Today, the BMJ published our paper on the association between population prevalence of e-cigarette use in England and quit attempts, their success, and the use of behavioural and pharmacological support (see my science blog and http://www.bmj.com/content/bmj/354/bmj.i4645.full.pdf).
This study used a methodology called time-series analysis. The aim of this blog is to provide a brief introduction to this type of analysis for those who are unfamiliar.
Time-series data generally occur when measurements of a variable(s) are taken over a period of time, most often at regular intervals (e.g., month, year or quarter). An example is the data used in the BMJ article which was collected on a monthly basis from 2006 to 2015. Such data allow us to assess interventions and associations between behaviours in a quasi-experimental manner. This is important for a number of reasons. First, RCTs (randomised controlled trials) can take many years to run, are expensive and often require a large amount of resources. Secondly, it is important to determine 'effectiveness' as well as 'efficacy' i.e. to assess use and impact in the real world. Thirdly, it is often unethical to assign people to certain conditions (such as smoking versus non-smoking) and so behaviours must be studied in an ecological manner. Fourthly, it is often of interest to assess associations while adjusting for the impact of population level policies and interventions.
Most research does not use time-series data. This might be due to its lack of availability, the fact that you need a large number of data points (some have suggested 50-100 months worth) or perhaps as the analysis is complex.
Statistical methods used to analyse time-series data needs to take into account underlying trends, seasonality (for example, smoking is often higher in summer months and drinking in winter months), and the internal structure of the data. The latter of which involves things such as the presence of autocorrelation. In very simple terms, autocorrelation occurs when measurements taken closer in time tend to be more similar. For example, if I were to ask you your weight today and tomorrow and in a years time, it is likely that the measurements taken today and tomorrow will be more similar. This is obvious, weight fluctuates in many people over a 12 month period.
We have statistical methods which can take these things into account. The one used in the BMJ study was called autoregressive integrated moving average modelling with exogenous variables (ARIMAX). The autoregessive and moving average components relate to the internal structure of the data (i.e. the autocorrelation discussed above), integrated refers to the part of the model taking into account the underlying trends (it does this by removing them), while exogenous indicates that we are including another variable to predict our outcome variable and both are time-series data (for example, we will use e-cigarette use to predict quit attempts).
In the near future I hope to be publishing a methodological paper on this. I will keep you posted.
This study used a methodology called time-series analysis. The aim of this blog is to provide a brief introduction to this type of analysis for those who are unfamiliar.
Time-series data generally occur when measurements of a variable(s) are taken over a period of time, most often at regular intervals (e.g., month, year or quarter). An example is the data used in the BMJ article which was collected on a monthly basis from 2006 to 2015. Such data allow us to assess interventions and associations between behaviours in a quasi-experimental manner. This is important for a number of reasons. First, RCTs (randomised controlled trials) can take many years to run, are expensive and often require a large amount of resources. Secondly, it is important to determine 'effectiveness' as well as 'efficacy' i.e. to assess use and impact in the real world. Thirdly, it is often unethical to assign people to certain conditions (such as smoking versus non-smoking) and so behaviours must be studied in an ecological manner. Fourthly, it is often of interest to assess associations while adjusting for the impact of population level policies and interventions.
Most research does not use time-series data. This might be due to its lack of availability, the fact that you need a large number of data points (some have suggested 50-100 months worth) or perhaps as the analysis is complex.
Statistical methods used to analyse time-series data needs to take into account underlying trends, seasonality (for example, smoking is often higher in summer months and drinking in winter months), and the internal structure of the data. The latter of which involves things such as the presence of autocorrelation. In very simple terms, autocorrelation occurs when measurements taken closer in time tend to be more similar. For example, if I were to ask you your weight today and tomorrow and in a years time, it is likely that the measurements taken today and tomorrow will be more similar. This is obvious, weight fluctuates in many people over a 12 month period.
We have statistical methods which can take these things into account. The one used in the BMJ study was called autoregressive integrated moving average modelling with exogenous variables (ARIMAX). The autoregessive and moving average components relate to the internal structure of the data (i.e. the autocorrelation discussed above), integrated refers to the part of the model taking into account the underlying trends (it does this by removing them), while exogenous indicates that we are including another variable to predict our outcome variable and both are time-series data (for example, we will use e-cigarette use to predict quit attempts).
In the near future I hope to be publishing a methodological paper on this. I will keep you posted.
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