Measures of interaction on an additive scale (relative excess risk due to interaction RERI, attributable proportion AP, synergy index S), were developed for risk factors rather than preventive ...factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 95% CI: -0.30; 0.82, AP = 0.30 95% CI: -0.28; 0.88, S = 0.35 95% CI: 0.02; 7.38), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 95% CI: -1.23; 0.49, AP = -0.29 95% CI: -0.98; 0.40, S = 0.43 95% CI: 0.07; 2.60), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding.
Abstract Objectives To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders. Study Design and ...Setting A PubMed search identified studies using PS methods from December 2011 through May 2012. For each article included in the review, information was extracted on important aspects of the PS such as the type of PS method used, variable selection for PS model, and assessment of balance. Results Among 296 articles that were included in the review, variable selection for PS model was explicitly reported in 102 studies (34.4%). Covariate balance was checked and reported in 177 studies (59.8%). P -values were the most commonly used statistical tools to report balance (125 of 177, 70.6%). The standardized difference and graphical displays were reported in 45 (25.4%) and 11 (6.2%) articles, respectively. Matching on the PS was the most commonly used approach to control for confounding (68.9%), followed by PS adjustment (20.9%), PS stratification (13.9%), and inverse probability of treatment weighting (IPTW, 7.1%). Balance was more often checked in articles using PS matching and IPTW, 70.6% and 71.4%, respectively. Conclusion The execution and reporting of covariate selection and assessment of balance is far from optimal. Recommendations on reporting of PS analysis are provided to allow better appraisal of the validity of PS-based studies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
To compare re-rupture rate, complication rate, and functional outcome after operative versus nonoperative treatment of Achilles tendon ruptures; to compare re-rupture rate after early and late full ...weight bearing; to evaluate re-rupture rate after functional rehabilitation with early range of motion; and to compare effect estimates from randomised controlled trials and observational studies.
Systematic review and meta-analysis.
PubMed/Medline, Embase, CENTRAL, and CINAHL databases were last searched on 25 April 2018 for studies comparing operative versus nonoperative treatment of Achilles tendon ruptures.
Randomised controlled trials and observational studies reporting on comparison of operative versus nonoperative treatment of acute Achilles tendon ruptures.
Data extraction was performed independently in pairs, by four reviewers, with the use of a predefined data extraction file. Outcomes were pooled using random effects models and presented as risk difference, risk ratio, or mean difference, with 95% confidence interval.
29 studies were included-10 randomised controlled trials and 19 observational studies. The 10 trials included 944 (6%) patients, and the 19 observational studies included 14 918 (94%) patients. A significant reduction in re-ruptures was seen after operative treatment (2.3%) compared with nonoperative treatment (3.9%) (risk difference 1.6%; risk ratio 0.43, 95% confidence interval 0.31 to 0.60; P<0.001; I
=22%). Operative treatment resulted in a significantly higher complication rate than nonoperative treatment (4.9%
1.6%; risk difference 3.3%; risk ratio 2.76, 1.84 to 4.13; P<0.001; I
=45%). The main difference in complication rate was attributable to the incidence of infection (2.8%) in the operative group. A similar reduction in re-rupture rate in favour of operative treatment was seen after both early and late full weight bearing. No significant difference in re-rupture rate was seen between operative and nonoperative treatment in studies that used accelerated functional rehabilitation with early range of motion (risk ratio 0.60, 0.26 to 1.37; P=0.23; I
=0%). No difference in effect estimates was seen between randomised controlled trials and observational studies.
This meta-analysis shows that operative treatment of Achilles tendon ruptures reduces the risk of re-rupture compared with nonoperative treatment. However, re-rupture rates are low and differences between treatment groups are small (risk difference 1.6%). Operative treatment results in a higher risk of other complications (risk difference 3.3%). The final decision on the management of acute Achilles tendon ruptures should be based on patient specific factors and shared decision making. This review emphasises the potential benefits of adding high quality observational studies in meta-analyses for the evaluation of objective outcome measures after surgical treatment.
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BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
This is the introductory paper in a series of eight papers. In this series, we integrate the theoretical design options with the practice of conducting pragmatic trials. For most new market-approved ...treatments, the clinical evidence is insufficient to fully guide physicians and policy makers in choosing the optimal treatment for their patients. Pragmatic trials can fill this gap, by providing evidence on the relative effectiveness of a treatment strategy in routine clinical practice, already in an early phase of development, while maintaining the strength of randomized controlled trials. Selecting the setting, study population, mode of intervention, comparator, and outcome are crucial in designing pragmatic trials. In combination with monitoring and data collection that does not change routine care, this will enable appropriate generalization to the target patient group in clinical practice. To benefit from the full potential of pragmatic trials, there is a need for guidance and tools in designing these studies while ensuring operational feasibility. This paper introduces the concept of pragmatic trial design. The complex interplay between pragmatic design options, feasibility, stakeholder acceptability, validity, precision, and generalizability will be clarified. In this way, balanced design choices can be made in pragmatic trials with an optimal chance of success in practice.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Summary Background Estimating attributable mortality of ventilator-associated pneumonia has been hampered by confounding factors, small sample sizes, and the difficulty of doing relevant subgroup ...analyses. We estimated the attributable mortality using the individual original patient data of published randomised trials of ventilator-associated pneumonia prevention. Methods We identified relevant studies through systematic review. We analysed individual patient data in a one-stage meta-analytical approach (in which we defined attributable mortality as the ratio between the relative risk reductions RRR of mortality and ventilator-associated pneumonia) and in competing risk analyses. Predefined subgroups included surgical, trauma, and medical patients, and patients with different categories of severity of illness scores. Findings Individual patient data were available for 6284 patients from 24 trials. The overall attributable mortality was 13%, with higher mortality rates in surgical patients and patients with mid-range severity scores at admission (ie, acute physiology and chronic health evaluation score APACHE 20–29 and simplified acute physiology score SAPS 2 35–58). Attributable mortality was close to zero in trauma, medical patients, and patients with low or high severity of illness scores. Competing risk analyses could be done for 5162 patients from 19 studies, and the overall daily hazard for intensive care unit (ICU) mortality after ventilator-associated pneumonia was 1·13 (95% CI 0·98–1·31). The overall daily risk of discharge after ventilator-associated pneumonia was 0·74 (0·68–0·80), leading to an overall cumulative risk for dying in the ICU of 2·20 (1·91–2·54). Highest cumulative risks for dying from ventilator-associated pneumonia were noted for surgical patients (2·97, 95% CI 2·24–3·94) and patients with mid-range severity scores at admission (ie, cumulative risks of 2·49 1·81–3·44 for patients with APACHE scores of 20–29 and 2·72 1·95–3·78 for those with SAPS 2 scores of 35–58). Interpretation The overall attributable mortality of ventilator-associated pneumonia is 13%, with higher rates for surgical patients and patients with a mid-range severity score at admission. Attributable mortality is mainly caused by prolonged exposure to the risk of dying due to increased length of ICU stay. Funding None.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Purpose
The Trials within Cohorts (TwiCs) design aims to overcome problems faced in conventional RCTs. We evaluated the TwiCs design when estimating the effect of exercise on quality of life (QoL) ...and fatigue in inactive breast cancer survivors.
Methods
UMBRELLA Fit was conducted within the prospective UMBRELLA breast cancer cohort. Patients provided consent for future randomization at cohort entry. We randomized inactive patients 12–18 months after cohort enrollment. The intervention group (
n
= 130) was offered a 12-week supervised exercise intervention. The control group (
n
= 130) was not informed and received usual care. Six-month exercise effects on QoL and fatigue as measured in the cohort were analyzed with intention-to-treat (ITT), instrumental variable (IV), and propensity scores (PS) analyses.
Results
Fifty-two percent (
n
= 68) of inactive patients accepted the intervention. Physical activity increased in patients in the intervention group, but not in the control group. We found no benefit of exercise for dimensions of QoL (ITT difference global QoL: 0.8, 95% CI = − 2.2; 3.8) and fatigue, except for a small beneficial effect on physical fatigue (ITT difference: − 1.1, 95% CI = − 1.8; − 0.3; IV: − 1.9, 95% CI = − 3.3; − 0.5, PS: − 1.2, 95% CI = − 2.3; − 0.2).
Conclusion
TwiCs gave insight into exercise intervention acceptance: about half of inactive breast cancer survivors accepted the offer and increased physical activity levels. The offer resulted in no improvement on QoL, and a small beneficial effect on physical fatigue.
Trial registration
Netherlands Trial Register (NTR5482/NL.52062.041.15), date of registration: December 07, 2015.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract
Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or ...procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study’s inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.
Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors ...compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses.
Immortal time bias should always be considered in an observational study if exposure status is determined based on a measurement or event that occurs after baseline. This bias can lead to an ...overestimation of an effect, but also to an underestimation, which is explained. Several approaches are illustrated that can be used to avoid immortal time bias in the analysis phase of the study; a time-dependent analysis to avoid immortal time bias optimizes the use of available information.
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