# Statistical Methods in Medical Research (STAT METHODS MED RES )

Publisher: SAGE Publications

## 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.

• Impact factor
2.36
• 5-year impact
3.14
• Cited half-life
0.00
• Immediacy index
0.76
• Eigenfactor
0.01
• Article influence
1.95
• 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

• Pre-print
• Author can archive a pre-print version
• Post-print
• Author cannot archive a post-print version
• Restrictions
• 12 months embargo
• Conditions
• On author website, repository and PubMed Central
• On author's personal web site
• 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
• If funding agency rules apply, authors may use SAGE open to comply
• Classification
​ yellow

## Publications in this journal

• ##### Article: A cautionary note on the use of attributable fractions in cohort studies.
[hide abstract]
ABSTRACT: The attributable fraction is a widely used measure to quantify the public health impact of an exposure on an outcome. It was originally proposed for binary outcomes, but attributable fraction estimators have also been proposed for time-to-event outcomes. In this note, we consider an estimator which was proposed by Benichou (Stats Methods Med Res, 2001) and is supposed to estimate the cohort attributable fraction, i.e. the number of events that would have been prevented in the cohort during follow-up, if the exposure would hypothetically have been eliminated. We show that this estimator is only valid under certain assumptions, which are often likely to be violated in practice. We further argue that the cohort attributable fraction may not be of substantial scientific interest in the first place. We propose a potentially more relevant measure of attributable fraction in cohort studies; the baseline attributable fraction. We show how the baseline attributable fraction can be conveniently estimated in Cox proportional hazards models.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Optimal scheduling of post-therapeutic follow-up of patients treated for cancer for early detection of relapses.
[hide abstract]
ABSTRACT: Post-therapeutic surveillance is one important component of cancer care. However, there still is no evidence-based strategies to schedule patients' follow-up examinations. Our approach is based on the modeling of the probability of the onset of relapse at an early asymptotic or preclinical stage and its transition to a clinical stage. For that we consider a multistate homogeneous Markov model, which includes the natural history of relapse. The model also handles separately the different types of possible relapses. The optimal schedule is provided by the calendar visit that maximizes a utility function. The methodology has been applied to laryngeal cancer. The different follow-up strategies revealed to be more efficient than those proposed by different scientific societies.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Optimal selection of individuals for repeated covariate measurements in follow-up studies.
[hide abstract]
ABSTRACT: Repeated covariate measurements bring important information on the time-varying risk factors in long epidemiological follow-up studies. However, due to budget limitations, it may be possible to carry out the repeated measurements only for a subset of the cohort. We study cost-efficient alternatives for the simple random sampling in the selection of the individuals to be remeasured. The proposed selection criteria are based on forms of the D-optimality. The selection methods are compared with the simulation studies and illustrated with the data from the East-West study carried out in Finland from 1959 to 1999. The results indicate that cost savings can be achieved if the selection is focused on the individuals with high expected risk of the event and, on the other hand, on those with extreme covariate values in the previous measurements.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Survival analysis with functional covariates for partial follow-up studies.
[hide abstract]
ABSTRACT: Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of the actual counts, that affect patient's disease-free survival time.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Bayesian analysis of transformation latent variable models with multivariate censored data.
[hide abstract]
ABSTRACT: Transformation latent variable models are proposed in this study to analyze multivariate censored data. The proposed models generalize conventional linear transformation models to semiparametric transformation models that accommodate latent variables. The characteristics of the latent variables were assessed based on several correlated observed indicators through measurement equations. A Bayesian approach was developed with Bayesian P-splines technique and the Markov chain Monte Carlo algorithm to estimate the unknown parameters and transformation functions. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method was applied to analyze a cardiovascular disease data set.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Confidence intervals for intraclass correlation coefficients in variance components models.
[hide abstract]
ABSTRACT: Confidence intervals for intraclass correlation coefficients in agreement studies with continuous outcomes are model-specific and no generic approach exists. This paper provides two generic approaches for intraclass correlation coefficients of the form $$\sum _{q=1}^{Q}{\sigma }_{q}^{2}/(\sum _{q=1}^{Q}{\sigma }_{q}^{2}+\sum _{p=Q+1}^{P}{\sigma }_{p}^{2})$$. The first approach uses Satterthwaite's approximation and an F-distribution. The second approach uses the first and second moments of the intraclass correlation coefficient estimate in combination with a Beta distribution. Both approaches are based on the restricted maximum likelihood estimates for the variance components involved. Simulation studies are conducted to examine the coverage probabilities of the confidence intervals for agreement studies with a mix of small sample sizes. Two different three-way variance components models and balanced and unbalanced one-way random effects models are investigated. The proposed approaches are compared with other approaches developed for these specific models. The approach based on the F-distribution provides acceptable coverage probabilities, but the approach based on the Beta distribution results in accurate coverages for most settings in both balanced and unbalanced designs. A real agreement study is provided to illustrate the approaches.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.
[hide abstract]
ABSTRACT: Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using parametric regressions such as generalised linear models. Misspecification of these models can lead to biased parameter estimates. We compare two approaches that combine the propensity score and the endpoint regression, and can make weaker modelling assumptions, by using machine learning approaches to estimate the regression function and the propensity score. Targeted maximum likelihood estimation is a double-robust method designed to reduce bias in the estimate of the parameter of interest. Bias-corrected matching reduces bias due to covariate imbalance between matched pairs by using regression predictions. We illustrate the methods in an evaluation of different types of hip prosthesis on the health-related quality of life of patients with osteoarthritis. We undertake a simulation study, grounded in the case study, to compare the relative bias, efficiency and confidence interval coverage of the methods. We consider data generating processes with non-linear functional form relationships, normal and non-normal endpoints. We find that across the circumstances considered, bias-corrected matching generally reported less bias, but higher variance than targeted maximum likelihood estimation. When either targeted maximum likelihood estimation or bias-corrected matching incorporated machine learning, bias was much reduced, compared to using misspecified parametric models.
Statistical Methods in Medical Research 02/2014;
• ##### Article: A combined gamma frailty and normal random-effects model for repeated, overdispersed time-to-event data.
[hide abstract]
ABSTRACT: This paper presents, extends, and studies a model for repeated, overdispersed time-to-event outcomes, subject to censoring. Building upon work by Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010), gamma and normal random effects are included in a Weibull model, to account for overdispersion and between-subject effects, respectively. Unlike these authors, censoring is allowed for, and two estimation methods are presented. The partial marginalization approach to full maximum likelihood of Molenberghs et al. (2010) is contrasted with pseudo-likelihood estimation. A limited simulation study is conducted to examine the relative merits of these estimation methods. The modeling framework is employed to analyze data on recurrent asthma attacks in children on the one hand and on survival in cancer patients on the other.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Analysis of cross-over studies with missing data.
[hide abstract]
ABSTRACT: This paper addresses some aspects of the analysis of cross-over trials with missing or incomplete data. A literature review on the topic reveals that many proposals provide correct results under the missing completely at random assumption while only some consider the more general missing at random situation. It is argued that mixed-effects models have a role in this context to recover some of the missing intra-subject from the inter-subject information, in particular when missingness is ignorable. Eventually, sensitivity analyses to deal with more general missingness mechanisms are presented.
Statistical Methods in Medical Research 02/2014;
• ##### Article: A Bayesian network for modelling blood glucose concentration and exercise in type 1 diabetes.
[hide abstract]
ABSTRACT: This article presents a new statistical approach to analysing the effects of everyday physical activity on blood glucose concentration in people with type 1 diabetes. A physiologically based model of blood glucose dynamics is developed to cope with frequently sampled data on food, insulin and habitual physical activity; the model is then converted to a Bayesian network to account for measurement error and variability in the physiological processes. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods for simultaneous estimation of all model parameters and prediction of blood glucose concentration. Although there are problems with parameter identification in a minority of cases, most parameters can be estimated without bias. Predictive performance is unaffected by parameter misspecification and is insensitive to misleading prior distributions. This article highlights important practical and theoretical issues not previously addressed in the quest for an artificial pancreas as treatment for type 1 diabetes. The proposed methods represent a new paradigm for analysis of deterministic mathematical models of blood glucose concentration.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Sample size considerations in active-control non-inferiority trials with binary data based on the odds ratio.
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ABSTRACT: This paper presents an approximate closed form sample size formula for determining non-inferiority in active-control trials with binary data. We use the odds-ratio as the measure of the relative treatment effect, derive the sample size formula based on the score test and compare it with a second, well-known formula based on the Wald test. Both closed form formulae are compared with simulations based on the likelihood ratio test. Within the range of parameter values investigated, the score test closed form formula is reasonably accurate when non-inferiority margins are based on odds-ratios of about 0.5 or above and when the magnitude of the odds ratio under the alternative hypothesis lies between about 1 and 2.5. The accuracy generally decreases as the odds ratio under the alternative hypothesis moves upwards from 1. As the non-inferiority margin odds ratio decreases from 0.5, the score test closed form formula increasingly overestimates the sample size irrespective of the magnitude of the odds ratio under the alternative hypothesis. The Wald test closed form formula is also reasonably accurate in the cases where the score test closed form formula works well. Outside these scenarios, the Wald test closed form formula can either underestimate or overestimate the sample size, depending on the magnitude of the non-inferiority margin odds ratio and the odds ratio under the alternative hypothesis. Although neither approximation is accurate for all cases, both approaches lead to satisfactory sample size calculation for non-inferiority trials with binary data where the odds ratio is the parameter of interest.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Various varying variances: The challenge of nuisance parameters to the practising biostatistician.
[hide abstract]
ABSTRACT: The 1997 Biometrics paper by Mike Kenward and James Roger has become a citation classic (more than 1260 citations by End June 2013 according to Google Scholar) and the solution that they proposed to deal with the problem of significance tests of fixed effects in REML models is now incorporated in many software packages and accepted by all biostatisticians as the method of choice. Nevertheless, it does not solve all problems, since there is more to analysis than just significance and since the problems that models with more than one variance pose arise in many contexts. In this paper, I discuss some problems and applications and make some tentative suggestions as to how they may be tackled. My excuse for raising problems I do not solve is that it may inspire James and Mike to complete what they started.
Statistical Methods in Medical Research 02/2014;
• ##### Article: Comments on number-needed-to-treat derived from ordinal scales.
Statistical Methods in Medical Research 02/2014; 23(1):107-10.
• ##### Article: Response to zimmermann and rahlfs.
Statistical Methods in Medical Research 02/2014; 23(1):111-3.
• ##### Article: Introduction to the special issue on joint modelling techniques.
[hide abstract]
ABSTRACT: Joint modelling techniques have seen great advances in the recent years, with several types of joint models having been developed in literature that can handle a wide range of applications. This special issue of Statistical Methods in Medical Research presents some recent developments from this field. This introductory article contains some background material and highlights the contents of the contributions.
Statistical Methods in Medical Research 02/2014; 23(1):3-10.
• ##### Article: Comparison of treatments in a cataract surgery with circular response.
[hide abstract]
ABSTRACT: Circular data are a natural outcome in many biomedical studies, e.g. some measurements in ophthalmologic studies, degrees of rotation of hand or waist, etc. With reference to a real data set on astigmatism induced in two types of cataract surgeries we carry out some two-sample testing problems with the possibility of common or different concentration parameters in the circular set up. Detailed simulation study and the analysis of the data set, including redesigning the cataract surgery data, are carried out.
Statistical Methods in Medical Research 01/2014;
• ##### Article: Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms.
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ABSTRACT: Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause-effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.
Statistical Methods in Medical Research 01/2014;
• ##### Article: The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study.
[hide abstract]
ABSTRACT: Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe how three different propensity score methods, propensity score matching, stratification on the propensity score and inverse probability of treatment weighting using the propensity score, can be used to estimate absolute measures of treatment effect on survival outcomes. These methods are all based on estimating marginal survival functions under treatment and lack of treatment. We then conducted an extensive series of Monte Carlo simulations to compare the relative performance of these methods for estimating the absolute effects of treatment on survival outcomes. We found that stratification on the propensity score resulted in the greatest bias. Caliper matching on the propensity score and a method based on earlier work by Cole and Hernán tended to have the best performance for estimating absolute effects of treatment on survival outcomes. When the prevalence of treatment was less extreme, then inverse probability of treatment weighting-based methods tended to perform better than matching-based methods.
Statistical Methods in Medical Research 01/2014;
• ##### Article: Graphical model-based O/E control chart for monitoring multiple outcomes from a multi-stage healthcare procedure.
[hide abstract]
ABSTRACT: Most statistical process control programmes in healthcare focus on surveillance of outcomes at the final stage of a procedure, such as mortality or failure rates. Such an approach ignores the multi-stage nature of these procedures, in which a patient progresses through several stages prior to the final stage. In this paper, we introduce a novel approach to statistical process control programmes in healthcare. Our proposed approach is based on the regression adjustment and multi-stage control charts that have been in use in industrial applications for decades. Three advantages of the approach are: better understanding of how outcomes at different stages relate to each other, explicit monitoring of upstream stage outcomes may help curtail trends that lead to poorer end-stage outcomes and understanding the impact of each stage can help determine the most effective allocation of quality improvement resources. A test statistic for the control charts is proposed. Simulations are performed to test the control charts, and the results are summarised using an empirical probability of true detection. An illustrative example using data from a maternity unit is included. A main result from the simulation study is that taking a multi-stage approach makes it easer to explicitly identify shifts in upstream stage outcomes that might otherwise be signalled in final stage outcomes if dependence between stages is ignored.
Statistical Methods in Medical Research 01/2014;

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