Pharmacoepidemiology & Drug Safety (2006), 15, 477-484.

John A. Mandryk1, Judith M. Mackson1, Fiona E. Horn1, Sonia E. Wutzke1, Caro-Anne Badcock2, Rob J. Hyndman3 and Lynn M. Weekes1

  1. National Prescribing Service Ltd, Surry Hills, Sydney, Australia.
  2. Covance Pty Ltd, Covance Biostatistics Asia-Pacific, North Ryde, Sydney, Australia.
  3. Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia.


Purpose: The National Prescribing Service Ltd (NPS) aims to improve prescribing and use of medicines consistent with evidence-based best practice. This study compares two statistical methods used to determine whether multiple educational interventions influenced antibiotic prescribing in Australia.

Methods Monthly data (July 1996 to June 2003) were obtained from a national administrative claims database. The outcome measures were the median number of antibiotic prescriptions per 1,000 consultations for each general practitioner (GP) each month, and the mean proportion (across GPs) of each subgroup of antibiotics (e.g. roxithromycin) out of the nine antibiotics having primary use for upper respiratory tract infection. Two methods were used to investigate shifts in prescribing: augmented regression, which included seasonality, autocorrelation and one intervention; and seasonally adjusted piecewise linear dynamic regression, which removed seasonality prior to modelling, included several interventions, a term for GP participation in NPS activities, and autocorrelated errors.

Results Both methods described a similar decrease in rates, with a non-significant increase after the first intervention in April 1999 – the inclusion of more interventions and the GP participation term made no difference. Using roxithromycin as an example of the analyses of proportions, both methods implied that after April 1999 the proportion significantly decreased. The statistical significance of this intervention disappears when all interventions and the GP term are included.

Conclusions The two analyses provide similar conclusions but raise questions about what is the best way to model drug utilization data, particularly regarding whether to include additional intervention terms when they are part of an extended roll-out of related interventions.

Keywords: claims data; longitudinal study; antibiotics; time series; seasonality; regression methods; evaluation.

Online article


1 2 3 5
March 7th, 2017

Coherent Probabilistic Forecasts for Hierarchical Time Series

February 28th, 2017

Forecasting with temporal hierarchies

February 14th, 2017

The Australian Macro Database: An online resource for macroeconomic research in Australia

February 14th, 2017

Macroeconomic forecasting for Australia using a large number of predictors

January 13th, 2017

Visualising forecasting algorithm performance using time series instance spaces

January 1st, 2017

Associations between outdoor fungal spores and childhood and adolescent asthma hospitalisations

January 1st, 2017

Grouped functional time series forecasting: an application to age-specific mortality rates

December 7th, 2016

Exploring the influence of short-term temperature patterns on temperature-related mortality: a case-study of Melbourne, Australia

October 13th, 2016

Reconciling forecasts: the hts and thief packages

September 20th, 2016

smoothAPC package for R

September 20th, 2016

stR package for R

September 15th, 2016

Forecasting large collections of related time series

August 22nd, 2016

thief package for R

June 21st, 2016

Exploring time series collections used for forecast evaluation

May 25th, 2016

ISCRR time series workshop

May 6th, 2016

Automatic foRecasting using R

February 29th, 2016

On sampling methods for costly multi-objective black-box optimization

February 19th, 2016

Dynamic Algorithm Selection for Pareto Optimal Set Approximation

February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

January 30th, 2016

Bayesian rank selection in multivariate regressions

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

January 1st, 2016

Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

January 1st, 2016

Fast computation of reconciled forecasts for hierarchical and grouped time series

December 31st, 2015

Measuring forecast accuracy

November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

November 2nd, 2015

Forecasting big time series data using R

October 7th, 2015

Optimal forecast reconciliation for big time series data

October 5th, 2015

Google workshop: Forecasting and visualizing big time series data

September 16th, 2015


August 25th, 2015

New IJF editors

August 17th, 2015

Machine learning bootcamp

August 7th, 2015

Statistical issues with using herbarium data for the estimation of invasion lag-phases

June 30th, 2015

Exploring the feature space of large collections of time series

June 26th, 2015

Exploring the boundaries of predictability: what can we forecast, and when should we give up?

June 25th, 2015

Automatic algorithms for time series forecasting

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

June 19th, 2015

Probabilistic forecasting of peak electricity demand

June 10th, 2015

Do human rhinovirus infections and food allergy modify grass pollen–induced asthma hospital admissions in children?

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

June 1st, 2015

Large-scale unusual time series detection

May 26th, 2015

Visualization of big time series data

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

April 20th, 2015

A note on the validity of cross-validation for evaluating time series prediction

April 4th, 2015

Discussion of “High-dimensional autocovariance matrices and optimal linear prediction”

April 1st, 2015

Change to the IJF editors

February 23rd, 2015

Visualization and forecasting of big time series data

January 12th, 2015

Visualizing and forecasting big time series data

December 24th, 2014

Bivariate data with ridges: two-dimensional smoothing of mortality rates