Biomedical subjects

Jeffrey N Jonkman

Publications and source records attributed to Jeffrey N Jonkman.

5 recordsLinked to original sources

PubMed2007

A comparison of heterogeneity variance estimators in combining results of studies.

For random effects meta-analysis, seven different estimators of the heterogeneity variance are compared and assessed using a simulation study. The seven estimators are the variance component type estimator (VC), the method of moments estimator (MM), the maximum likelihood estimator (ML), the restricted maximum likelihood estimator (REML), the empirical Bayes estimator (EB), the model error variance type estimator (MV), and a variation of the MV estimator (MVvc). The performance of the estimators is compared in terms of both bias and mean squared error, using Monte Carlo simulation. The results show that the REML and especially the ML and MM estimators are not accurate, having large biases unless the true heterogeneity variance is small. The VC estimator tends to overestimate the heterogeneity variance in general, but is quite accurate when the number of studies is large. The MV estimator is not a good estimator when the heterogeneity variance is small to moderate, but it is reasonably accurate when the heterogeneity variance is large. The MVvc estimator is an improved estimator compared to the MV estimator, especially for small to moderate values of the heterogeneity variance. The two estimators MVvc and EB are found to be the most accurate in general, particularly when the heterogeneity variance is moderate to large.

Bias↗

PubMed2007

Disordered gambling among college students: a meta-analytic synthesis.

The purpose of this study was to use a meta-analytic procedure to synthesize the rates of disordered gambling for college students that have been reported in the research literature. In order to identify all possible studies that met stringent inclusion criteria, Medline, PsychINFO, and SocioIndex databases were searched with the terms "gambling," and "college student". This process resulted in 15 studies concerning gambling among college students that were published through July 2005. To synthesize the 15 studies, a random effects model for meta-analysis was applied. The estimated proportion of disordered gamblers among college students was 7.89%. This estimate is noteworthy because it is higher than that reported for adolescents, college students or adults in a previous study using meta-analytic procedures with studies conducted prior to 1997.

Adult↗

PubMed2005

Practice variation and length of stay in alcohol and drug detoxification centers.

Admissions to 20 publicly funded alcohol and drug detoxification centers in Massachusetts were examined to identify program and patient variables that influenced length of stay. The last admission during fiscal year 1996 was abstracted for patients 18 years of age and older seeking alcohol, cocaine, or heroin detoxification (n=21,311; 29% women). A hierarchical generalized linear model examined the effects of patient and program characteristics on variation in length of stay and tested case-mix adjustments. Program size had the most influence on mean adjusted length of stay; stays were more than 40% longer in detoxification centers with 35 or more beds (7.69 days) than in centers with less than 35 beds (5.42 days). The study highlights the contribution of program size to treatment processes and suggests the need for more attention to program attributes in studies of patient outcomes and treatment processes.

Adolescent↗

PubMed2005

A note on variance estimation in random effects meta-regression.

For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

Analysis of Variance↗

PubMed2002

A simple confidence interval for meta-analysis.

In the context of a random effects model for meta-analysis, a number of methods are available to estimate confidence limits for the overall mean effect. A simple and commonly used method is the DerSimonian and Laird approach. This paper discusses an alternative simple approach for constructing the confidence interval, based on the t-distribution. This approach has improved coverage probability compared to the DerSimonian and Laird method. Moreover, it is easy to calculate, and unlike some methods suggested in the statistical literature, no iterative computation is required.

Computer Simulation↗