Statistical Methods in Medical Research (STAT METHODS MED RES)

Publisher: SAGE Publications

Journal description

Statistical Methods in Medical Research is the leading vehicle for review articles in all the main areas of medical statistics and is an essential reference for all medical statisticians. It is particularly useful for medical researchers dealing with data and provides a key resource for medical and statistical libraries, as well as pharmaceutical companies. This unique journal is devoted solely to statistics and medicine and aims to keep professionals abreast of the many powerful statistical techniques now available to the medical profession. As techniques are constantly adopted by statisticians working both inside and outside the medical environment, this review journal aims to satisfy the increasing demand for accurate and up-to-the-minute information.

Current impact factor: 4.47

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 4.472
2013 Impact Factor 2.957
2012 Impact Factor 2.364
2011 Impact Factor 2.443
2010 Impact Factor 1.768
2009 Impact Factor 2.569
2008 Impact Factor 2.177
2007 Impact Factor 1.492
2006 Impact Factor 1.377
2005 Impact Factor 1.327
2004 Impact Factor 2.583
2003 Impact Factor 1.857
2002 Impact Factor 1.553
2001 Impact Factor 1.886

Impact factor over time

Impact factor

Additional details

5-year impact 3.73
Cited half-life >10.0
Immediacy index 1.28
Eigenfactor 0.01
Article influence 2.61
Website Statistical Methods in Medical Research website
Other titles Statistical methods in medical research (Online)
ISSN 1477-0334
OCLC 42423902
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

SAGE Publications

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Authors retain copyright
    • Pre-print on any website
    • Author's post-print on author's personal website, departmental website, institutional website or institutional repository
    • On other repositories including PubMed Central after 12 months embargo
    • Publisher copyright and source must be acknowledged
    • Publisher's version/PDF cannot be used
    • Post-print version with changes from referees comments can be used
    • "as published" final version with layout and copy-editing changes cannot be archived but can be used on secure institutional intranet
    • Must link to publisher version with DOI
    • Publisher last reviewed on 29/07/2015
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. This article describes the preparatory simulation study for a Bayesian response-adaptive dose-finding trial design. Dexamethasone for Excessive Menstruation aims to assess the efficacy of Dexamethasone in reducing excessive menstrual bleeding and to determine the best dose for further study. To maximise learning about the dose response, patients receive placebo or an active dose with randomisation probabilities adapting based on evidence from patients already recruited. The dose-response relationship is estimated using a flexible Bayesian Normal Dynamic Linear Model. Several competing design options were considered including: number of doses, proportion assigned to placebo, adaptation criterion, and number and timing of adaptations. We performed a fractional factorial study using SAS software to simulate virtual trial data for candidate adaptive designs under a variety of scenarios and to invoke WinBUGS for Bayesian model estimation. We analysed the simulated trial results using Normal linear models to estimate the effects of each design feature on empirical type I error and statistical power. Our readily-implemented approach using widely available statistical software identified a final design which performed robustly across a range of potential trial scenarios.
    Statistical Methods in Medical Research 10/2015; DOI:10.1177/0962280215606155
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    ABSTRACT: In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215607546
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    ABSTRACT: Time-dependent covariates can be modeled within the Cox regression framework and can allow both proportional and nonproportional hazards for the risk factor of research interest. However, in many areas of health services research, interest centers on being able to estimate residual longevity after the occurrence of a particular event such as stroke. The survival trajectory of patients experiencing a stroke can be potentially influenced by stroke type (hemorrhagic or ischemic), time of the stroke (relative to time zero), time since the stroke occurred, or a combination of these factors. In such situations, researchers are more interested in estimating lifetime lost due to stroke rather than merely estimating the relative hazard due to stroke. To achieve this, we propose an ensemble approach using the generalized gamma distribution by means of a semi-Markov type model with an additive hazards extension. Our modeling framework allows stroke as a time-dependent covariate to affect all three parameters (location, scale, and shape) of the generalized gamma distribution. Using the concept of relative times, we answer the research question by estimating residual life lost due to ischemic and hemorrhagic stroke in the chronic dialysis population.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215605107
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    ABSTRACT: In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215607411
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    ABSTRACT: The marginalized two-part (MTP) model for semicontinuous data proposed by Smith et al. provides direct inference for the effect of covariates on the marginal mean of positively continuous data with zeros. This brief note addresses mischaracterizations of the MTP model by Gebregziabher et al. Additionally, the MTP model is extended to incorporate the three-parameter generalized gamma distribution, which takes many well-known distributions as special cases, including the Weibull, gamma, inverse gamma, and log-normal distributions. © The Author(s) 2015.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215602290
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    ABSTRACT: The Tobit model, also known as a censored regression model to account for left- and/or right-censoring in the dependent variable, has been used in many areas of applications, including dental health, medical research and economics. The reported Tobit model coefficient allows estimation and inference of an exposure effect on the latent dependent variable. However, this model does not directly provide overall exposure effects estimation on the original outcome scale. We propose a direct-marginalization approach using a reparameterized link function to model exposure and covariate effects directly on the truncated dependent variable mean. We also discuss an alternative average-predicted-value, post-estimation approach which uses model-predicted values for each person in a designated reference group under different exposure statuses to estimate covariate-adjusted overall exposure effects. Simulation studies were conducted to show the unbiasedness and robustness properties for both approaches under various scenarios. Robustness appears to diminish when covariates with substantial effects are imbalanced between exposure groups; we outline an approach for model choice based on information criterion fit statistics. The methods are applied to the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort study to assess associations between obesity and cognitive function in the non-Hispanic white participants. © The Author(s) 2015.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215602716
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    ABSTRACT: The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215602040
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    ABSTRACT: Many non-experimental studies use propensity-score methods to estimate causal effects by balancing treatment and control groups on a set of observed baseline covariates. Full matching on the propensity score has emerged as a particularly effective and flexible method for utilizing all available data, and creating well-balanced treatment and comparison groups. However, full matching has been used infrequently with binary outcomes, and relatively little work has investigated the performance of full matching when estimating effects on binary outcomes. This paper describes methods that can be used for estimating the effect of treatment on binary outcomes when using full matching. It then used Monte Carlo simulations to evaluate the performance of these methods based on full matching (with and without a caliper), and compared their performance with that of nearest neighbour matching (with and without a caliper) and inverse probability of treatment weighting. The simulations varied the prevalence of the treatment and the strength of association between the covariates and treatment assignment. Results indicated that all of the approaches work well when the strength of confounding is relatively weak. With stronger confounding, the relative performance of the methods varies, with nearest neighbour matching with a caliper showing consistently good performance across a wide range of settings. We illustrate the approaches using a study estimating the effect of inpatient smoking cessation counselling on survival following hospitalization for a heart attack. © The Author(s) 2015.
    Statistical Methods in Medical Research 09/2015; DOI:10.1177/0962280215601134
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    ABSTRACT: Rank-based sampling designs are widely used in situations where measuring the variable of interest is costly but a small number of sampling units (set) can be easily ranked prior to taking the final measurements on them and this can be done at little cost. When the variable of interest is binary, a common approach for ranking the sampling units is to estimate the probabilities of success through a logistic regression model. However, this requires training samples for model fitting. Also, in this approach once a sampling unit has been measured, the extra rank information obtained in the ranking process is not used further in the estimation process. To address these issues, in this paper, we propose to use the partially rank-ordered set sampling design with multiple concomitants. In this approach, instead of fitting a logistic regression model, a soft ranking technique is employed to obtain a vector of weights for each measured unit that represents the probability or the degree of belief associated with its rank among a small set of sampling units. We construct an estimator which combines the rank information and the observed partially rank-ordered set measurements themselves. The proposed methodology is applied to a breast cancer study to estimate the proportion of patients with malignant (cancerous) breast tumours in a given population. Through extensive numerical studies, the performance of the estimator is evaluated under various concomitants with different ranking potentials (i.e. good, intermediate and bad) and tie structures among the ranks. We show that the precision of the partially rank-ordered set estimator is better than its counterparts under simple random sampling and ranked set sampling designs and, hence, the sample size required to achieve a desired precision is reduced. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215601458
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    ABSTRACT: Split-mouth designs are frequently used in dental clinical research, where a mouth is divided into two or more experimental segments that are randomly assigned to different treatments. It has the distinct advantage of removing a lot of inter-subject variability from the estimated treatment effect. Methods of statistical analyses for split-mouth design have been well developed. However, little work is available on sample size consideration at the design phase of a split-mouth trial, although many researchers pointed out that the split-mouth design can only be more efficient than a parallel-group design when within-subject correlation coefficient is substantial. In this paper, we propose to use the generalized estimating equation (GEE) approach to assess treatment effect in split-mouth trials, accounting for correlations among observations. Closed-form sample size formulas are introduced for the split-mouth design with continuous and binary outcomes, assuming exchangeable and "nested exchangeable" correlation structures for outcomes from the same subject. The statistical inference is based on the large sample approximation under the GEE approach. Simulation studies are conducted to investigate the finite-sample performance of the GEE sample size formulas. A dental clinical trial example is presented for illustration. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215601137
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    ABSTRACT: We study matched pair designs with two binary endpoints under three different approaches. Power approximation and sample size calculation are derived under these situations and facilitated by R programs. An adaptive design with sample size re-estimation is also presented. Through extensive simulations, we provide general guidelines for practitioners to choose the best approach according to the ranges of the interested parameters in the sense of feasibility and robustness. Application to a cancer chemotherapy trial is illustrated. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215601136
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    ABSTRACT: Adherence to medication is often measured as a continuous outcome but analyzed as a dichotomous outcome due to lack of appropriate tools. In this paper, we illustrate the use of the temporal kernel canonical correlation analysis (tkCCA) as a method to analyze adherence measurements and symptom levels on a continuous scale. The tkCCA is a novel method developed for studying the relationship between neural signals and hemodynamic response detected by functional MRI during spontaneous activity. Although the tkCCA is a powerful tool, it has not been utilized outside the application that it was originally developed for. In this paper, we simulate time series of symptoms and adherence levels for patients with a hypothetical brain disorder and show how the tkCCA can be used to understand the relationship between them. We also examine, via simulations, the behavior of the tkCCA under various missing value mechanisms and imputation methods. Finally, we apply the tkCCA to a real data example of psychotic symptoms and adherence levels obtained from a study based on subjects with a first episode of schizophrenia, schizophreniform or schizoaffective disorder. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215598805
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    ABSTRACT: One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to identify subgroups of trajectories within a population, such that trajectories that are grouped together are more similar to each other than to trajectories in distinct groups. Group-based trajectory models generally assume a certain structure in the covariances between measurements, for example conditional independence, homogeneous variance between groups or stationary variance over time. Violations of these assumptions could be expected to result in poor model performance. We used simulation to investigate the effect of covariance misspecification on misclassification of trajectories in commonly used models under a range of scenarios. To do this we defined a measure of performance relative to the ideal Bayesian correct classification rate. We found that the more complex models generally performed better over a range of scenarios. In particular, incorrectly specified covariance matrices could significantly bias the results but using models with a correct but more complicated than necessary covariance matrix incurred little cost. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215598806
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    ABSTRACT: In longitudinal clinical trials, some subjects will drop out before completing the trial, so their measurements towards the end of the trial are not obtained. Mixed-effects models for repeated measures (MMRM) analysis with "unstructured" (UN) covariance structure are increasingly common as a primary analysis for group comparisons in these trials. Furthermore, model-based covariance estimators have been routinely used for testing the group difference and estimating confidence intervals of the difference in the MMRM analysis using the UN covariance. However, using the MMRM analysis with the UN covariance could lead to convergence problems for numerical optimization, especially in trials with a small-sample size. Although the so-called sandwich covariance estimator is robust to misspecification of the covariance structure, its performance deteriorates in settings with small-sample size. We investigated the performance of the sandwich covariance estimator and covariance estimators adjusted for small-sample bias proposed by Kauermann and Carroll (J Am Stat Assoc 2001; 96: 1387-1396) and Mancl and DeRouen (Biometrics 2001; 57: 126-134) fitting simpler covariance structures through a simulation study. In terms of the type 1 error rate and coverage probability of confidence intervals, Mancl and DeRouen's covariance estimator with compound symmetry, first-order autoregressive (AR(1)), heterogeneous AR(1), and antedependence structures performed better than the original sandwich estimator and Kauermann and Carroll's estimator with these structures in the scenarios where the variance increased across visits. The performance based on Mancl and DeRouen's estimator with these structures was nearly equivalent to that based on the Kenward-Roger method for adjusting the standard errors and degrees of freedom with the UN structure. The model-based covariance estimator with the UN structure under unadjustment of the degrees of freedom, which is frequently used in applications, resulted in substantial inflation of the type 1 error rate. We recommend the use of Mancl and DeRouen's estimator in MMRM analysis if the number of subjects completing is (n + 5) or less, where n is the number of planned visits. Otherwise, the use of Kenward and Roger's method with UN structure should be the best way. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215597938
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    ABSTRACT: Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization. We discover that it is small in magnitude and, under mild assumptions, can only be negative - causing one's estimate to be closer to zero on average than the truth. A simple unbiased estimator for p is obtained, but it is shown to have a large mean squared error. Two approaches are therefore explored to improve its precision based on inverse probability weighting and Rao-Blackwellization. We illustrate these estimation strategies using two well-known designs from the literature. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215597716
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    ABSTRACT: Recently, the joint analysis of longitudinal and survival data has been an active research area. Most joint models focus on survival data with only one type of failure. The research on joint modeling of longitudinal and competing risks survival data is sparse. Even so, many joint models for this type of data assume parametric function forms for both longitudinal and survival sub-models, thus limits their use. Further, the common data features that are usually observed in practice, such as asymmetric distribution and missingness in response, measurement errors in covariate, need to be taken into account for reliable parameter estimation. The statistical inference is complicated when all these factors are considered simultaneously. In the article, driven by a motivating example, we assume nonparametric function forms for the varying coefficients in both longitudinal and competing risks survival sub-models. We propose a Bayesian nonparametric mixed-effects joint model for the analysis of longitudinal-competing risks data with asymmetry, missingness, and measurement errors. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed method to an AIDS dataset and compare a few candidate models under various settings. Some interesting results are reported. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215597939
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    ABSTRACT: For complex surveys with a binary outcome, logistic regression is widely used to model the outcome as a function of covariates. Complex survey sampling designs are typically stratified cluster samples, but consistent and asymptotically unbiased estimates of the logistic regression parameters can be obtained using weighted estimating equations (WEEs) under the naive assumption that subjects within a cluster are independent. Despite the relatively large samples typical of many complex surveys, with rare outcomes, many interaction terms, or analysis of subgroups, the logistic regression parameters estimates from WEE can be markedly biased, just as with independent samples. In this paper, we propose bias-corrected WEEs for complex survey data. The proposed method is motivated by a study of postoperative complications in laparoscopic cystectomy, using data from the 2009 United States' Nationwide Inpatient Sample complex survey of hospitals. © The Author(s) 2015.
    Statistical Methods in Medical Research 08/2015; DOI:10.1177/0962280215596550