How to Display Data- P20

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How to Display Data- P20:The best method to convey a message from a piece of research in health is via a fi gure. The best advice that a statistician can give a researcher is to fi rst plot the data. Despite this, conventional statistics textbooks give only brief details on how to draw fi gures and display data.

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Reporting study results 87 6 Simpson AG. A comparison of the ability of cranial ultrasound, neonatal neuro- logical assessment and observation of spontaneous movements to predict out- come in preterm infants University of Shefﬁeld; 2004. 7 Diggle PJ, Heagerty P, Liang K-J, Zeger SL. Analysis of longitudinal data, 2nd ed. Oxford: Oxford University Press; 2002. 8 Matthews JNS, Altman DG, Campbell MJ, Royston P. Analysis of serial measure- ments in medical research. British Medical Journal 1990;300:230–5. 9 Moher D, Schulz KF, Altman DG, for the CONSORT Group. The CONSORT statement: revised recommendations for improving the quality of reports of par- allel group randomised trials. Lancet 2001;357:1191–4. 10 Altman DG. Practical statistics for medical research. London: Chapman & Hall; 1991. 11 Kapoor AS, Kanji H, Buckingham J, Devereaux PJ, McAlister FA. Strength of evi- dence for perioperative use of statins to reduce cardiovascular risk: systematic review of controlled studies. British Medical Journal 2006;333:1149–55. 12 Deeks JJ, Everitt B. Forest plot. In: Everitt B, Palmer C, editors. The encyclopaedic companion to medical statistics. London: Arnold; 2005. 13 Deeks JJ. Funnel plots. In: Everitt B, Palmer C, editors. The encyclopaedic compan- ion to medical statistics. London: Arnold; 2005. 14 Egger M, Davey Smith G, Schnieder M, Minder C. Bias in meta-analysis detected by a simple graphical method. British Medical Journal 1997;315:629–34.
88 How to Display Data Appendix Table A7.1 CONSORT checklist of items to include when reporting a randomised trial9 Item No. Descriptor Title and abstract 1 How patients were allocated to interventions. Introduction Background 2 Scientiﬁc background and explanation of rationale. Methods Participants 3 Eligibility criteria for participants and the settings and locations where the data were collected. Interventions 4 Precise details of the interventions intended for each group and how and when they were actually administered. Objectives 5 Speciﬁc objectives and hypotheses. Outcomes 6 Clearly deﬁned primary and secondary outcome measures and, when applicable, any methods used to enhance the quality of measurements (e.g. multiple observations, training of assessors). Sample size 7 How sample size was determined and, when applicable, explanation of any interim analyses and stopping rules. Randomisation Sequence 8 Method used to generate the random allocation generation sequence, including details of any restriction (e.g. blocking, stratiﬁcation). Allocation 9 Method used to implement the random allocation concealment sequence (e.g. numbered containers or central telephone), clarifying whether the sequence was concealed until interventions were assigned. Implementation 10 Who generated the allocation sequence, who enrolled participants and who assigned participants to their groups. Blinding (masking) 11 Whether or not participants, those administering the interventions, and those assessing the outcomes were blinded to group assignment. When relevant, how the success of blinding was evaluated. Statistical methods 12 Statistical methods used to compare groups for primary outcome(s). Methods for additional analyses, such as subgroup analyses and adjusted analyses. (Continued)
Reporting study results 89 Table A7.1 (Continued.) Item No. Descriptor Results Participant ﬂow 13 Flow of participants through each stage (a diagram is strongly recommended). Speciﬁcally, for each group report the numbers of participants randomly assigned, receiving intended treatment, completing the study protocol and analysed for the primary outcome. Describe protocol deviations from study as planned, together with reasons. Recruitment 14 Dates deﬁning the periods of recruitment and follow-up. Baseline data 15 Baseline demographic and clinical characteristics of each group. Numbers analysed 16 Number of participants (denominator) in each group included in each analysis and whether the analysis was by ‘intention-to-treat’. State the results in absolute numbers when feasible (e.g. 10/20, not 50%). Outcomes and 17 For each primary and secondary outcome, a estimation summary of results for each group, and the estimated effect size and its precision (e.g. 95% conﬁdence interval). Ancillary analyses 18 Address multiplicity by reporting any other analyses performed, including subgroup analyses and adjusted analyses, indicating those pre-speciﬁed and those exploratory. Adverse events 19 Address multiplicity by reporting any other analyses performed, including subgroup analyses and adjusted analyses, indicating those pre-speciﬁed and those exploratory. Discussion Interpretation 20 Interpretation of results, taking into account study hypotheses, sources of potential bias or imprecision and the dangers associated with multiplicity of analyses and outcomes. Generalisability 21 Generalisability (external validity) of the trial ﬁndings. Overall evidence 22 General interpretation of the results in the context of current evidence.
Chapter 8 Time series plots and survival curves 8.1 Introduction This chapter outlines good practice when displaying data that are ordered in time. These data can arise either as a result of the monitoring of a particular event or events across a population over time (time series) or following up individuals over time to measure their time to a particular event (survival analysis). This chapter is not concerned with repeated measures outcome data as they have already been dealt with in Chapter 7. 8.2 Time series plots A time series is a series of observations ordered in time. It differs from the repeated measures data discussed in the previous chapter in two ways: 1 Usually there is only one replication of the data, for example one subject’s heart rate monitored over time, or the annual death rates of one country over time. With repeated measures we have more than one subject under consideration. 2 There are many time points. Typically in patient monitoring thousands of points are sampled. An example of a time series plot is given in Figure 8.1. The data are the number of infant deaths per day in England and Wales over a 7-week period during 1979.1 The important points to consider when drawing a time series are that time should be on the X-axis (horizontal) and the series of events that are being monitored, the observations, are on the Y-axis (vertical). In addition, adjacent points should be joined by straight lines. If the origin has been omitted this should be made clear, as here, by two diagonal lines on the axis line. Care should be taken when examining published time series plots. They are often used in newspapers and a common trick is not to show the origin, so that a small trend can appear magniﬁed. This is discussed in more detail in Section 2.3. 90
Time series plots and survival curves 91 45 40 35 Infant deaths (n) 30 25 20 15 0 10 20 30 40 50 Time (days) Figure 8.1 Daily infant deaths in England and Wales over a 7-week period during 1979.1 8.3 Lowess smoothing plots Lowess smoothing plots are a useful way of displaying some time series data.2 They are described in more detail in Section 5.3, where they are applied to continuous data. For time series they are useful for investigating non-linear trends, as demonstrated here. Figure 8.2a shows the number of prescriptions for non-selective serotonin reuptake inhibitors (SSRIs), a type of antidepressant, over a 3.5-year period, from 2002 to 2006 for one general practice in Yorkshire, England (Senior J., Personal Communication, 2006). The scatter plot seems to show a generally increasing trend, with more scatter towards the end. However, ﬁtting a lowess smoothing curve with bandwidth of 50% suggests that in fact the number of prescriptions peaked at around month 30 (Figure 8.2b). This corresponds to the time when national guide- lines were published by NICE recommending that SSRIs should be prescribed in preference to non-SSRIs for the treatment of depression. The peak is sug- gested by the data, and so lowess plots are useful for data exploration, but not for testing hypotheses. Note that as the Y-axis does not begin at the origin (value 0) this has been indicated by two parallel lines.