Clinical Trials

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❘❙❚■ Chapter 17 | Types of Data and Normal DistributionWhat are survival data?In some medical research, the response variable indicates not only whether anevent occurs, but also the time it takes for an event to occur. This kind of datarequires a combination of a binary (event status) and a continuous variable (time).In the above example, investigators are interested in the factors that predictCAD-free time from the start of this study. In some instances, the event of interestis death (such as cardiovascular death), but it might be the end of a periodof remission from a disease, the relief of symptoms, or a further admissionto hospital. These types of data are generally referred to as ‘time-to-eventdata’ or most frequently ‘survival data’, even when the endpoint or the eventbeing studied is something other than death. The terms ‘survival analysis’ or‘time-to-event analysis’ encompass the methods and models that are applied tosurvival data.Presenting summaries of variablesIn medical reports, data are summarized for presentation by groups (such as theage-specific group for age distribution) using frequency distributions. Thesepresent the distribution of both qualitative and quantitative data, summarizinghow often each value of a variable is repeated. With quantitative data, we mostlypresent a grouped frequency distribution table from which we can appreciate:• the frequency (number of cases) occurring for each categoryor interval (eg, number of 70- to 74-year-old patients)• the relative frequency (percentage) of the total sample in eachcategory or interval (eg, 70- to 74-year-old patients comprised10% of the overall sample)• the highest and lowest or the range of possible values from ourpatient groups (eg, the oldest patient was aged over 95 yearsand the youngest patient was aged below 25 years)Although a frequency table provides a detailed summary of the distribution of thedata, the message from the data can be made more immediate by presenting thedistribution in a graph or a chart. The type of graph presented depends on thetype of data. Generally, for categorical data we prefer to use a bar chart or a piechart. For continuous data, a histogram or frequency polygon is more appropriate;this can either represent data from the entire treatment group of patients or fromsmaller subgroups of interest. This gives an immediate way of seeing broadsimilarities or differences between treatment groups. We can then use statisticaltests to ascertain whether any differences between the groups are significant.170
<strong>Clinical</strong> <strong>Trials</strong>: A Practical Guide ■❚❙❘Figure 1. Pie chart and bar chart for percentage distribution by gender and New York Heart Association(NYHA) class.MaleFemalePercentage (%)10080604020Percentage distribution by gender0I II III IVNYHA classFigure 2. Histogram for systolic blood pressure (SBP) and polygon for heart rate.Histogram for SBPPolygon for heart rate1001008080Frequency6040Frequency60402020050 100 150 200 250020 60 100 140 180SBP (mm Hg)Heart rate (bpm)Box plots are another tool for conveying location and variation information forcontinuous data, particularly for detecting and illustrating location and variationchanges between different groups of data.Figure 1 shows a pie chart and a bar chart comparing the percentage distributionsby gender and New York Heart Association class, respectively, of patients withheart failure. In Figure 2, a histogram and a frequency polygon are displayed todescribe the SBP and heart rate, respectively.171
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❘❙❚■ ContributorsStephen L
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Clinical Trials: A Practical Guide
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■■❚❙❘Chapter 1RandomizedC
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■■❚❙❘Chapter 3Protocol De
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■■❚❙❘Part IIAlternativeTr
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■■❚❙❘Part IVSpecial Trial
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■■❚❙❘Chapter 22Intention-
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■■❚❙❘Chapter 23Subgroup A
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■■❚❙❘Chapter 24Regression
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■■❚❙❘Chapter 25Adjustment
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■■❚❙❘Chapter 32Overview o
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■■❚❙❘Chapter 33Trial Prof
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■■❚❙❘Chapter 34Presenting
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■■❚❙❘Chapter 35Use of Tab
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■■❚❙❘GlossaryGlossary453
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■■❚❙❘IndexIndex467
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