Bmc Med Res Methodol
-
Bmc Med Res Methodol · Mar 2012
Hospital-level associations with 30-day patient mortality after cardiac surgery: a tutorial on the application and interpretation of marginal and multilevel logistic regression.
Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery. However, it is not widely understood how the interpretation of hospital-level effects differs between these methods. ⋯ Marginal and multilevel models take different approaches to account for correlation between patients within hospitals and they lead to different interpretations for hospital-level odds ratios.
-
Bmc Med Res Methodol · Feb 2012
A simple method for estimating relative risk using logistic regression.
Odds ratios (OR) significantly overestimate associations between risk factors and common outcomes. The estimation of relative risks (RR) or prevalence ratios (PR) has represented a statistical challenge in multivariate analysis and, furthermore, some researchers do not have access to the available methods. ⋯ This simple tool could be useful for calculating the effect of risk factors and the impact of health interventions in developing countries when other statistical strategies are not available.
-
Bmc Med Res Methodol · Jan 2012
Randomized Controlled Trial Comparative StudyTelephone follow-up to a mail survey: when to offer an interview compared to a reminder call.
Using a different mode of contact on the final follow-up to survey non-respondents is an identified strategy to increase response rates. This study was designed to determine if a reminder phone call or a phone interview as a final mode of contact to a mailed survey works better to increase response rates and which strategy is more cost effective. ⋯ The additional cost of completing an interview is worth it when an additional signed form is not required of the respondent. However, when such a signed form is required, offering an interview instead of a reminder phone call as a follow up to non-respondents does not increase response rates enough to outweigh the additional costs.
-
Bmc Med Res Methodol · Jan 2012
Comparative Studyt-tests, non-parametric tests, and large studies--a paradox of statistical practice?
During the last 30 years, the median sample size of research studies published in high-impact medical journals has increased manyfold, while the use of non-parametric tests has increased at the expense of t-tests. This paper explores this paradoxical practice and illustrates its consequences. ⋯ Non-parametric tests are most useful for small studies. Using non-parametric tests in large studies may provide answers to the wrong question, thus confusing readers. For studies with a large sample size, t-tests and their corresponding confidence intervals can and should be used even for heavily skewed data.
-
Bmc Med Res Methodol · Jan 2012
ReviewA review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures.
Retaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data. ⋯ This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies.