| 1. |
- Mathiassen, Svend Erik, et al.
(författare)
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Optimizing cost-efficiency in mean exposure assessment – cost functions reconsidered
- 2011
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 11:76
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Tidskriftsartikel (refereegranskat)abstract
- Background. Reliable exposure data is a vital concern in medical epidemiology and intervention studies. The present study addresses the needs of the medical researcher to spend monetary resources devoted to exposure assessment with an optimal cost-efficiency, i.e. obtain the best possible statistical performance at a specified budget. A few previous studies have suggested mathematical optimization procedures based on very simple cost models; this study extends the methodology to cover even non-linear cost scenarios. Methods. Statistical performance, i.e. efficiency, was assessed in terms of the precision of an exposure mean value, as determined in a hierarchical, nested measurement model with three stages. Total costs were assessed using a corresponding three-stage cost model, allowing costs at each stage to vary non-linearly with the number of measurements, according to a power function. Using these models, procedures for identifying the optimally cost-efficient allocation of measurements under a constrained budget were developed, and applied on 225 scenarios combining different sizes of unit costs, cost function exponents, and exposure variance components. Results. Explicit mathematical rules for identifying optimal allocation could be developed when cost functions were linear, while non-linear cost functions implied that parts of or the entire optimization procedure had to be carried out using numerical methods. For many of the 225 scenarios, the optimal strategy consisted in measuring on one occasion from each of as many subjects as allowed by the budget. Significant deviations from this principle occurred if costs for recruiting subjects were large compared to costs for setting up measurement occasions, and, at the same time, the between-subjects to within-subject variance ratio was small. In these cases, non-linearities had a profound influence on the optimal allocation and on the eventual size of the exposure data set. Conclusions. The analysis procedures developed in the present study can be used for informed design of exposure assessment strategies, provided that data are available on exposure variability and the costs of collecting and processing data. The present shortage of empirical evidence on costs and appropriate cost functions however impedes general conclusions on optimal exposure measurement strategies in different epidemiologic scenarios.
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| 2. |
- Mathiassen, Svend Erik, et al.
(författare)
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Bias and imprecision in posture percentile variables estimated from short exposure samples
- 2012
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Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 12:1, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundUpper arm postures are believed to be an important risk determinant for musculoskeletal disorder development in the neck and shoulders. The 10th and 90th percentiles of the angular elevation distribution have been reported in many studies as measures of neutral and extreme postural exposures, and variation has been quantified by the 10th-90th percentile range. Further, the 50th percentile is commonly reported as a measure of "average" exposure. These four variables have been estimated using samples of observed or directly measured postures, typically using sampling durations between 5 and 120 min.MethodsThe present study examined the statistical properties of estimated full-shift values of the 10th, 50th and 90th percentile and the 10th-90th percentile range of right upper arm elevation obtained from samples of seven different durations, ranging from 5 to 240 min. The sampling strategies were realized by simulation, using a parent data set of 73 full-shift, continuous inclinometer recordings among hairdressers. For each shift, sampling duration and exposure variable, the mean, standard deviation and sample dispersion limits (2.5% and 97.5%) of all possible sample estimates obtained at one minute intervals were calculated and compared to the true full-shift exposure value.ResultsEstimates of the 10th percentile proved to be upward biased with limited sampling, and those of the 90th percentile and the percentile range, downward biased. The 50th percentile was also slightly upwards biased. For all variables, bias was more severe with shorter sampling durations, and it correlated significantly with the true full-shift value for the 10th and 90th percentiles and the percentile range. As expected, shorter samples led to decreased precision of the estimate; sample standard deviations correlated strongly with true full-shift exposure values.ConclusionsThe documented risk of pronounced bias and low precision of percentile estimates obtained from short posture samples presents a concern in ergonomics research and practice, and suggests that alternative, unbiased exposure variables should be considered if data collection resources are restricted.
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| 3. |
- Samani, Afshin, et al.
(författare)
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Cluster-based exposure variation analysis
- 2013
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 13, s. 54-54
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Tidskriftsartikel (refereegranskat)abstract
- Background: Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation.Methods: For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity. Each simulation trace included two realizations of 100 concatenated cycles with either low (r=0.1), medium (r=0.5) or high (r=0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined.Results: C-EVA classified exposures more correctly than uni 1 variate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p<0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration.Conclusion: While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA.
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| 4. |
- Benamer, Hani T S, et al.
(författare)
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Arab nations lagging behind other Middle Eastern countries in biomedical research: a comparative study.
- 2009
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 9:Apr 17
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Analysis of biomedical research and publications in a country or group of countries is used to monitor research progress and trends. This study aims to assess the performance of biomedical research in the Arab world during 2001-2005 and to compare it with other Middle Eastern non-Arab countries. METHODS: PubMed and Science Citation Index Expanded (SCI-expanded) were searched systematically for the original biomedical research publications and their citation frequencies of 16 Arab nations and three non-Arab Middle Eastern countries (Iran, Israel and Turkey), all of which are classified as middle or high income countries. RESULTS: The 16 Arab countries together have 5775 and 14,374 original research articles listed by PubMed and SCI-expanded, respectively, significantly less (p < 0.001) than the other three Middle Eastern countries (25,643 and 49,110). The Arab countries also scored less when the data were normalized to population, gross domestic product (GDP), and GDP/capita. The publications from the Arab countries also have a significantly lower (p < 0.001) citation frequency. CONCLUSION: The Arab world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Studies are needed to clarify the causes and to propose strategies to improve the biomedical research status in Arab countries.
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| 5. |
- Gummesson, Christina, et al.
(författare)
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Performance of health-status scales when used selectively or within multi-scale questionnaire
- 2003
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Little work has been done to investigate the suggestion that the use of selected scales from a multi-scale health-status questionnaire would compromise reliability and validity. The aim of this study was to compare the performance of three scales selected from the SF-36 generic health questionnaire when administered in isolation or within the entire SF-36 to patients with musculoskeletal disorders. METHODS: Two groups of patients referred to an orthopedic department completed a mailed questionnaire within 4 weeks prior to and a second questionnaire during their visit. The first group completed three SF-36 scales related to physical health (physical functioning, bodily pain, and general health perceptions) on one occasion and all eight SF-36 scales on the other occasion. The second group completed the entire SF-36 on two occasions. Results for patients who reported unchanged health status and had complete scores were analyzed; 80 patients in the first and 62 patients in the second group. RESULTS: The Cronbach alpha reliability and intraclass correlation coefficients exceeded 0.7 for all three scales for both groups. For the first group the mean difference between the scores was 0.4 point for physical functioning, 2.5 points for bodily pain, and 0.5 point for general health perceptions, which did not differ significantly from the corresponding differences for the second group (0.1, 1.9 and 1 point, respectively). CONCLUSION: The use of selected scales from a multi-scale health-status questionnaire seems to yield similar results compared to their use within the entire questionnaire.
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| 6. |
- May, Anne M., et al.
(författare)
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Determinants of non- response to a second assessment of lifestyle factors and body weight in the EPIC-PANACEA study
- 2012
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Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Background: This paper discusses whether baseline demographic, socio-economic, health variables, length of follow-up and method of contacting the participants predict non-response to the invitation for a second assessment of lifestyle factors and body weight in the European multi-center EPIC-PANACEA study. Methods: Over 500.000 participants from several centers in ten European countries recruited between 1992 and 2000 were contacted 2-11 years later to update data on lifestyle and body weight. Length of follow-up as well as the method of approaching differed between the collaborating study centers. Non-responders were compared with responders using multivariate logistic regression analyses. Results: Overall response for the second assessment was high (81.6%). Compared to postal surveys, centers where the participants completed the questionnaire by phone attained a higher response. Response was also high in centers with a short follow-up period. Non-response was higher in participants who were male (odds ratio 1.09 (confidence interval 1.07; 1.11), aged under 40 years (1.96 (1.90; 2.02), living alone (1.40 (1.37; 1.43), less educated (1.35 (1.12; 1.19), of poorer health (1.33 (1.27; 1.39), reporting an unhealthy lifestyle and who had either a low (<18.5 kg/m2, 1.16 (1.09; 1.23)) or a high BMI (>25, 1.08 (1.06; 1.10); especially >= 30 kg/m2, 1.26 (1.23; 1.29)). Conclusions: Cohort studies may enhance cohort maintenance by paying particular attention to the subgroups that are most unlikely to respond and by an active recruitment strategy using telephone interviews.
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| 7. |
- Ranstam, Jonas, et al.
(författare)
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Alternative analyses for handling incomplete follow-up in the intention-to-treat analysis: the randomized controlled trial of balloon kyphoplasty versus non-surgical care for vertebral compression fracture (FREE)
- 2012
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 12:35
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Tidskriftsartikel (refereegranskat)abstract
- Background: Clinical trial participants may be temporarily absent or withdraw from trials, leading to missing data. In intention-to-treat (ITT) analyses, several approaches are used for handling the missing information - complete case (CC) analysis, mixed-effects model (MM) analysis, last observation carried forward (LOCF) and multiple imputation (MI). This report discusses the consequences of applying the CC, LOCF and MI for the ITT analysis of published data (analysed using the MM method) from the Fracture Reduction Evaluation (FREE) trial. Methods: The FREE trial was a randomised, non-blinded study comparing balloon kyphoplasty with non-surgical care for the treatment of patients with acute painful vertebral fractures. Patients were randomised to treatment (1: 1 ratio), and stratified for gender, fracture aetiology, use of bisphosphonates and use of systemic steroids at the time of enrolment. Six outcome measures - Short-form 36 physical component summary (SF-36 PCS) scale, EuroQol 5-Dimension Questionnaire (EQ-5D), Roland-Morris Disability (RMD) score, back pain, number of days with restricted activity in last 2 weeks, and number of days in bed in last 2 weeks - were analysed using four methods for dealing with missing data: CC, LOCF, MM and MI analyses. Results: There were no missing data in baseline covariates values, and only a few missing baseline values in outcome variables. The overall missing-response level increased during follow-up (1 month: 14.5%; 24 months: 28%), corresponding to a mean of 19% missing data during the entire period. Overall patterns of missing response across time were similar for each treatment group. Almost half of all randomised patients were not available for a CC analysis, a maximum of 4% were not included in the LOCF analysis, and all randomised patients were included in the MM and MI analyses. Improved estimates of treatment effect were observed with LOCF, MM and MI compared with CC; only MM provided improved estimates across all six outcomes considered. Conclusions: The FREE trial results are robust as the alternative methods used for substituting missing data produced similar results. The MM method showed the highest statistical precision suggesting it is the most appropriate method to use for analysing the FREE trial data.
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| 8. |
- Van Calster, Ben, et al.
(författare)
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Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models
- 2010
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Background: Hitherto, risk prediction models for preoperative ultrasound-based diagnosis of ovarian tumors were dichotomous (benign versus malignant). We develop and validate polytomous models (models that predict more than two events) to diagnose ovarian tumors as benign, borderline, primary invasive or metastatic invasive. The main focus is on how different types of models perform and compare. Methods: A multi-center dataset containing 1066 women was used for model development and internal validation, whilst another multi-center dataset of 1938 women was used for temporal and external validation. Models were based on standard logistic regression and on penalized kernel-based algorithms (least squares support vector machines and kernel logistic regression). We used true polytomous models as well as combinations of dichotomous models based on the 'pairwise coupling' technique to produce polytomous risk estimates. Careful variable selection was performed, based largely on cross-validated c-index estimates. Model performance was assessed with the dichotomous c-index (i.e. the area under the ROC curve) and a polytomous extension, and with calibration graphs. Results: For all models, between 9 and 11 predictors were selected. Internal validation was successful with polytomous c-indexes between 0.64 and 0.69. For the best model dichotomous c-indexes were between 0.73 (primary invasive vs metastatic) and 0.96 (borderline vs metastatic). On temporal and external validation, overall discrimination performance was good with polytomous c-indexes between 0.57 and 0.64. However, discrimination between primary and metastatic invasive tumors decreased to near random levels. Standard logistic regression performed well in comparison with advanced algorithms, and combining dichotomous models performed well in comparison with true polytomous models. The best model was a combination of dichotomous logistic regression models. This model is available online. Conclusions: We have developed models that successfully discriminate between benign, borderline, and invasive ovarian tumors. Methodologically, the combination of dichotomous models was an interesting approach to tackle the polytomous problem. Standard logistic regression models were not outperformed by regularized kernel-based alternatives, a finding to which the careful variable selection procedure will have contributed. The random discrimination between primary and metastatic invasive tumors on temporal/external validation demonstrated once more the necessity of validation studies.
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| 9. |
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| 10. |
- Broberg, Per
(författare)
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Sample size re-assessment leading to a raised sample size does not inflate type I error rate under mild conditions.
- 2013
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 13:Jul,19
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Tidskriftsartikel (refereegranskat)abstract
- One major concern with adaptive designs, such as the sample size adjustable designs, has been the fear of inflating the type I error rate. In (Stat Med 23:1023-1038, 2004) it is however proven that when observations follow a normal distribution and the interim result show promise, meaning that the conditional power exceeds 50%, type I error rate is protected. This bound and the distributional assumptions may seem to impose undesirable restrictions on the use of these designs. In (Stat Med 30:3267-3284, 2011) the possibility of going below 50% is explored and a region that permits an increased sample size without inflation is defined in terms of the conditional power at the interim.
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| 11. |
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| 12. |
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| 13. |
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| 14. |
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| 15. |
- Axén, Iben, et al.
(författare)
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Analyzing repeated data collected by mobile phones and frequent text messages : An example of Low back pain measured weekly for 18 weeks
- 2012
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Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Background: Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of respondents by sending and receiving frequently repeated short questions and answers as text messages.The analysis of repeated data involves some challenges. Vital issues to consider are the within-subject correlation, the between measurement occasion correlation and the presence of missing values.The overall aim of this commentary is to describe different methods of analyzing repeated data. It is meant to give an overview for the clinical researcher in order for complex outcome measures to be interpreted in a clinically meaningful way.Methods: A model data set was formed using data from two clinical studies, where patients with low back pain were followed with weekly text messages for 18 weeks. Different research questions and analytic approaches were illustrated and discussed, as well as the handling of missing data. In the applications the weekly outcome “number of days with pain” was analyzed in relation to the patients’ “previous duration of pain” (categorized as more or less than 30 days in the previous year).Research questions with appropriate analytical methods1: How many days with pain do patients experience? This question was answered with data summaries.2: What is the proportion of participants “recovered” at a specific time point? This question was answered using logistic regression analysis.3: What is the time to recovery? This question was answered using survival analysis, illustrated in Kaplan-Meier curves, Proportional Hazard regression analyses and spline regression analyses.4: How is the repeatedly measured data associated with baseline (predictor) variables? This question was answered using generalized Estimating Equations, Poisson regression and Mixed linear models analyses.5: Are there subgroups of patients with similar courses of pain within the studied population?A visual approach and hierarchical cluster analyses revealed different subgroups using subsets of the model data.Conclusions: We have illustrated several ways of analysing repeated measures with both traditional analytic approaches using standard statistical packages, as well as recently developed statistical methods that will utilize all the vital features inherent in the data.
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| 16. |
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| 17. |
- De Costa, Ayesha, et al.
(författare)
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Design of a randomized trial to evaluate the influence of mobile phone reminders on adherence to first line antiretroviral treatment in South India : the HIVIND study protocol
- 2010
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 10:25
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Tidskriftsartikel (refereegranskat)abstract
- Poor adherence to antiretroviral treatment has been a public health challenge associated with the treatment of HIV. Although different adherence-supporting interventions have been reported, their long term feasibility in low income settings remains uncertain. Thus, there is a need to explore sustainable contextual adherence aids in such settings, and to test these using rigorous scientific designs. The current ubiquity of mobile phones in many resource-constrained settings, make it a contextually appropriate and relatively low cost means of supporting adherence. In India, mobile phones have wide usage and acceptability and are potentially feasible tools for enhancing adherence to medications. This paper presents the study protocol for a trial, to evaluate the influence of mobile phone reminders on adherence to first-line antiretroviral treatment in South India. Methods/Design: 600 treatment naive patients eligible for first-line treatment as per the national antiretroviral treatment guidelines will be recruited into the trial at two clinics in South India. Patients will be randomized into control and intervention arms. The control arm will receive the standard of care; the intervention arm will receive the standard of care plus mobile phone reminders. Each reminder will take the form of an automated call and a picture message. Reminders will be delivered once a week, at a time chosen by the patient. Patients will be followed up for 24 months or till the primary outcome i.e. virological failure, is reached, whichever is earlier. Self-reported adherence is a secondary outcome. Analysis is by intention-to-treat. A cost-effectiveness study of the intervention will also be carried out. Stepping up telecommunications technology in resource-limited healthcare settings is a priority of the World Health Organization. The trial will evaluate if the use of mobile phone reminders can influence adherence to first-line antiretrovirals in an Indian context.
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| 18. |
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| 19. |
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| 20. |
- Flynn, Terry N, et al.
(författare)
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Estimating preferences for a dermatology consultation using Best-Worst Scaling : comparison of various methods of analysis.
- 2008
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Ingår i: BMC Medical Research Methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 8, s. 76-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Additional insights into patient preferences can be gained by supplementing discrete choice experiments with best-worst choice tasks. However, there are no empirical studies illustrating the relative advantages of the various methods of analysis within a random utility framework.METHODS: Multinomial and weighted least squares regression models were estimated for a discrete choice experiment. The discrete choice experiment incorporated a best-worst study and was conducted in a UK NHS dermatology context. Waiting time, expertise of doctor, convenience of attending and perceived thoroughness of care were varied across 16 hypothetical appointments. Sample level preferences were estimated for all models and differences between patient subgroups were investigated using covariate-adjusted multinomial logistic regression.RESULTS: A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it). Adjusting for covariates showed that patients who felt particularly affected by their skin condition during the previous week displayed extreme preference for short/no waiting time and were less concerned about other aspects of the appointment. Higher levels of educational attainment were associated with larger differences in utility between the levels of all attributes, although the attributes per se had the same impact upon choices as those with lower levels of attainment. The study also demonstrated the high levels of agreement between summary analyses using weighted least squares and estimates from multinomial models.CONCLUSION: Robust policy-relevant information on preferences can be obtained from discrete choice experiments incorporating best-worst questions with relatively small sample sizes. The separation of the effects due to attribute impact from the position of levels on the latent utility scale is not possible using traditional discrete choice experiments. This separation is important because health policies to change the levels of attributes in health care may be very different from those aiming to change the attribute impact per se. The good approximation of summary analyses to the multinomial model is a useful finding, because weighted least squares choice totals give better insights into the choice model and promote greater familiarity with the preference data.
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| 21. |
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| 22. |
- Fottrell, Edward, et al.
(författare)
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Demonstrating the robustness of population surveillance data : implications of error rates on demographic and mortality estimates
- 2008
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Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 8, s. Article nr 13-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: As in any measurement process, a certain amount of error may be expected in routine population surveillance operations such as those in demographic surveillance sites (DSSs). Vital events are likely to be missed and errors made no matter what method of data capture is used or what quality control procedures are in place. The extent to which random errors in large, longitudinal datasets affect overall health and demographic profiles has important implications for the role of DSSs as platforms for public health research and clinical trials. Such knowledge is also of particular importance if the outputs of DSSs are to be extrapolated and aggregated with realistic margins of error and validity.METHODS: This study uses the first 10-year dataset from the Butajira Rural Health Project (BRHP) DSS, Ethiopia, covering approximately 336,000 person-years of data. Simple programmes were written to introduce random errors and omissions into new versions of the definitive 10-year Butajira dataset. Key parameters of sex, age, death, literacy and roof material (an indicator of poverty) were selected for the introduction of errors based on their obvious importance in demographic and health surveillance and their established significant associations with mortality. Defining the original 10-year dataset as the 'gold standard' for the purposes of this investigation, population, age and sex compositions and Poisson regression models of mortality rate ratios were compared between each of the intentionally erroneous datasets and the original 'gold standard' 10-year data.RESULTS: The composition of the Butajira population was well represented despite introducing random errors, and differences between population pyramids based on the derived datasets were subtle. Regression analyses of well-established mortality risk factors were largely unaffected even by relatively high levels of random errors in the data.CONCLUSION: The low sensitivity of parameter estimates and regression analyses to significant amounts of randomly introduced errors indicates a high level of robustness of the dataset. This apparent inertia of population parameter estimates to simulated errors is largely due to the size of the dataset. Tolerable margins of random error in DSS data may exceed 20%. While this is not an argument in favour of poor quality data, reducing the time and valuable resources spent on detecting and correcting random errors in routine DSS operations may be justifiable as the returns from such procedures diminish with increasing overall accuracy. The money and effort currently spent on endlessly correcting DSS datasets would perhaps be better spent on increasing the surveillance population size and geographic spread of DSSs and analysing and disseminating research findings.
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| 23. |
- Genell, Anna, et al.
(författare)
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Model selection in Medical Research: A simulation study comparing Bayesian Model Averaging and Stepwise Regression.
- 2010
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Ingår i: BMC medical research methodology. - : Springer Science and Business Media LLC. - 1471-2288. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited. Bayesian model averaging may be useful for model selection but only limited attempts to compare it to stepwise regression have been published. We therefore performed a simulation study to compare stepwise regression with Bayesian model averaging. Methods We simulated data corresponding to five different data generating processes and thirty different values of the effect size (the parameter estimate divided by its standard error). Each data generating process contained twenty explanatory variables in total and had between zero and two true predictors. Three data generating processes were built of uncorrelated predictor variables while two had a mixture of correlated and uncorrelated variables. We fitted linear regression models to the simulated data. We used Bayesian model averaging and stepwise regression respectively as model selection procedures and compared the estimated selection probabilities. Results The estimated probability of not selecting a redundant variable was between 0.99 and 1 for Bayesian model averaging while approximately 0.95 for stepwise regression when the redundant variable was not correlated with a true predictor. These probabilities did not depend on the effect size of the true predictor. In the case of correlation between a redundant variable and a true predictor, the probability of not selecting a redundant variable was 0.95 to 1 for Bayesian model averaging while for stepwise regression it was between 0.7 and 0.9, depending on the effect size of the true predictor. The probability of selecting a true predictor increased as the effect size of the true predictor increased and leveled out at between 0.9 and 1 for stepwise regression, while it leveled out at 1 for Bayesian model averaging. Conclusions Our simulation study showed that under the given conditions, Bayesian model averaging had a higher probability of not selecting a redundant variable than stepwise regression and had a similar probability of selecting a true predictor. Medical researchers building regression models with limited subject matter knowledge could thus benefit from using Bayesian model averaging.
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| 24. |
- Harder, Thomas, et al.
(författare)
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Evidence-based decision-making in infectious diseases epidemiology, prevention and control: matching research questions to study designs and quality appraisal tools
- 2014
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Ingår i: BMC Medical Research Methodology. - : BioMed Central. - 1471-2288. ; 14:69
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND:The Project on a Framework for Rating Evidence in Public Health (PRECEPT) was initiated and is being funded by the European Centre for Disease Prevention and Control (ECDC) to define a methodology for evaluating and grading evidence and strength of recommendations in the field of public health, with emphasis on infectious disease epidemiology, prevention and control. One of the first steps was to review existing quality appraisal tools (QATs) for individual research studies of various designs relevant to this area, using a question-based approach.METHODS:Through team discussions and expert consultations, we identified 20 relevant types of public health questions, which were grouped into six domains, i.e. characteristics of the pathogen, burden of disease, diagnosis, risk factors, intervention, and implementation of intervention. Previously published systematic reviews were used and supplemented by expert consultation to identify suitable QATs. Finally, a matrix was constructed for matching questions to study designs suitable to address them and respective QATs. Key features of each of the included QATs were then analyzed, in particular in respect to its intended use, types of questions and answers, presence/absence of a quality score, and if a validation was performed.RESULTS:In total we identified 21 QATs and 26 study designs, and matched them. Four QATs were suitable for experimental quantitative study designs, eleven for observational quantitative studies, two for qualitative studies, three for economic studies, one for diagnostic test accuracy studies, and one for animal studies. Included QATs consisted of six to 28 items. Six of the QATs had a summary quality score. Fourteen QATs had undergone at least one validation procedure.CONCLUSIONS:The results of this methodological study can be used as an inventory of potentially relevant questions, appropriate study designs and QATs for researchers and authorities engaged with evidence-based decision-making in infectious disease epidemiology, prevention and control.
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