PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15718113

Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data.

M C Gulliford·G Adams·O C Ukoumunne·R Latinovic·S Chinn·M J Campbell
View original source ↗Export metadata ↓

Abstract

BACKGROUND AND OBJECTIVE: To describe the association between values for a proportion and the intraclass correlation coefficient (ICC). METHODS: Analysis of data obtained from the General Practice Research Database (GPRD) for variation between United Kingdom general practices and results from a Health Technology Assessment (HTA) review for a range of outcomes in community and health services settings. RESULTS: There were 188 ICCs from the GPRD, the median prevalence was 13.1% (interquartile range IQR 3.5 to 28.4%) and median ICC 0.051 (IQR 0.011 to 0.094). There were 136 ICCs from the HTA review, with median prevalence 6.5% (IQR 0.4 to 20.7%) and median ICC 0.006 (IQR 0.0003 to 0.036). There was a linear association of log ICC with log prevalence in both datasets (GPRD, regression coefficient 0.61, 95% confidence interval 0.53 to 0.69, P < 0.001; HTA, 0.91, 0.81 to 1.01, P < 0.001). When the prevalence was 1% the predicted ICC was 0.008 from the GPRD or 0.002 from the HTA, but when the prevalence was 40% the predicted ICC was 0.075 (GPRD) or 0.046 (HTA). CONCLUSION: The prevalence of an outcome may be used to make an informed assumption about the magnitude of the intraclass correlation coefficient.

Explore related subjects

Data Interpretation, StatisticalDatabases, FactualFamily PracticeHealth Services ResearchHumansPrevalencePrimary Health CareRandomized Controlled Trials as TopicResearch DesignTechnology Assessment, BiomedicalUnited KingdomJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M C Gulliford, G Adams, O C Ukoumunne, R Latinovic, S Chinn, M J Campbell. 2005. Intraclass correlation coefficient and outcome prevalence are associated in clustered binary data.. https://doi.org/10.1016/j.jclinepi.2004.08.012

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.

Clinicians and health service researchers are frequently interested in predicting patient-specific probabilities of adverse events (e.g. death, disease recurrence, post-operative complications, hospital readmission). There is an increasing interest in the use of classification and regression trees (CART) for predicting outcomes in clinical studies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting mortality after hospitalization with an acute myocardial infarction (AMI). We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 9484 patients admitted to hospital with an AMI in Ontario. We used repeated split-sample validation: the data were randomly divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using the area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times-the initial data set was randomly divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.762, while the mean ROC curve area of a simple logistic regression model was 0.845. The mean ROC curve areas for the other methods ranged from a low of 0.831 to a high of 0.851. Our study shows that regression trees do not perform as well as logistic regression for predicting mortality following AMI. However, the logistic regression model had performance comparable to that of more flexible, data-driven models such as GAMs and MARS.

Data Interpretation, Statistical↗
PubMed2007

Multiple linear regression with some correlated errors: classical and robust methods.

In this paper we consider classical and robust methods of estimation and diagnostics for the multiple linear regression model when some of the errors are correlated. This work was motivated by the analysis of a medical data set, from an observational study aimed at identifying factors affecting the outcome of a surgical method for the correction of scoliosis (abnormal lateral spinal curvature). There are 392 observations but some of them are on the same patient (double curves). It seems adequate to consider a multiple linear regression model but, since it is not desirable to discard the double curves, the assumption of non-correlated errors is clearly violated, and this is indeed confirmed by related diagnostics on the residuals (Durbin-Watson test). A more appropriate model retains the linear structure but allows for non-null correlation between the errors on the same patient. We propose two different procedures for the estimation of the parameters of the linear model and the correlation parameters: maximum likelihood assuming normal errors and a robustified version obtained by plugging-in results from robust linear regression. The latter procedure is designed to be resistant to outlying observations or error distributions with heavy tails and has produced the most satisfactory results for the analysed data set.

Data Interpretation, Statistical↗
PubMed2007

Adaptive design method based on sum of p-values.

Bauer and Kohne proposed an adaptive design using Fisher's combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an adaptive design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the adaptive designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power.

Data Interpretation, Statistical↗