DES115 Dentistry Lab Session Plan Interpreting Biomedical Data - Week 10-11 PDF

Laboratory Session Plan Course: Program: Principles of Cell Biology and Genetics Week 11 10 - 11 DES115 Dentistry Ad. Assoc. Prof. Kousparou C. Module: Interpreting biomedical data from plots and graphs Year: 1 Dr Charous C....

Laboratory Session Plan Course: Program: Principles of Cell Biology and Genetics Week 11 10 - 11 DES115 Dentistry Ad. Assoc. Prof. Kousparou C. Module: Interpreting biomedical data from plots and graphs Year: 1 Dr Charous C. Semester: 1 Mrs Tomouzou C. Lab Session Activities This laboratory session demonstrates how data representations and figures are likely to convey an impression consistent with valid trial conclusions. Also, which aspects of figures may, without careful interpretation, be misleading. Lab Session Location Laboratories N34, N23 N41, N42 Lab Session Duration 2hrs x 5 GROUPS A B C D E Team- Time 120 min Dr Harous C. in each laboratory Structure Dr Tomouzou C. 2 hrs x 8 GROUPS Lab In place ABCDEFGH Rotations in each laboratory I. Learning Objectives This laboratory session demonstrates how data representations and figures are likely to convey an impression consistent with valid trial conclusions. Also, which aspects of figures may, without careful interpretation, be misleading. II. Laboratory Session Description Activity Learning about important interpretations which are necessary when reviewing clinical data. Description Activity Duration 120 min (in minutes) Objective Learn about important interpretations which are necessary when reviewing clinical data. Instructions Follow the instructors taking you through the different types of graphs. INTRODUCTION How to interpret figures in reports of clinical trials The graphical display of data is among the most powerful tools available for communicating medical research findings, given the increasing complexity of study designs and the mind’s preference for information conveyed in pictorial format. However, although general information is available on what constitutes an effective data display and what constitutes good practice in reporting trials, there is relatively little guidance on using figures to aid the presentation of trial results. Because figures are so effective in creating an enduring impression of results, their construction—and interpretation by the readers—must be handled with care. A survey which was run to determine the types of figures used most commonly in reports of clinical trials and to uncover the good, and not so good, practices that typically attend their use, showed that the four most common types of figure were flow diagrams, Kaplan-Meier plots, forest plots, and repeated measures plots. Flow diagrams Flow diagrams are integral to the CONSORT guidelines for the reporting of clinical trials. They display the flow of participants through the stages of the trial in a way that should be easy to follow. The figure below depicts a successful example of a flow diagram portraying a clear picture of the trial’s design and conduct. It includes the numbers of people screened and reasons for exclusion, information that many trials fail to collect and report. The numbers not receiving randomized treatment and numbers lost to follow-up are key limitations that every study should document. Kaplan-Meier plots The Kaplan-Meier plot is for time to event or survival data, when interest is focused on the risk of a particular event (such as death or myocardial infarction) as participants move through time. Because the aim of many treatments or interventions is to try to reduce the occurrence of a particular event, this type of plot is used commonly in reporting clinical trials. However, it is an aspect of statistics not well understood by doctors. The plot is drawn with time in the study on the horizontal axis and either the cumulative proportion with the event, or the proportion for whom the event has not yet occurred (the survival probability), plotted on the vertical. Curves are drawn for each treatment group, and the separation between the curves indicates potential differences in the treatments’ effectiveness. The Kaplan-Meier estimates change only when events occur, so that each plot is a series of steps. Note how few participants were followed to five years. The figure below shows the essential features of a clear Kaplan Meier plot. The treatment groups are visually differentiable, with an appropriate vertical scale and axes clearly labelled. Below the horizontal axis, the numbers of participants remaining at risk (that is, those who remain under observation and for whom the event is yet to occur) are displayed. A formal statistical comparison (in this case a hazard ratio with 95% confidence interval and P value from the log rank test) is needed to assess whether the distance between the curves is sufficient to depict a real difference in risk between treatment and control arms. This information is often best included on the figure itself. In this case the slight difference is not significant. The following figure shows a plot going down (plotting the proportion of participants who are event free), covering the whole scale from probability 1 to 0. Much of the graph is empty space because the event (defaulting from treatment) has low incidence. For outcomes of this type, it is more useful to present the cumulative probability curve going up, with the vertical axis truncated at a reasonable maximum. It can be helpful if Kaplan-Meier plots take account of statistical uncertainty by displaying standard error bars (or confidence intervals) at a few key follow-up times13 to help restrain readers from overinterpreting any apparent differences between the curves. LEFT: Kaplan-Meier curve from trial of percutaneous coronary intervention (PCI) for persistent occlusion after myocardial infarction. The primary end point was death from any cause, non-fatal reinfarction, or heart failure requiring hospital admission. RIGHT: Kaplan-Meier plot from trial of strategies to improve adherence to tuberculosis treatment Forest plots A forest plot displays estimated treatment effects across various patient subgroups. Typically, a forest plot presents an overall effect (for all randomized participants) and then various subgroup computations (for instance, by sex) on a common axis. Each point plotted represents a comparison between treatment and control participants in the relevant subgroup and is accompanied by its 95% CI. The figure below shows a simple example of a forest plot, with only one set of subgroup analyses. This figure has several features consistent with good practice. It shows the overall estimate and confidence intervals (combining all subgroups) and the labels indicate which direction favours treatment or control. Subgroup estimates are displayed underneath the overall estimate. Although the lines suggest that patients with a baseline albumin concentration below 25 g/l may benefit from albumin treatment, inclusion of the heterogeneity test (sometimes called interaction test) makes it clear that the evidence is not strong enough to be conclusive. Such interaction tests are key to interpretation of forest plots and should be included on the plot or in the legend. Forest plot from study comparing resuscitation with albumin or saline in intensive care showing unadjusted odds ratio of death stratified by baseline albumin concentration Repeated measures plots For trials with a quantitative outcome measured at baseline and two or more follow-up times, it is common to plot the means by treatment over time. The figure below shows this approach in a clear style for three outcome scores each recorded at baseline and five follow-up times. The figure uses different symbols for each treatment to help distinguish them and joining the means by lines helps the eye to follow the trends over time. As with the forest plots, it is important to express the statistical uncertainty in each mean; this is done here using confidence intervals. To enhance clarity, the authors have helpfully staggered group means at each time to ensure that intervals do not obscure each other. The figure also includes a global P value corresponding to a test of overall differences in outcomes between study arms. This avoids the undesirable use of repeated significance tests at every time point and the consequent problem of inflated type I error due to multiple testing. Means scores over time for SF-36 bodily pain and physical function scales and Oswestry disability index from a study of surgical versus non-operative treatment for lumbar disc herniation. Bibliography 1. Tufte ER. The visual display of quantitative information. Cheshire, CT: Graphics Press, 1983. 2. 2 Cleveland WS. The elements of graphing data. Summit, NJ: Hobart Press, 1994. 3. 3 Gelman A, Pasarica C, Dodhia R. Let’s practice what we preach: turning tables into graphs. Am Statistician 2002;56:121-30. 4. 4 Schriger DL, Cooper RJ. Achieving graphical excellence: suggestions and methods for creating high-quality visual displays of experimental data. Ann Emerg Med 2001;37:75-87. 5. 5 Puhan MA, Riet G, Eichler K, Steurer J, Bachmann LM. More medical journals should inform their contributors about three key principles of graph construction. J Clin Epidemiol 2006;59:1017-22. 6. 6 Lang T, Secic M. Visual displays of data and statistics. In: How to report statistics in medicine. 2nd ed. Philadelphia: American College of Physicians, 2006:349-92.