RRs in meta-analysis is shown to be problematic using the same examples. The OR should replace the RR in clinical research and meta-analyses though there should be conversion of the end product into ratios or differences of risk, solely, for interpretation. To this end we provide a Stata module (logittorisk) for this purpose.

Quantifying the effect of treatment is crucial for clinical decision-making. Effect measures can be expressed in absolute terms, such as risk difference, number needed to treat, or number needed to harm, or in relative terms, including risk ratio, odds ratio, and incidence rate ratio. Although all are derived from the same data, they provide different perspectives: relative measures capture proportional changes, while absolute measures translate these into clinically meaningful terms. Confidence intervals (CIs) are essential to assess the precision of these estimates and to evaluate their statistical significance and potential clinical relevance. In this methodological note, we illustrate the use of these measures with examples from studies on venous thromboembolism, a clinical field where both benefits and risks of anticoagulant therapy (i.e., thrombosis and bleeding) must be carefully balanced. Clinicians and researchers should be aware of the strengths and limitations of each measure. A balanced interpretation, integrating absolute and relative metrics together with their CIs, is central to properly assessing treatment effects and to support evidence-based decision-making.

In randomized clinical trials and observational studies alike, it is difficult and challenging to collect gold-standard outcome measures for all participants. Although it would be ideal to use gold-standard measures, the costs and logistics of collecting them are often prohibitive. Therefore, surrogate or proxy measures or screening survey instruments are more often used to mitigate such difficulties, yet at the expense of misclassification errors and consequent biased statistical inferences. In this paper, when misclassification errors of proxy measures in comparison to a gold-standard measure are available through external or internal validation samples, we determined appropriate offset values in generalized binary regression models as a function of the proxy measure to eliminate biases of estimated effects in terms of risk difference, relative risk, and odds ratio that are incurred due to misclassification errors. Simulation studies were conducted to empirically demonstrate and verify the approach using appropriate offset values specific to each binary effect measure for estimating unbiased effects. Both point estimates of all effect measures and standard errors of regression coefficients obtained from the proposed offset-adjusted binary models were shown to be unbiased.

Background Reporting of absolute risk difference (RD) is recommended for clinical and epidemiological prospective studies. In analyses of multicenter studies, adjustment for center is necessary when randomization is stratified by center or when there is large variation in patients outcomes across centers. While regression methods are used to estimate RD adjusted for baseline predictors and clustering, no formal evaluation of their performance has been previously conducted. Methods We performed a simulation study to evaluate 6 regression methods fitted under a generalized estimating equation framework: binomial identity, Poisson identity, Normal identity, log binomial, log Poisson, and logistic regression model. We compared the model estimates to unadjusted estimates. We varied the true response function (identity or log), number of subjects per center, true risk difference, control outcome rate, effect of baseline predictor, and intracenter correlation. We compared the models in terms of convergence, absolute bias and coverage of 95 % confidence intervals for RD. Results The 6 models performed very similar to each other for the majority of scenarios. However, the log binomial model did not converge for a large portion of the scenarios including a baseline predictor. In scenarios with outcome rate close to the parameter boundary, the binomial and Poisson identity models had the best performance, but differences from other models were negligible. The unadjusted method introduced little bias to the RD estimates, but its coverage was larger than the nominal value in some scenarios with an identity response. Under the log response, coverage from the unadjusted method was well below the nominal value (<80 % ) for some scenarios. Conclusions We recommend the use of a binomial or Poisson GEE model with identity link to estimate RD for correlated binary outcome data. If these models fail to run, then either a logistic regression, log Poisson regression, or linear regression GEE model can be used.

The number needed to treat (NNT) is a simple-to-understand absolute effect measure. However, it is only sensible when the risk difference is statistically significant. We highlight two important limitations of using NNT in the context of decision-making (developing a guideline, a policy decision, or a health technology assessment). The first limitation of NNT relates to difficulties in expressing and interpreting the confidence interval (CI) for the NNT when the CI of the risk difference includes the null (ie, the results are not statistically significant). This CI of NNT will be disjointed and will include implausible values. The second limitation of NNT relates to the increased complexity of trading off benefits and harms on the NNT scale. This proposal calls for abandoning the use of NNT from decision-making contexts. The number needed to treat (NNT) has statistical and methodological limitations that make it unhelpful in the context of developing clinical practice guidelines and policy decisions. •The number needed to treat (NNT) is a simple-to-understand absolute effect measure.•When results are nonsignificant, the confidence interval of NNT includes infinity.•Making decisions based on multiple outcomes is complicated with NNTs.•Due to several limitations, we propose abandoning NNT from decision-making contexts.

Background Incisional hernias are one of the most common long-term complications associated with open abdominal surgery. The aim of this review and meta-analysis was to systematically assess laparoscopic versus open abdominal surgery as a general surgical strategy in all available indications in terms of incisional hernia occurrence. Methods A systematic literature search was performed to identify randomized controlled trials comparing incisional hernia rates after laparoscopic versus open abdominal surgery in all indications. Random effects meta-analyses were calculated and presented as risk differences (RD) with their corresponding 95 % confidence intervals (CI). Results 24 trials (3490 patients) were included. Incisional hernias were significantly reduced in the laparoscopic group (RD −0.06, 95 % CI [−0.09, −0.03], p  = 0.0002, I 2  = 75). The advantage of the laparoscopic procedure persisted in the subgroup of total-laparoscopic interventions (RD −0.14, 95 % CI [−0.22, −0.06], p  = 0.001, I 2  = 87 %), whereas laparoscopically assisted procedures did not show a significant reduction of incisional hernias compared to open surgery (RD −0.01, 95 % CI [−0.03, 0.01], p  = 0.31, I 2  = 35 %). Wound infections were significantly reduced in the laparoscopic group (RD −0.06, 95 % CI [−0.09, −0.03], p  < 0.0001, I 2  = 35 %); overall postoperative morbidity was comparable in both groups (RD −0.06, 95 % CI [−0.13, 0.00], p  = 0.06; I 2  = 64 %). Open abdominal surgery showed a significantly longer hospital stay compared to laparoscopy (RD −1.92, 95 % CI [−2.67, −1.17], p  < 0.00001, I 2  = 87 %). At short-term follow-up, quality of life was in favor of laparoscopy. Conclusions Incisional hernias are less frequent using the total-laparoscopic approach instead of open abdominal surgery. Whenever possible, the less traumatic access should be chosen.