Training a New Generation of Biostatisticians: A Successful Consortium Model

Figures & data

Figure 1. Frequency plot of source of biostatistics positions in Australia by year

Table 1: Required units of study for each course. Each unit or subject is delivered over one semester and is of length equivalent to a quarter of a full-time study load.

BCA Code	Unit of study	Masters	Post-graduate Diploma	Post-graduate Certificate
Introductory
EPI	Epidemiology*	✓	✓	✓
	Mathematical Background for
MBB	Biostatistics	✓	✓
PDT	Probability and Distribution Theory	✓	✓
HIS	Health Indicators & Health Surveys
	Data Management & Statistical
DMC	Computing	✓	✓
Intermediate
PSI	Principles of Statistical Inference	✓	✓
CLB	Clinical Biostatistics
DES	Design of Randomized Controlled Trials	✓	✓
LMR	Linear Models	✓	✓
	Categorical Data & Generalized Linear
CDA	Models	✓	✓
SVA	Survival Analysis	✓
Advanced
WPP	Workplace Project Portfolio (1 or 2 units)	✓
LCD	Longitudinal & Correlated Data
BAY	Bayesian Statistical Methods
BIF	Bioinformatics
ACT	Advanced Clinical Trials

✓ Unit is compulsory.

* This pre-existing unit is offered by most of the consortium universities as part of their Master of Public Health degrees.

Table

Introductory units
EPI	Epidemiology
Content:	Measures of frequency and association (e.g. relative risk, attributable risk); main types of study designs — cross-sectional surveys, case-control studies, cohort or follow-up studies, randomized controlled trials; sources of error (chance, bias, confounding); association and causality; evaluating published papers.
Textbook:	None
MBB	Mathematical Background for Biostatistics
Content:	Basic algebra and analysis; exponential functions; calculus; series, limits, approximations and expansions; linear algebra, matrices and determinants; and numerical methods.
Textbook:	Anton H, Bivens I, Davis S. Calculus: early transcendentals version, 8th edition. Wiley, 2005
PDT	Probability and Distribution Theory
Content:	Probability, random variables, discrete and continuous distributions, use of calculus to obtain parameters such as mean and variance. Joint distributions, independence, correlation, covariance, marginal and conditional distributions. Distributions of transformations of random variables. Sampling distributions and standard error of an estimator of a parameter, key properties of estimators, large sample results for properties of estimators with emphasis on role of Normal distribution. Numerical simulation and graphing with Stata is used to demonstrate concepts.
Textbook:	Wackerly DD, Mendenhall W, Scheaffer RL. Mathematical Statistics with Applications, 7th edition, 2008, Duxbury Press, USA
HIS	Health Indicators & Health Surveys
Content:	Routinely collected health-related data; quantitative methods in demography, including standardization and life tables; health differentials; design and analysis of population health surveys including the roles of stratification, clustering and weighting.
Textbook:	Scheaffer RL, Mendenhall W, Ott RL. Elementary Survey Sampling. 6th edition. Wadsworth 2006
DMC	Data Management & Statistical Computing
Content:	Data management principles and concepts are developed using relational database software (Microsoft Access). Data manipulation, descriptive analyses and interpretation are introduced using SAS and Stata statistical software. Students acquire skills in data display, summary presentation and pattern recognition using these tools.
Textbooks:	Cody R, Smith J. Applied Statistics & the SAS Programming Language. 5th edition. Prentice Hall 2005
	Hills M & De Stavola B. A Short Introduction to Stata for Biostatistics. Timberlake 2007
Intermediate units
PSI	Principles of Statistical Inference
Content:	Key concepts of estimation, and construction of Normal-theory confidence intervals; frequentist theory of estimation including hypothesis tests; methods of inference based on likelihood theory, including use of Fisher and observed information and likelihood ratio; Wald and score tests; introduction to Bayesian approach to inference and to distribution-free statistical methods.
Textbook:	Recommended — not compulsory:
	Azzalini, A. Statistical Inference: Based on the Likelihood. Chapman and Hall, London 1996.
	Clayton and Hills. Statistical Models in Epidemiology. Oxford University Press, Oxford, 1993.
CLB	Clinical Biostatistics
Content:	Clinical agreement (kappa statistics, Bland-Altman agreement method, intraclass correlation); diagnostic tests (sensitivity, specificity, predictive values, ROC curves, likelihood ratio); statistical process control (special and common causes of variation, Shewhart, CUSUM and EWMA charts); and systematic reviews (process, estimating treatment effect, assessing heterogeneity, publication bias).
Textbook:	None
DES	Design of Randomized Controlled Trials (RCTs)
Content:	Principles and methods of randomization in controlled trials; treatment allocation, blocking, stratification and allocation concealment; parallel, factorial and crossover designs, including n-of-1 studies; practical issues in sample size determination; intention-to-treat principle; phase I dose finding studies; phase II safety and efficacy studies; interim analyses and early stopping; multiple outcomes/endpoints, multiple tests and subgroup analyses, including adjustment of significance levels and P-values; reporting trial results and use of the CONSORT statement.
Textbook:	Piantadosi S. Clinical Trials a Methodological Perspective, 2nd edition. John Wiley & Sons 2005
LMR	Linear Models
Content:	Method of least squares; regression models and related statistical inference; flexible non-parametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of dummy variables, parameterization, interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects.
Textbook:	Recommended — not compulsory: Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied Linear Statistical Models. 5th edition. McGraw-Hill/Irwin 2005.
CDA	Categorical Data & Generalized Linear Models
Content:	Methods for contingency tables: odds ratios, relative risks, chi-squared tests for independence, Mantel-Haenszel methods for stratified tables, and methods for paired data. The exponential family of distributions; generalized linear models (GLMs), and parameter estimation for GLMs. Inference for GLMs — including the use of score, Wald and deviance statistics for confidence intervals and hypothesis tests, and residuals. Binary variables and logistic regression models — including methods for assessing model adequacy. Nominal and ordinal logistic regression for categorical response variables with more than two categories. Count data: Poisson regression.
Textbook:	None
SVA	Survival Analysis
Content:	Kaplan-Meier life tables; logrank test to compare two or more groups; Cox's proportional hazards regression model; checking the proportional hazards assumption; time-dependent covariates; multiple or recurrent events; sample size calculations for survival studies.
Textbook:	Hosmer D W, Lemeshow S, May S. Applied Survival Analysis: Regression modeling of time to event data. 2nd edition. Wiley Interscience 2008.
	Recommended — not compulsory: Cleves M, Gould W, Gutierrez R. An Introduction to Survival Analysis Using Stata, 2004. Stata Press - http://survey-design.com.au/
Advanced units
WPP	Workplace Project Portfolio (1 or 2 units)
Content:	The aim of this unit is that the student gains practical experience, usually in a workplace setting, in the application of knowledge and skills learnt during the coursework of the Masters program. The student provides evidence of having met this goal by presenting a portfolio, made up of a preface and project report(s), for examination by one examiner from the home university and one from another BCA university.
Textbook:	None
LCD	Longitudinal & Correlated Data
Content:	Paired data; effect of non-independence on comparisons within and between clusters of observations; methods for continuous outcomes: normal mixed effects (hierarchical or multilevel) models and generalized estimating equations (GEE); role and limitations of repeated measures ANOVA; methods for discrete data: GEE and generalized linear mixed models (GLMM); methods for count data.
Textbook:	Recommended — not compulsory:
	Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley and Sons, 2004.
BAY	Bayesian Statistical Methods
Content:	Simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of non-informative prior distributions; the relationship between Bayesian methods and standard “classical” approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; application of Bayesian methods for fitting hierarchical models to complex data structures.
Textbook:	Gelman, A., Carlin, JB, Stern, HS and Rubin, DB. Bayesian Data Analysis, 2nd edition. Chapman and Hall, 2003.
BIF	Bioinformatics
Content:	Biology basics; population genetics; web-based tools, data sources and data retrieval; analysis of single and multiple DNA or protein sequences; hidden Markov Models and their applications; evolutionary models; phylogenetic trees; analysis of microarrays; functional genomics; use of R.
Textbook:	Durbin R, Eddy S, Krogh A, Mitchison G. Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, 1998.
ACT	Advanced Clinical Trials
Content:	Methods in RCTs for determining: stopping rules for interim analyses (O'Brien-Fleming, Peto), spending functions, stochastic curtailment; statistical principles encountered in relation to aspects of regulatory guidelines (ICH, FDA, EMEA), and related to reports prepared for data safety and monitoring committees (DSMC); design and analysis of cross-over trials (period effects, interactions); equivalence and non-inferiority trials; problems of defining and using surrogate endpoints as alternatives to direct clinical outcomes
Textbook:	Recommended — not compulsory:
	Senn S. Cross-over trials in clinical research, 2nd edition. Wiley, 2002.
	Jennison C. and B.W. Turnbull. Group sequential methods with applications to clinical trials. Chapman & Hall, 1999.