Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio.

Abstract: Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2-stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.

Risk of colorectal neoplasm in patients with acromegaly: a meta-analysis.

Abstract: AIM: To examine the risk of colorectal neoplasm in acromegalic patients by meta-analyzing all relevant controlled studies. METHODS: Extensive English language medical literature searches for human studies, up to December 2007, were performed using suitable keywords. Pooled estimates [odds ratio (OR) with 95% confidence intervals (CI)] were obtained using either the fixed or random-effects model as appropriate. Heterogeneity between studies was evaluated with the Cochran Q test whereas the likelihood of publication bias was assessed by constructing funnel plots. Their symmetry was estimated by the adjusted rank correlation test. RESULTS: For hyperplastic polyps the pooled ORs with 95% CI were 3.557 (2.587-4.891) by fixed effects model and 3.703 (2.565-5.347) by random effects model. The Z test values for overall effect were 7.81 and 6.984, respectively (P < 0.0001). For colon adenomas the pooled ORs with 95% CI were 2.486 (1.908-3.238) (fixed effects model) and 2.537 (1.914-3.364) (random effects model). The Z test values were 6.747 and 6.472, respectively (P < 0.0001). For colon cancer the pooled OR with 95% CI was identical for both fixed and random effects model (OR, 4.351; 95% CI, 1.533-12.354; Z = 2.762, P = 0.006). There was no significant heterogeneity and no publication bias in all the above meta-analyses. CONCLUSION: Acromegaly is associated with an increased risk of colorectal neoplasm.

An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown

Abstract: SUMMARY Multivariate meta-analysis models can be used to synthesize multiple, correlated endpoints such as overall and disease-free survival. A hierarchical framework for multivariate random-effects meta-analysis includes both within-study and between-study correlation. The within-study correlations are assumed known, but they are usually unavailable, which limits the multivariate approach in practice. In this paper, we consider synthesis of 2 correlated endpoints and propose an alternative model for bivariate randomeffects meta-analysis (BRMA). This model maintains the individual weighting of each study in the analysis but includes only one overall correlation parameter, ρ, which removes the need to know the within-study correlations. Further, the only data needed to fit the model are those required for a separate univariate random-effects meta-analysis (URMA) of each endpoint, currently the common approach in practice. This makes the alternative model immediately applicable to a wide variety of evidence synthesis situations, including studies of prognosis and surrogate outcomes. We examine the performance of the alternative model through analytic assessment, a realistic simulation study, and application to data sets from the literature. Our results show that, unless ˆ ρ is very close to 1 or –1, the alternative model produces appropriate pooled estimates with little bias that (i) are very similar to those from a fully hierarchical BRMA model where the within-study correlations are known and (ii) have better statistical properties than those from separate URMAs, especially given missing data. The alternative model is also less prone to estimation at parameter space boundaries than the fully hierarchical model and thus may be preferred even when the within-study correlations are known. It also suitably estimates a function of the pooled estimates and their correlation; however, it only provides an approximate indication of the between-study variation. The alternative model greatly facilitates the utilization of correlation in meta-analysis and should allow an increased application of BRMA in practice.

Aggregate data studies of disease risk factors

Abstract: SUMMARY Statistical methods are proposed for estimating relative rate parameters, based on estimated disease rates and covariate data from random samples of individuals from each of several cohorts. A random effects model is used to derive mean and variance models for estimated disease rates. Estimating equations for relative rate parameters are then developed by replacing cohort covariate averages by corresponding sample averages. The asymptotic distribution of regression parameter estimates is derived, and the asymptotic bias is shown to be small, even if covariates are contaminated by classical random measurement errors, provided the covariate sample size in each cohort is not small. Simulation studies, motivated by international data on diet and breast cancer, provide insights into the properties of the proposed estimators.

An Analysis of Paediatric Cd4 Counts for Acquired Immune Deficiency Syndrome Using Flexible Random Curves

Abstract: SUMMARY In this paper we analyse CD4 counts from infants born to mothers who are infected with the human immunodeficiency virus. A random effects model with linear or low order polynomials in time is unsatisfactory for these longitudinal data We develop an alternative approach based on a flexible family of models for which both the fixed and the random effects are linear combinations of B-splines. The fixed and random parts are smooth functions of time and the covariance structure is parsimonious. The procedure allows estimates of each individual's smooth trajectory over time to be exhibited. Model selection, estimation and computation are discussed. Centile curves are presented that take into account the longitudinal nature of the data. We emphasize a graphical approach to the presentation of results.