Statistics in medicine
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Statistics in medicine · Dec 2010
Summary ROC curve based on a weighted Youden index for selecting an optimal cutpoint in meta-analysis of diagnostic accuracy.
Established approaches for analyzing meta-analyses of diagnostic accuracy model the bivariate distribution of the observed pairs of specificity Sp and sensitivity Se, thus accounting for across-study correlation. However, it is still a matter of debate how to define a summary ROC (SROC) curve. It was recently pointed out that the SROC curve is in principle unidentifiable if only one (Sp, Se) pair per study is known. ⋯ While the slope depends on the variance ratio of the underlying distributions, the intercept is a function of the mean difference. Our approach leads in a natural way to a new, model-based proposal for a summary ROC curve. It is illustrated using an example from the literature.
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Statistics in medicine · Oct 2010
Visualizing and assessing discrimination in the logistic regression model.
Logistic regression models are widely used in medicine for predicting patient outcome (prognosis) and constructing diagnostic tests (diagnosis). Multivariable logistic models yield an (approximately) continuous risk score, a transformation of which gives the estimated event probability for an individual. A key aspect of model performance is discrimination, that is, the model's ability to distinguish between patients who have (or will have) an event of interest and those who do not (or will not). ⋯ The larger the overlap, the weaker the discrimination. Under certain assumptions about the distribution of the risk score, the c-index, effect size and overlap are functionally related. We illustrate the ideas with simulated and real data sets.
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Statistics in medicine · Sep 2010
Sample size determination in clinical trials with multiple co-primary binary endpoints.
Clinical trials often employ two or more primary efficacy endpoints. One of the major problems in such trials is how to determine a sample size suitable for multiple co-primary correlated endpoints. We provide fundamental formulae for the calculation of power and sample size in order to achieve statistical significance for all the multiple primary endpoints given as binary variables. ⋯ For all five methods, the achieved sample size decreases as the value of association measure increases when the effect sizes among endpoints are approximately equal. In particular, a high positive association has a greater effect on the decrease in the sample size. On the other hand, such a relationship is not very strong when the effect sizes are different.
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Statistics in medicine · Sep 2010
Comparative StudyThe performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies.
Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. ⋯ Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively.
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Statistics in medicine · Sep 2010
Comparative StudyA comparison of the results of intent-to-treat, per-protocol, and g-estimation in the presence of non-random treatment changes in a time-to-event non-inferiority trial.
While intent-to-treat (ITT) analysis is widely accepted for superiority trials, there remains debate about its role in non-inferiority trials. It has often been said that ITT analysis tends to be anti-conservative in demonstrating non-inferiority, suggesting that per-protocol (PP) analysis may be preferable for non-inferiority trials, despite the inherent bias of such analyses. We propose using randomization-based g-estimation analyses that more effectively preserve the integrity of randomization than do the more widely used PP analyses. ⋯ PP analysis, in which treatment-switching cases were censored at the time of treatment change, maintained type I error near the nominal level for independent treatment changes, whereas for outcome-dependent cases, PP analysis was either conservative or anti-conservative depending on the mechanism underlying the percentage of treatment changes. G-estimation analysis maintained type I error near the nominal level even for outcome-dependent treatment changes, although information on unmeasured covariates is not used in the analysis. Thus, randomization-based g-estimation analyses should be used to supplement the more conventional ITT and PP analyses, especially for non-inferiority trials.