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Cardiovascular risk factors are associated with an increased risk of dementia. We assessed whether a multidomain intervention targeting these factors can prevent dementia in a population of community-dwelling older people. In this open-label, cluster-randomised controlled trial, we recruited individuals aged 70–78 years through participating general practices in the Netherlands. General practices within each health-care centre were randomly assigned (1:1), via a computer-generated randomisation sequence, to either a 6-year nurse-led, multidomain cardiovascular intervention or control (usual care). The primary outcomes were cumulative incidence of dementia and disability score (Academic Medical Center Linear Disability Score [ALDS]) at 6 years of follow-up. The main secondary outcomes were incident cardiovascular disease and mortality. Outcome assessors were masked to group assignment. Analyses included all participants with available outcome data. This trial is registered with ISRCTN, number ISRCTN29711771. Between June 7, 2006, and March 12, 2009, 116 general practices (3526 participants) within 26 health-care centres were recruited and randomly assigned: 63 (1890 participants) were assigned to the intervention group and 53 (1636 participants) to the control group. Primary outcome data were obtained for 3454 (98%) participants; median follow-up was 6·7 years (21 341 person-years). Dementia developed in 121 (7%) of 1853 participants in the intervention group and in 112 (7%) of 1601 participants in the control group (hazard ratio [HR] 0·92, 95% CI 0·71–1·19; p=0·54). Mean ALDS scores measured during follow-up did not differ between groups (85·7 [SD 6·8] in the intervention group and 85·7 [7·1] in the control group; adjusted mean difference −0·02, 95% CI −0·38 to 0·42; p=0·93). 309 (16%) of 1885 participants died in the intervention group, compared with 269 (16%) of 1634 participants in the control group (HR 0·98, 95% CI 0·80–1·18; p=0·81). Incident cardiovascular disease did not differ between groups (273 [19%] of 1469 participants in the intervention group and 228 [17%] of 1307 participants in the control group; HR 1·06, 95% CI 0·86–1·31; p=0·57). A nurse-led, multidomain intervention did not result in a reduced incidence of all-cause dementia in an unselected population of older people. This absence of effect might have been caused by modest baseline cardiovascular risks and high standards of usual care. Future studies should assess the efficacy of such interventions in selected populations. Dutch Ministry of Health, Welfare and Sport; Dutch Innovation Fund of Collaborative Health Insurances; and Netherlands Organisation for Health Research and Development.

Age-associated motor and cognitive deficits increase the risk of falls, a major cause of morbidity and mortality. Because of the significant ramifications of falls, many interventions have been proposed, but few have aimed to prevent falls via an integrated approach targeting both motor and cognitive function. We aimed to test the hypothesis that an intervention combining treadmill training with non-immersive virtual reality (VR) to target both cognitive aspects of safe ambulation and mobility would lead to fewer falls than would treadmill training alone. We carried out this randomised controlled trial at five clinical centres across five countries (Belgium, Israel, Italy, the Netherlands, and the UK). Adults aged 60–90 years with a high risk of falls based on a history of two or more falls in the 6 months before the study and with varied motor and cognitive deficits were randomly assigned by use of computer-based allocation to receive 6 weeks of either treadmill training plus VR or treadmill training alone. Randomisation was stratified by subgroups of patients (those with a history of idiopathic falls, those with mild cognitive impairment, and those with Parkinson's disease) and sex, with stratification per clinical site. Group allocation was done by a third party not involved in onsite study procedures. Both groups aimed to train three times per week for 6 weeks, with each session lasting about 45 min and structured training progression individualised to the participant's level of performance. The VR system consisted of a motion-capture camera and a computer-generated simulation projected on to a large screen, which was specifically designed to reduce fall risk in older adults by including real-life challenges such as obstacles, multiple pathways, and distracters that required continual adjustment of steps. The primary outcome was the incident rate of falls during the 6 months after the end of training, which was assessed in a modified intention-to-treat population. Safety was assessed in all patients who were assigned a treatment. This study is registered with ClinicalTrials.gov, NCT01732653. Between Jan 6, 2013, and April 3, 2015, 302 adults were randomly assigned to either the treadmill training plus VR group (n=154) or treadmill training alone group (n=148). Data from 282 (93%) participants were included in the prespecified, modified intention-to-treat analysis. Before training, the incident rate of falls was similar in both groups (10·7 [SD 35·6] falls per 6 months for treadmill training alone vs 11·9 [39·5] falls per 6 months for treadmill training plus VR). In the 6 months after training, the incident rate was significantly lower in the treadmill training plus VR group than it had been before training (6·00 [95% CI 4·36–8·25] falls per 6 months; p<0·0001 vs before training), whereas the incident rate did not decrease significantly in the treadmill training alone group (8·27 [5·55–12·31] falls per 6 months; p=0·49). 6 months after the end of training, the incident rate of falls was also significantly lower in the treadmill training plus VR group than in the treadmill training group (incident rate ratio 0·58, 95% CI 0·36–0·96; p=0·033). No serious training-related adverse events occurred. In a diverse group of older adults at high risk for falls, treadmill training plus VR led to reduced fall rates compared with treadmill training alone. European Commission.

Immobilisation predicts adverse outcomes in patients in the surgical intensive care unit (SICU). Attempts to mobilise critically ill patients early after surgery are frequently restricted, but we tested whether early mobilisation leads to improved mobility, decreased SICU length of stay, and increased functional independence of patients at hospital discharge. We did a multicentre, international, parallel-group, assessor-blinded, randomised controlled trial in SICUs of five university hospitals in Austria (n=1), Germany (n=1), and the USA (n=3). Eligible patients (aged 18 years or older, who had been mechanically ventilated for <48 h, and were expected to require mechanical ventilation for ≥24 h) were randomly assigned (1:1) by use of a stratified block randomisation via restricted web platform to standard of care (control) or early, goal-directed mobilisation using an inter-professional approach of closed-loop communication and the SICU optimal mobilisation score (SOMS) algorithm (intervention), which describes patients’ mobilisation capacity on a numerical rating scale ranging from 0 (no mobilisation) to 4 (ambulation). We had three main outcomes hierarchically tested in a prespecified order: the mean SOMS level patients achieved during their SICU stay (primary outcome), and patient's length of stay on SICU and the mini-modified functional independence measure score (mmFIM) at hospital discharge (both secondary outcomes). This trial is registered with ClinicalTrials.gov (NCT01363102). Between July 1, 2011, and Nov 4, 2015, we randomly assigned 200 patients to receive standard treatment (control; n=96) or intervention (n=104). Intention-to-treat analysis showed that the intervention improved the mobilisation level (mean achieved SOMS 2·2 [SD 1·0] in intervention group vs 1·5 [0·8] in control group, p<0·0001), decreased SICU length of stay (mean 7 days [SD 5–12] in intervention group vs 10 days [6–15] in control group, p=0·0054), and improved functional mobility at hospital discharge (mmFIM score 8 [4–8] in intervention group vs 5 [2–8] in control group, p=0·0002). More adverse events were reported in the intervention group (25 cases [2·8%]) than in the control group (ten cases [0·8%]); no serious adverse events were observed. Before hospital discharge 25 patients died (17 [16%] in the intervention group, eight [8%] in the control group). 3 months after hospital discharge 36 patients died (21 [22%] in the intervention group, 15 [17%] in the control group). Early, goal-directed mobilisation improved patient mobilisation throughout SICU admission, shortened patient length of stay in the SICU, and improved patients’ functional mobility at hospital discharge. Jeffrey and Judy Buzen.

Observational studies have consistently reported large reductions in COVID-19 risk among vaccinated individuals. However, critics have raised concerns that unmeasured confounding may entirely explain these associations. We combined the classical Cornfield inequality with a Monte Carlo sensitivity analysis to evaluate whether unmeasured confounding alone could plausibly account for the observed effectiveness of COVID-19 vaccines. The Cornfield inequality provides a lower bound on the strength of confounding required to explain a given association. The Monte Carlo analysis simulates uncertainty over possible confounder-exposure and confounder-outcome relationships by drawing from weakly informative prior distributions, allowing us to estimate the frequency with which such confounding would be sufficient. For an observed risk ratio of 0.08-consistent with early estimates for the Pfizer-BioNTech vaccine-the confounder would need to be both highly imbalanced (e.g., 10 times more prevalent among vaccinated individuals) and strongly protective (e.g., reducing disease risk by 99%). Simulation results showed that, under the specified assumptions, fewer than 2% of draws satisfied this condition. Even in the more moderate case of a risk ratio of 0.25 (e.g., AstraZeneca), the proportion remained below 6%. Our findings suggest that while residual confounding may attenuate effect estimates, it is statistically and epidemiologically implausible that unmeasured confounding alone could fully account for the magnitude of observed vaccine effectiveness. This framework combines the falsificatory logic of Cornfield bounds with the flexibility of simulation-based sensitivity analysis, providing a transparent tool for evaluating confounding-based explanations in observational research.

In previous clinical trials of patients with metastatic renal-cell carcinoma, patients treated with axitinib as second-line therapy had longer median progression-free survival than those treated with sorafenib. We therefore undertook a phase 3 trial comparing axitinib with sorafenib in patients with treatment-naive metastatic renal-cell carcinoma. In this randomised, open-label, phase 3 trial, patients with treatment-naive, measurable, clear-cell metastatic renal-cell carcinoma from 13 countries were stratified by Eastern Cooperative Oncology Group performance status, and then randomly assigned (2:1) by a centralised registration system to receive axitinib 5 mg twice daily, or sorafenib 400 mg twice daily. The primary endpoint was progression-free survival, assessed by masked independent review committee in the intention-to-treat population. This ongoing trial is registered at ClinicalTrials.gov, NCT00920816. Between June 14, 2010, and April 21, 2011, we randomly assigned 192 patients to receive axitinib, and 96 patients to receive sorafenib. The cutoff date for this analysis was July 27, 2012, when 171 (59%) of 288 patients died or had disease progression, as assessed by the independent review committee. There was no significant difference in median progression-free survival between patients treated with axitinib or sorafenib (10·1 months [95% CI 7·2–12·1] vs 6·5 months [4·7–8·3], respectively; stratified hazard ratio 0·77, 95% CI 0·56–1·05). Any-grade adverse events that were more common (≥10% difference) with axitinib than with sorafenib were diarrhoea (94 [50%] of 189 patients vs 38 [40%] of 96 patients), hypertension (92 [49%] vs 28 [29%]), weight decrease (69 [37%] vs 23 [24%]), decreased appetite (54 [29%] vs 18 [19%]), dysphonia (44 [23%] vs ten [10%]), hypothyroidism (39 [21%] vs seven [7%]), and upper abdominal pain (31 [16%] vs six [6%]); those more common with sorafenib than with axitinib included palmar-plantar erythrodysaesthesia (PPE; 37 [39%] of 96 patients vs 50 [26%] of 189), rash (19 [20%] vs 18 [10%]), alopecia (18 [19%] vs eight [4%]), and erythema (18 [19%] vs five [3%]). The most common grade 3 or 4 adverse events in patients treated with axitinib included hypertension (26 [14%] of 189 patients), diarrhoea (17 [9%]), asthenia (16 [8%]), weight decrease (16 [8%]), and PPE (14 [7%]); common grade 3 or 4 adverse events in patients treated with sorafenib included PPE (15 [16%] of 96 patients), diarrhoea (five [5%]), and asthenia (five [5%]). Serious adverse events were reported in 64 (34%) of 189 patients receiving axitinib, and 24 (25%) of 96 patients receiving sorafenib. Axitinib did not significantly increase progression-free survival in patients with treatment-naive metastatic renal-cell carcinoma compared with those treated with sorafenib, but did demonstrate clinical activity and an acceptable safety profile. Pfizer Inc.

Conventional exposure–response (E–R) analyses, such as logistic regression and time‐to‐event analysis using summary exposure metrics, are often conducted in oncology using data from the pivotal trial(s) with a single dose level to support dosing decisions. However, these E–R analyses, affected by multiple confounding factors, may mischaracterize true E–R relationships, potentially limiting their utility in dosing decisions. This study investigates potential mischaracterization in such analyses influenced by the following time‐dependent confounding factors: exposure accumulation, dose modification patterns, and event onset time. We used a simulation‐based approach to evaluate two E–R scenarios: ER1, where event time generated with a Weibull distribution is not affected by drug exposure, and ER2, where the response is driven by drug exposure via a joint PK‐tumor size model. Our analyses indicate that when using time‐dependent exposure metrics (e.g., average concentration till event/censoring), exposure accumulation tends to induce an inverse E–R trend, while dose modifications (interruptions/reductions) likely induce a positive E–R trend. Simulations suggest that employing static exposure metrics (e.g., first‐cycle or steady‐state) minimizes these biases. Additionally, adopting an Emax model aligned with the underground truth in ER2 in the E–R analyses could reduce bias. When significant dose modifications are present, including relevant data from a dose‐range study and employing modified methods for time‐dependent exposure derivation may help mitigate bias. Overall, using multiple exposure metrics (including static ones) to assess E–R consistency and interpreting the potential causal effects with totality of evidence (including dose–response results) should be implemented to better inform dosing decisions. Scheme of evaluation and mitigation of time‐dependent confounding effects in conventional exposure‐response analyses for oncology drugs.

Background Common methods for confounder identification such as directed acyclic graphs (DAGs), hypothesis testing, or a 10 % change-in-estimate (CIE) criterion for estimated associations may not be applicable due to (a) insufficient knowledge to draw a DAG and (b) when adjustment for a true confounder produces less than 10 % change in observed estimate (e.g. in presence of measurement error). Methods We compare previously proposed simulation-based approach for confounder identification that can be tailored to each specific study and contrast it with commonly applied methods (significance criteria with cutoff levels of p -values of 0.05 or 0.20, and CIE criterion with a cutoff of 10 %), as well as newly proposed two-stage procedure aimed at reduction of false positives (specifically, risk factors that are not confounders). The new procedure first evaluates potential for confounding by examination of correlation of covariates and applies simulated CIE criteria only if there is evidence of correlation, while rejecting a covariate as confounder otherwise. These approaches are compared in simulations studies with binary, continuous, and survival outcomes. We illustrate the application of our proposed confounder identification strategy in examining the association of exposure to mercury in relation to depression in the presence of suspected confounding by fish intake using the National Health and Nutrition Examination Survey (NHANES) 2009–2010 data. Results Our simulations showed that the simulation-determined cutoff was very sensitive to measurement error in exposure and potential confounder. The analysis of NHANES data demonstrated that if the noise-to-signal ratio (error variance in confounder/variance of confounder) is at or below 0.5, roughly 80 % of the simulated analyses adjusting for fish consumption would correctly result in a null association of mercury and depression, and only an extremely poorly measured confounder is not useful to adjust for in this setting. Conclusions No a prior criterion developed for a specific application is guaranteed to be suitable for confounder identification in general. The customization of model-building strategies and study designs through simulations that consider the likely imperfections in the data, as well as finite-sample behavior, would constitute an important improvement on some of the currently prevailing practices in confounder identification and evaluation.

Loneliness has a significant influence on both physical and mental health. Few studies have investigated the possible associations of loneliness with mortality risk, impact on men and women and whether this impact concerns the situation of being alone (social isolation), experiencing loneliness (feeling lonely) or both. The current study investigated whether social isolation and feelings of loneliness in older men and women were associated with increased mortality risk, controlling for depression and other potentially confounding factors. In our prospective cohort study of 4004 older persons aged 65-84 years with a 10-year follow-up of mortality data a Cox proportional hazard regression analysis was used to test whether social isolation factors and feelings of loneliness predicted an increased risk of mortality, controlling for psychiatric disorders and medical conditions, cognitive functioning, functional status and sociodemographic factors. At 10 years follow-up, significantly more men than women with feelings of loneliness at baseline had died. After adjustment for explanatory variables including social isolation, the mortality hazard ratio for feelings of loneliness was 1.30 [95% confidence interval (CI) 1.04-1.63] in men and 1.04 (95% CI 0.90-1.24) in women. No higher risk of mortality was found for social isolation. Feelings of loneliness rather than social isolation factors were found to be a major risk factor for increasing mortality in older men. Developing a better understanding of the nature of this association may help us to improve quality of life and longevity, especially in older men.

Background Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. Methods A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. Results The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. Conclusion When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used.

Poor quality of reporting of confounding has been observed in observational studies prior the STrenghtening the Reporting of Observational studies in Epidemiology (STROBE) statement, a reporting guideline for observational studies. We assessed whether the reporting of confounding improved after the STROBE statement. We searched MEDLINE for all articles about observational cohort and case–control studies on interventions with a hypothesized beneficial effect in five general medical and five epidemiologic journals published between January 2010 and December 2012. We abstracted data for the baseline period before the publication of the STROBE statement (January 2004–April 2007) from a prior study. Six relevant items related to confounding were scored for each article. A comparison of the median number of items reported in both periods was made. In total, 174 articles published before and 220 articles published after the STROBE statement were included. The median number reported items was similar before and after the publication of the STROBE statement [median, 4; interquartile range [IQR], 3–5 vs. median, 4; IQR, 3.75–5]. However, the distribution of the number of reported items shifted somewhat to the right (P = 0.01). Although the quality of reporting of confounding improved in certain aspects, the overall quality remains suboptimal.