Bmc Med Res Methodol
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Bmc Med Res Methodol · Nov 2016
Observational StudyPredictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks.
Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain. ⋯ Elixhauser Comorbidity Index is a superior comorbidity risk-adjustment model for major LEA survival prediction in patients with T2DM than Charlson Comorbidity Index model using ANN models. Female sex, congestive heart failure, and renal failure are strong predictors of mortality in these patients.
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Bmc Med Res Methodol · Nov 2016
Does size really matter? A sensitivity analysis of number of seeds in a respondent-driven sampling study of gay, bisexual and other men who have sex with men in Vancouver, Canada.
Respondent-driven sampling (RDS) is an increasingly used peer chain-recruitment method to sample "hard-to-reach" populations for whom there are no reliable sampling frames. Implementation success of RDS varies; one potential negative factor being the number of seeds used. ⋯ Within a sample of GBMSM in Vancouver, Canada, this RDS study suggests that when equilibrium and homophily are met, although potentially costly and time consuming, analysis is not negatively affected by large numbers of unproductive or lowly productive seeds.
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Bmc Med Res Methodol · Oct 2016
Abstracts reporting of HIV/AIDS randomized controlled trials in general medicine and infectious diseases journals: completeness to date and improvement in the quality since CONSORT extension for abstracts.
Sufficiently detailed abstracts of randomized controlled trials (RCTs) are important, because readers often base their assessment of a trial solely on information in the abstract. We aimed at comparing reporting quality of RCTs in HIV/AIDS medicine before and after the publication of the 2008 CONSORT extension for abstracts and to investigate factors associated with better reporting quality. ⋯ After the publication of the CONSORT extension for abstracts, the reporting quality of HIV/AIDS RCT abstracts in general medicine and infectious diseases journals has suboptimally improved. Thus, stricter adherence to the CONSORT for abstract are needed to improve the reporting quality of HIV/AIDS RCT abstracts.
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Bmc Med Res Methodol · Oct 2016
Comparative StudyImpact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.
The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence. ⋯ In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.
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Bmc Med Res Methodol · Aug 2016
Performance of models for estimating absolute risk difference in multicenter trials with binary outcome.
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. ⋯ 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.