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Use of auxiliary covariates in estimating a biomarker-adjusted treatment effect model with clinical trial data
A biomarker-adjusted treatment effect (BATE) model describes the effect of one treatment versus another on a subpopulation of patients defined by a biomarker. Such a model can be estimated from clinical trial data without relying on additional modeling assumptions, and the estimator can be made more efficient by incorporating information on the main effect of the biomarker on the outcome of interest. Motivated by an HIV trial known as THRIVE, we consider the use of auxiliary covariates, which are usually available in clinical trials and have been used in overall treatment comparisons, in estimating a BATE model. Such covar...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Zhang, Z., Qu, Y., Zhang, B., Nie, L., Soon, G. Tags: Articles Source Type: research

Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves
Assessments of the discriminative performance of prognostic models have led to the development of several measures that extend the concept of discrimination as evaluated by the receiver operating characteristics curve and the area under the receiver operating characteristic curve (AUC) of diagnostic settings. Thus, several time-dependent-receiver operating characteristic curve and AUC(t) have been proposed. One of the most used, the cumulative/dynamic AUCC,D(t) is the probability that, given two randomly chosen patients, one having failed before t and the other having failed after t, the prognostic marker will be correctly...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Lambert, J., Chevret, S. Tags: Articles Source Type: research

Rasch-family models are more valuable than score-based approaches for analysing longitudinal patient-reported outcomes with missing data
This study highlights the interest of Rasch-based models in clinical research and epidemiology for the analysis of incomplete patient-reported outcomes data. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: de Bock, E., Hardouin, J.-B., Blanchin, M., Le Neel, T., Kubis, G., Bonnaud-Antignac, A., Dantan, E., Sebille, V. Tags: Articles Source Type: research

Causal inference with missing exposure information: Methods and applications to an obstetric study
Causal inference in observational studies is frequently challenged by the occurrence of missing data, in addition to confounding. Motivated by the Consortium on Safe Labor, a large observational study of obstetric labor practice and birth outcomes, this article focuses on the problem of missing exposure information in a causal analysis of observational data. This problem can be approached from different angles (i.e. missing covariates and causal inference), and useful methods can be obtained by drawing upon the available techniques and insights in both areas. In this article, we describe and compare a collection of methods...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Zhang, Z., Liu, W., Zhang, B., Tang, L., Zhang, J. Tags: Articles Source Type: research

Multilevel models for cost-effectiveness analyses that use cluster randomised trial data: An approach to model choice
Multilevel models provide a flexible modelling framework for cost-effectiveness analyses that use cluster randomised trial data. However, there is a lack of guidance on how to choose the most appropriate multilevel models. This paper illustrates an approach for deciding what level of model complexity is warranted; in particular how best to accommodate complex variance–covariance structures, right-skewed costs and missing data. Our proposed models differ according to whether or not they allow individual-level variances and correlations to differ across treatment arms or clusters and by the assumed cost distribution (N...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Ng, E. S.-W., Diaz-Ordaz, K., Grieve, R., Nixon, R. M., Thompson, S. G., Carpenter, J. R. Tags: Articles Source Type: research

Multiple imputation in the presence of high-dimensional data
Missing data are frequently encountered in biomedical, epidemiologic and social research. It is well known that a naive analysis without adequate handling of missing data may lead to bias and/or loss of efficiency. Partly due to its ease of use, multiple imputation has become increasingly popular in practice for handling missing data. However, it is unclear what is the best strategy to conduct multiple imputation in the presence of high-dimensional data. To answer this question, we investigate several approaches of using regularized regression and Bayesian lasso regression to impute missing values in the presence of high-d...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Zhao, Y., Long, Q. Tags: Articles Source Type: research

A corrected formulation for marginal inference derived from two-part mixed models for longitudinal semi-continuous data
We present a corrected formulation and additionally explore the use of the two-part model for inferences on the overall marginal mean, which may be of more practical relevance in our application and more generally. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Tom, B. D., Su, L., Farewell, V. T. Tags: Articles Source Type: research

Estimating and testing interactions when explanatory variables are subject to non-classical measurement error
Assessing interactions in linear regression models when covariates have measurement error (ME) is complex. We previously described regression calibration (RC) methods that yield consistent estimators and standard errors for interaction coefficients of normally distributed covariates having classical ME. Here we extend normal based RC (NBRC) and linear RC (LRC) methods to a non-classical ME model, and describe more efficient versions that combine estimates from the main study and internal sub-study. We apply these methods to data from the Observing Protein and Energy Nutrition (OPEN) study. Using simulations we show that (i...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Murad, H., Kipnis, V., Freedman, L. S. Tags: Articles Source Type: research

Composite growth model applied to human oral and pharyngeal structures and identifying the contribution of growth types
The growth patterns of different anatomic structures in the human body vary in terms of growth amount over time, growth rate and growth periods. The oral and pharyngeal structures, also known as vocal tract structures, are housed in the craniofacial complex where the cranium/brain follows a distinct neural growth pattern, and the face follows a distinct somatic or skeletal growth pattern. Thus, it is reasonable to expect the oral and pharyngeal structures to follow a combined or mixed growth pattern. Existing parametric growth models are limited in that they are mainly focused on modeling one particular type of growth patt...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Wang, Y., Chung, M. K., Vorperian, H. K. Tags: Articles Source Type: research

Normalization of mean squared differences to measure agreement for continuous data
Agreement among observations on two variables for reliability or validation purposes is usually assessed by the evaluation of the mean squared differences (MSD). Many transformations of MSD have been proposed to interpret and make statistical inferences about the agreement between the two variables, including the concordance correlation coefficient (CCC) and the random marginal agreement coefficient (RMAC). This paper presents a normalization of MSD based on a reference range and uses it to derive CCC and RMAC (or ACC alternatively). The normalization of MSD enables the comparison between these two coefficients. The paper ...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Almehrizi, R. Tags: Articles Source Type: research

Propensity score estimators for the average treatment effect and the average treatment effect on the treated may yield very different estimates
Conclusion Although both approaches are recommended as valid methods for causal inference, propensity score-matching for ATT and inverse probability of treatment weighting for average treatment effect yield substantially different estimates of treatment effect. The choice of the estimand should drive the choice of the method. (Source: Statistical Methods in Medical Research)
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Pirracchio, R., Carone, M., Rigon, M. R., Caruana, E., Mebazaa, A., Chevret, S. Tags: Articles Source Type: research

Studying noncollapsibility of the odds ratio with marginal structural and logistic regression models
One approach to quantifying the magnitude of confounding in observational studies is to compare estimates with and without adjustment for a covariate, but this strategy is known to be defective for noncollapsible measures such as the odds ratio. Comparing estimates from marginal structural and standard logistic regression models, the total difference between crude and conditional effects can be decomposed into the sum of a noncollapsibility effect and confounding bias. We provide an analytic approach to assess the noncollapsibility effect in a point-exposure study and provide a general formula for expressing the noncollaps...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Pang, M., Kaufman, J. S., Platt, R. W. Tags: Articles Source Type: research

Item response theory and structural equation modelling for ordinal data: Describing the relationship between KIDSCREEN and Life-H
Both item response theory and structural equation models are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the item response theory and structural equation modelling approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebral palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models,...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Titman, A. C., Lancaster, G. A., Colver, A. F. Tags: Articles Source Type: research

Longitudinal prostate-specific antigen reference ranges: Choosing the underlying model of age-related changes
Serial measurements of prostate-specific antigen (PSA) are used as a biomarker for men diagnosed with prostate cancer following an active monitoring programme. Distinguishing pathological changes from natural age-related changes is not straightforward. Here, we compare four approaches to modelling age-related change in PSA with the aim of developing reference ranges for repeated measures of PSA. A suitable model for PSA reference ranges must satisfy two criteria. First, it must offer an accurate description of the trend of PSA on average and in individuals. Second, it must be able to make accurate predictions about new PSA...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Simpkin, A. J., Metcalfe, C., Martin, R. M., Lane, J. A., Donovan, J. L., Hamdy, F. C., Neal, D. E., Tilling, K. Tags: Articles Source Type: research

Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts
We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions...
Source: Statistical Methods in Medical Research - October 1, 2016 Category: Statistics Authors: Howe, L. D., Tilling, K., Matijasevich, A., Petherick, E. S., Santos, A. C., Fairley, L., Wright, J., Santos, I. S., Barros, A. J., Martin, R. M., Kramer, M. S., Bogdanovich, N., Matush, L., Barros, H., Lawlor, D. A. Tags: Articles Source Type: research