ViCBiostat researchers publish a series of educational articles on modern biostatistical methods for clinicians

Led by Rory Wolfe, Professor of Biostatistics, School of Public Health and Preventive Medicine, several members of the ViCBiostat team have contributed to a series of articles that provide accessible expositions and reviews of a number of important modern biostatistical methods.

INVITED REVIEW SERIES

MODERN STATISTICAL METHODS IN RESPIRATORY MEDICINE

SERIES EDITORS: RORY WOLFE and MICHAEL ABRAMSON

Paper 1: Modern statistical methods in respiratory medicine

RORY WOLFE and MICHAEL J ABRAMSON

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

ABSTRACT

Statistics sits right at the heart of scientific endeavour in respiratory medicine and many other disciplines. In this introductory article, some key epidemiological concepts such as representativeness, random sampling, association and causation, and confounding are reviewed. A brief introduction to basic statistics covering topics such as frequentist methods, confidence intervals, hypothesis testing, P values and Type II error is provided. Subsequent articles in this series will cover some modern statistical methods including regression models, analysis of repeated measures, causal diagrams, propensity scores, multiple imputation, accounting for measurement error, survival analysis, risk prediction, latent class analysis and meta-analysis.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-1/issuetoc

Paper 2: Interpretation of commonly used statistical regression models

JESSICA KASZA1,2 and RORY WOLFE1,2  

Department of Epidemiology and Preventive Medicine, Monash University, and 2Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia

ABSTRACT

A review of some regression models commonly used in respiratory health applications is provided in this article.  Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-1/issuetoc

 
Paper 3: Models for the analysis of repeated continuous outcome measures in clinical trials

ALYSHA M. DE LIVERA1,2 SOPHIE ZALOUMIS1,2  and JULIE A. SIMPSON1,2

1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, and 2Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia

ABSTRACT

Repeated continuous outcome measures are common in clinical trials. In this tutorial style paper, using data collected from a trial evaluating an intervention for managing asthma and chronic obstructive pulmonary disease, we demonstrate ways of statistically analysing such data to answer frequently encountered clinical research questions. We illustrate the use of linear mixed effects modelling in doing so and discuss its advantages over several other commonly used approaches. The methods described in this paper can easily be carried out using standard statistical software.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-2/issuetoc


Paper 4: Introduction to multiple imputation for dealing with missing data

KATHERINE J. LEE1,2 and JULIE A. SIMPSON3

1Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, 2Department of Paediatrics, The University of Melbourne, 3Centre for Molecular, Environmental, Genetic & Analytic Epidemiology, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia

ABSTRACT

Missing data are common in both observational and experimental studies. Multiple imputation (MI) is a two-stage approach where missing values are imputed a number of times using a statistical model based on the available data and then inference is combined across the completed datasets. This approach is becoming increasingly popular for handling missing data. In this paper, we introduce the method of MI, as well as a discussion surrounding when MI can be a useful method for handling missing data and the drawbacks of this approach. We illustrate MI when exploring the association between current asthma status and forced expiratory volume in 1 s after adjustment for potential confounders using data from a population based longitudinal cohort study.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-2/issuetoc

Paper 5: Introduction to causal diagrams for confounder selection

ELIZABETH J. WILLIAMSON1,2,3 ZOE AITKEN4 JOCK LAWRIE3,5 SHYAMALI C. DHARMAGE2 JOHN A. BURGESS2 AND ANDREW B. FORBES1,3

1School of Public Health and Preventive Medicine, Monash University, 2Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3The Victorian Centre for Biostatistics (VICBiostat), 4Centre for Women’s Health, Gender and Society, Melbourne School of Population and Global Health, The University of Melbourne, and 5Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia

ABSTRACT

In respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis. Common statistical methods used to achieve this include multivariable regression models adjusting for selected confounding variables or stratification on those variables.  Therefore, a key question is which measured variables need to be controlled for in order to remove confounding. An approach to confounder-selection based on the use of causal diagrams (often called directed acyclic graphs) is discussed. A causal diagram is a visual representation of the causal relationships believed to exist between the variables of interest, including the exposure, outcome and potential confounding variables. After creating a causal diagram for the research question, an intuitive and easy-to-use set of rules can be applied, based on a foundation of rigorous mathematics, to decide which measured variables must be controlled for in the statistical analysis in order to remove confounding, to the extent that is possible using the available data. This approach is illustrated by constructing a causal diagram for the research question: ‘Does personal smoking affect the risk of subsequent asthma?’. Using data taken from the Tasmanian Longitudinal Health Study, the statistical analysis suggested by the causal diagram approach was performed.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-3/issuetoc

 Paper 6: Survival analysis of time-to-event data in respiratory health research studies

JESSICA KASZA,1,2 DARREN WRAITH,2,3 KAREN LAMB2,4,5 AND RORY WOLFE1,2

1Department of Epidemiology and Preventive Medicine, Monash University, 2Victorian Centre for Biostatistics (ViCBiostat), 3Centre for Epidemiology and Biostatistics, School of Population and Global Health, University of Melbourne, 4Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, Royal Children’s Hospital, and 5Department of Paediatrics, University of Melbourne, Royal Children’s Hospital, Melbourne, Victoria, Australia

ABSTRACT

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies.Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

http://onlinelibrary.wiley.com/doi/10.1111/resp.12281/abstract

Paper 7: Introduction to propensity scores

ELIZABETH J. WILLIAMSON1,2,3,4 and ANDREW FORBES1,3

1School of Public Health & Preventive Medicine, Monash University, and 2Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 3The Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia, and 4Farr Institute of Health Informatics Research, London, UK

ABSTRACT

Although randomization provides a gold-standard method of assessing causal relationships, it is not always possible to randomly allocate exposures. Where exposures are not randomized, estimating exposure effects is complicated by confounding. The traditional approach to dealing with confounding is to adjust for measured confounding variables within a regression model for the outcome variable. An alternative approach—propensity scoring—instead fits a regression model to the exposure variable. For a binary exposure, the propensity score is the probability of being exposed, given the measured confounders. These scores can be estimated from the data, for example by fitting a logistic regression model for the exposure including the confounders as explanatory variables and obtaining the estimated propensity scores from the predicted exposure probabilities from this model. These estimated propensity scores can then be used in various ways—matching, stratification, covariate-adjustment or inverse-probability weighting—to obtain estimates of the exposure effect. In this paper, we provide an introduction to propensity score methodology and review its use within respiratory health research.We illustrate propensity score methods by investigating the research question: ‘Does personal smoking affect the risk of subsequent asthma?’ using data taken from the Tasmanian Longitudinal Health Study.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-5/issuetoc


Paper 8: Correcting for the bias caused by exposure measurement error in epidemiological studies

MICHAEL T. FAHEY,1,2 ANDREW B. FORBES1,2 AND ALISON M. HODGE3

1School of Public Health and Preventive Medicine, Monash University, 2The Victorian Centre for Biostatistics (ViCBiostat) and 3Cancer Council Victoria, Melbourne, Victoria, Australia

ABSTRACT

An important goal of many epidemiological studies is to estimate the magnitude of association between an exposure and an outcome. Exposure measurement error causes bias in such estimates of association and can be substantial. In this article, we describe the problem of exposure measurement error and its effects.  We show how a simple hand calculation, in conjunction with validation study data and a calibration equation, can be used to correct estimates for the bias caused by exposure measurement error. Correcting estimates of association for measurement error helps researchers appropriately assess effect size.

http://onlinelibrary.wiley.com/doi/10.1111/resp.12356/abstract

 Paper 9: Classifying patients by their characteristics and clinical presentations; the use of latent class analysis

DARREN WRAITH and RORY WOLFE

Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia

ABSTRACT

In this article, we introduce the general statistical analysis approach known as latent class analysis and discuss some of the issues associated with this type of analysis in practice.  Two recent examples from the respiratory health literature are used to highlight types of research questions that have been addressed using this approach.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2014.19.issue-8/issuetoc


Paper 10: Statistical models for respiratory disease diagnosis and prognosis

RORY WOLFE and JOHN CARLIN

Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia

ABSTRACT

Risk prediction equations are used in a variety of healthcare settings to provide prognosis for patients with various respiratory conditions. This article provides a review of statistical methods for the development, evaluation and implementation of respiratory disease prediction models. We also consider a second, closely related application of these methods: the creation of equations that describe normal lung function in a particular population and the use of such equations in the diagnosis of abnormal lung function. The methods are illustrated with examples of models that have been developed for use in respiratory medicine and research.

http://onlinelibrary.wiley.com/doi/10.1111/resp.2015.20.issue-4/issuetoc


Paper 11: Introduction to systematic reviews and meta-analysis

JOANNE E. McKENZIE,1 Elaine M. BELLER2 AND Andrew B. FORBES1,3

Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia

ABSTRACT

Systematic reviews provide a method for collating and synthesizing research, and are used to inform healthcare decision making by clinicians, consumers and policy makers. A core component of many systematic reviews is a meta-analysis, which is a statistical synthesis of results across studies. In this review article, we introduce meta-analysis, focusing on the different meta-analysismodels, their interpretation, how a model should be selected and discuss potential threats to the validity of meta-analyses. We illustrate the application of meta-analysis using data from a review examining the effects of early use of inhaled corticosteroids in the emergency department treatment of acute asthma.

http://onlinelibrary.wiley.com/doi/10.1111/resp.12783/abstract


SERIES EDITORIAL—EPILOGUE
MODERN STATISTICAL METHODS IN
RESPIRATORY MEDICINE

SERIES EDITORS: RORY WOLFE AND MICHAEL ABRAMSON

Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia

The series of articles on modern statistical methods that has been published in Respirology has attempted to introduce respiratory clinicians, scientists and researchers to a range of important statistical methods that have risen to prominence in recent years. The series is by no means an exhaustive list of such methods; for example, a recent textbook on biostatistics suitable for health researchers interested in quantitative methods includes a similar range of methods, with further inclusion of bootstrapping and Bayesian methods.

http://onlinelibrary.wiley.com/doi/10.1111/resp.12785/abstract