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This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator are described in a small simulation study. [ABSTRACT FROM AUTHOR]

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The 2-in-1 adaptive design allows seamless expansion of an ongoing Phase II trial into a Phase III trial to expedite a drug development program. Since its publication, it has generated a lot of interest. So far, most of the related research focused on type I error control. Similar to most adaptive designs, 2-in-1 design could also pose a great challenge on estimation of treatment effect due to the data-driven adaptation. In addition, the use of intermediate endpoint for interim adaptive decision-making is a less well-studied field. In this paper, we investigate the bias and variances in estimation for 2-in-1 design and some of its extensions, and propose some bias-adjusted estimators for 2-in-1 design. The properties of the proposed estimators are further studied theoretically and/or numerically, so as to provide guidance on how to interpret the estimated treatment effect of 2-in-1 design. [ABSTRACT FROM AUTHOR]

Outcomes in clinical trials may be affected by the choice of treatment that participants might make, if they were indeed allowed to choose (a so-called selection effect), and by whether they actually receive their preferred treatment (a preference effect). Selection and preference effects can be important, but they cannot be estimated in the conventional trial design. An alternative approach is the two-stage randomised trial, in which participants are first randomly divided into two subgroups. In one subgroup, participants are randomly assigned to treatments, while in the other, participants are allowed to choose their own treatment. This approach yields estimates of the direct treatment effect, and of the preference and selection effects. The latter two provide insight that goes considerably beyond what is possible in the standard randomised trial. In this paper, we determine the optimal proportion of participants who should be allocated to the choice subgroup. The precision of the estimated selection, preference and treatment effects are functions of: the total sample size; the proportion of participants allocated to choose their treatment; the variances of the outcome; the proportions of participants who select each treatment in the choice group; and the selection, preference and treatment effects themselves. We develop general expressions for the optimum proportion of participants in the choice group, depending on which effects are of primary interest. We illustrate the results with trial data comparing alternative clinical management strategies for women with abnormal results on cervical screening. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A variety of methods have been proposed to estimate a standard deviation, when only a sample range has been observed or reported. This problem occurs in the interpretation of individual clinical studies that are incompletely reported, and also in their incorporation into meta‐analyses. The methods differ with respect to their focus being either on the standard deviation in the underlying population or on the particular sample in hand, a distinction that has not been widely recognized. In this article, we contrast and compare various estimators of these two quantities with respect to bias and mean squared error, for normally distributed data. We show that unbiased estimators are available for either quantity, and recommend our preferred methods. We also propose a Taylor series method to obtain inverse‐variance weights, for samples where only the sample range is available; this method yields very little bias, even for quite small samples. In contrast, the naïve approach of simply taking the inverse of an estimated variance is shown to be substantially biased, and can place unduly large weight on small samples, such as small clinical trials in a meta‐analysis. Accordingly, this naïve (but commonly used) method is not recommended. [ABSTRACT FROM AUTHOR]

One-stage individual participant data meta-analysis models should account for within-trial clustering, but it is currently debated how to do this. For continuous outcomes modeled using a linear regression framework, two competing approaches are a stratified intercept or a random intercept. The stratified approach involves estimating a separate intercept term for each trial, whereas the random intercept approach assumes that trial intercepts are drawn from a normal distribution. Here, through an extensive simulation study for continuous outcomes, we evaluate the impact of using the stratified and random intercept approaches on statistical properties of the summary treatment effect estimate. Further aims are to compare (i) competing estimation options for the one-stage models, including maximum likelihood and restricted maximum likelihood, and (ii) competing options for deriving confidence intervals (CI) for the summary treatment effect, including the standard normal-based 95% CI, and more conservative approaches of Kenward-Roger and Satterthwaite, which inflate CIs to account for uncertainty in variance estimates. The findings reveal that, for an individual participant data meta-analysis of randomized trials with a 1:1 treatment:control allocation ratio and heterogeneity in the treatment effect, (i) bias and coverage of the summary treatment effect estimate are very similar when using stratified or random intercept models with restricted maximum likelihood, and thus either approach could be taken in practice, (ii) CIs are generally best derived using either a Kenward-Roger or Satterthwaite correction, although occasionally overly conservative, and (iii) if maximum likelihood is required, a random intercept performs better than a stratified intercept model. An illustrative example is provided. [ABSTRACT FROM AUTHOR]

The goal of quantitative traits genome-wide association studies is to identify associations between a phenotypic variable, such as a vitamin level and genetic variants, often single-nucleotide polymorphisms. When funding limits the number of assays that can be performed to measure the level of the phenotypic variable, a subgroup of subjects is often randomly selected from the genotype database and the level of the phenotypic variable is then measured for each subject. Because only a proportion of the genotype data can be used, such a simple random sampling method may suffer from substantial loss of efficiency, especially when the number of assays is relative small and the frequency of the less common variant (minor allele frequency) is low. We propose a pooling strategy in which subjects in a randomly selected reference subgroup are aligned with randomly selected subjects from the remaining study subjects to form independent pools; blood samples from subjects in each pool are mixed; and the level of the phenotypic variable is measured for each pool. We demonstrate that the proposed pooling approach produces considerable gains in efficiency over the simple random sampling method for inference concerning the phenotype-genotype association, resulting in higher precision and power. The methods are illustrated using genotypic and phenotypic data from the Trinity Students Study, a quantitative genome-wide association study. [ABSTRACT FROM AUTHOR]

A wide variety of estimators of the between-study variance are available in random-effects meta-analysis. Many, but not all, of these estimators are based on the method of moments. The DerSimonian-Laird estimator is widely used in applications, but the Paule-Mandel estimator is an alternative that is now recommended. Recently, DerSimonian and Kacker have developed two-step moment-based estimators of the between-study variance. We extend these two-step estimators so that multiple (more than two) steps are used. We establish the surprising result that the multistep estimator tends towards the Paule-Mandel estimator as the number of steps becomes large. Hence, the iterative scheme underlying our new multistep estimator provides a hitherto unknown relationship between two-step estimators and Paule-Mandel estimator. Our analysis suggests that two-step estimators are not necessarily distinct estimators in their own right; instead, they are quantities that are closely related to the usual iterative scheme that is used to calculate the Paule-Mandel estimate. The relationship that we establish between the multistep and Paule-Mandel estimator is another justification for the use of the latter estimator. Two-step and multistep estimators are perhaps best conceptualized as approximate Paule-Mandel estimators. [ABSTRACT FROM AUTHOR]

A number of antibody biomarkers have been developed to distinguish between recent and established Human Immunodeficiency Virus (HIV) infection and used for HIV incidence estimation from cross-sectional specimens. In general, a cut-off value is specified, and estimates of the following parameters are needed: (i) the mean time interval ( w) between seroconversion and reaching that cut-off; (ii) the probability of correctly identifying individuals who became infected in the last w years (sensitivity); and (iii) the probability of correctly identifying individuals who have been infected for more than w years (specificity). We develop two statistical methods to study the distribution of a biomarker and derive a formula for estimating HIV incidence from a cross-sectional survey. Both methods allow handling interval censored data and basically consist of using a generalized mixture model to model the growth of the biomarker as a function of time since infection. The first uses data from all followed-up individuals and allows incidence estimation in the cohort, whereas the second only uses data from seroconverters. We illustrate our methods using repeated measures of the IgG capture BED enzyme immunoassay. Estimates of calibration parameters, that is, mean window period, mean recency period, sensitivity, and specificities obtained from both models are comparable. The formula derived for incidence estimation gives the maximum likelihood estimate of incidence which, for a given window period, depends only on sensitivity and specificity. The optimal choice of the window period is discussed. Numerical simulations suggest that data from seroconverters can provide reasonable estimates of the calibration parameters. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the diagnostic area, the usual setting considers two populations: nondiseased and diseased. The use of the standard ROC analysis methodology is well established. Sometimes, however, diagnostic problems inherently include more than two classification states. For example, 'yes, uncertain, no' or 'low, normal, high'. Here we consider a three-normal distribution setting and derive estimators for the optimum thresholds between states based on a cost function. These estimators can be extended for clinical contexts with more than three states. This approach is well known for the two-state setting and its advantage lies in the fact that it accounts for the specific context's properties, such as disease prevalence and classification costs. Here we calculated the variance of the estimators by the use of parametric methods on nonlinear equations and we constructed confidence intervals accounting for possible uncertainty in the threshold estimation. We conducted a simulation study to assess the performance of these estimators and the confidence intervals. Comparisons with the naive threshold estimation method of joining the distributions two-by-two and applying standard ROC techniques proved that the latter method is not reliable for all parameter combinations and should be avoided. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]