Biomedical subjects

L Endahl

Publications and source records attributed to L Endahl.

4 recordsLinked to original sources

PubMed2001

Physical exposure assessment in monotonous repetitive work--the PRIM study.

OBJECTIVES: A program called the Project on Research and Intervention in Monotonous Work (PRIM) was initiated in 1994 as a prospective cohort study of work-related musculoskeletal disorders. The group-based exposure assessment strategy, focusing on task-related exposure and used to obtain baseline measures of physical exposures, is reported in this paper. METHODS: Monotonous, repetitive worktasks were evaluated at 19 factories. Tasks with an estimated similarity in physical exposure were aggregated before 103 exposure groups were formed. Subjects from the exposure groups were randomly sampled for measurements, and task-related exposure levels were quantified by 43 single exposure items using a real-time video-based observation method that allowed computerized estimates of repetitiveness, body postures, force, and velocity. In combination with questionnaire-based data on task distribution, the duration of exposure was calculated at the individual level. RESULTS: The video-based observational method and the large number of exposure variables enabled the establishment of detailed quantitative exposure profiles in 103 task-based exposure groups. However, methodological problems associated with the use of grouped exposure assessment were revealed. Despite efforts to optimize group homogeneity, the within-group variance was larger than the between-group variance for several shoulder postural variables. CONCLUSIONS: A task-based exposure-assessment strategy can be successful in solving some of the main problems associated with the assessment of physical workplace exposures. The large within-group variance in exposure to nonneutral shoulder postures may eventually require individual assessment or the inclusion of groups with maximal contrast in exposure or both.

Adult↗

PubMed2000

Interpreting parameters in the logistic regression model with random effects.

Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail the interpretation of both fixed effects and random effects parameters. As heterogeneity measures, the random effects parameters included in the model are not easily interpreted. We discuss different alternative measures of heterogeneity and suggest using a median odds ratio measure that is a function of the original random effects parameters. The measure allows a simple interpretation, in terms of well-known odds ratios, that greatly facilitates communication between the data analyst and the subject-matter researcher. Three examples from different subject areas, mainly taken from our own experience, serve to motivate and illustrate different aspects of parameter interpretation in these models.

Animals↗

PubMed1998

Prevalence proportion ratios: estimation and hypothesis testing.

BACKGROUND: Recent communications have argued that often it may not be appropriate to analyse cross-sectional studies of prevalent outcomes with logistic regression models. The purpose of this communication is to compare three methods that have been proposed for application to cross sectional studies: (1) a multiplicative generalized linear model, which we will call the log-binomial model, (2) a method based on logistic regression and robust estimation of standard errors, which we will call the GEE-logistic model, and (3) a Cox regression model. METHODS: Five sets of simulations representing fourteen separate simulation conditions were used to test the performance of the methods. RESULTS: All three models produced point estimates close to the true parameter, i.e. the estimators of the parameter associated with exposure had negligible bias. The Cox regression produced standard errors that were too large, especially when the prevalence of the disease was high, whereas the log-binomial model and the GEE-logistic model had the correct type I error probabilities. It was shown by example that the GEE-logistic model could produce prevalences greater than one, whereas it was proven that this could not happen with the log-binomial model. The log-binomial model should be preferred.

Cross-Sectional Studies↗

PubMed1996

Randomized, placebo controlled trial of withdrawal of slow-acting antirheumatic drugs and of observer bias in rheumatoid arthritis.

Patients with rheumatoid arthritis, in stable treatment with methotrexate, penicillamine, or sulfasalazine, were randomized in a double-blind fashion either to continuation of their usual treatment or to placebo. 112 patients were included; 52 patients who refused participation had no more severe disease than the others. The patients felt worse on placebo than on active drug (p = 0.002). The mean differences in number of tender, painful and swollen joints after one month were 2.4 (p = 0.08), 3.0 (p = 0.12) and 2.2 (p = 0.03), respectively. Treatment failure occurred for 42 patients of whom 33 received placebo (p = 0.000,001). There was no difference in the severity of side effects (p = 0.91). The patients guessed their treatment correctly more often than expected (p = 0.02) because of the perceived effect. None of the two observers guessed better than chance, and there were no differences between the observers' evaluations of the joints. The effect of slow-acting antirheumatic drugs was unequivocal and no observer bias occurred.

Aged↗