Statistics in medicine
-
Statistics in medicine · Aug 2007
A simulation study of odds ratio estimation for binary outcomes from cluster randomized trials.
We used simulation to compare accuracy of estimation and confidence interval coverage of several methods for analysing binary outcomes from cluster randomized trials. The following methods were used to estimate the population-averaged intervention effect on the log-odds scale: marginal logistic regression models using generalized estimating equations with information sandwich estimates of standard error (GEE); unweighted cluster-level mean difference (CL/U); weighted cluster-level mean difference (CL/W) and cluster-level random effects linear regression (CL/RE). Methods were compared across trials simulated with different numbers of clusters per trial arm, numbers of subjects per cluster, intraclass correlation coefficients (rho), and intervention versus control arm proportions. ⋯ CL/U and CL/W have good properties for trials where the number of subjects per cluster is sufficiently large and rho is sufficiently small. CL/RE also has good properties in this situation provided a t-distribution multiplier is used for confidence interval calculation in studies with small numbers of clusters. For studies where the number of subjects per cluster is small and rho is large all cluster-level methods may perform poorly for studies with between 10 and 50 clusters per trial arm.
-
A mediator acts as a third variable in the causal pathway between a risk factor and an outcome. In this paper, we consider the estimation of the mediation effect when the mediator is a binary variable. ⋯ Our theoretical developments, which are supported by a Monte Carlo study, show that the estimators that account for the binary nature of the mediator are consistent for the mediation effect defined in this paper while other estimators are inconsistent. We use these estimators to study the mediation effect of chronic cerebral infarction in the causal relationship between the apolipoprotein E epsilon4 allele and cognitive function among 233 deceased participants from the Religious Orders Study, a longitudinal, clinical-pathologic study of aging and Alzheimer's disease.
-
Statistics in medicine · Jul 2007
Modelling seasonal and weather dependency of cardiac arrests using the covariate order method.
A data set concerning cardiac arrests treated by the Emergency Medical Service in Trondheim during a nine year period is analysed. The relationship between the occurrence of cardiac arrest and covariates related to weather and season is examined. The covariate order method is used in the analysis of the data. ⋯ The incidence of cardiac arrest decreases with increasing temperature. Further a significant effect of snowfall is also found, with increased intensity of cardiac arrest on days with snowfall. A more borderline significant effect of precipitation is also identified.
-
Statistics in medicine · Jul 2007
The performance of different propensity score methods for estimating marginal odds ratios.
The propensity score which is the probability of exposure to a specific treatment conditional on observed variables. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. In the medical literature, propensity score methods are frequently used for estimating odds ratios. ⋯ Stratifying on the propensity score resulted in moderate bias, with relative biases ranging from 15.8 to 59.2 per cent. For both methods, relative bias was proportional to the true odds ratio. Finally, matching on the propensity score tended to result in estimators with the lowest MSE.
-
Statistics in medicine · May 2007
Regression analysis of failure time data with informative interval censoring.
Interval censoring arises when a subject misses prescheduled visits at which the failure is to be assessed. Most existing approaches for analysing interval-censored failure time data assume that the censoring mechanism is independent of the true failure time. ⋯ The method makes use of the proportional hazards frailty model and an EM algorithm is presented for estimation. Finite sample properties of the proposed estimators of regression parameters are examined through simulation studies and we illustrate the method with data from an AIDS study.