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Thread: [Biostatistics] Tables

  1. #1
    PharmD Year 1 TomHsiung's Avatar
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    Default [Biostatistics] Tables

    Frequency tables - nominal data

    [Biostatistics] Tables-screen-shot-2015-12-10-at-6-37-45-pm-png

    The label at the top of the first (left-hand) column indicates the variable being described in the table. The remainder of the first column is a list of the categories for this variable. The second (right-hand) column is the frequency column. Frequency is another word for "count" and lists, in this example, the number of babies in each category, that is, males and females.

    Relative/Percentage frequency table

    Often of more use than the actual number of individuals in each category are the percentages. Tables with this information are called relative or percentage frequency tables. The third column of Figure 2.3 shows the percentage of children in each hair color category.

    [Biostatistics] Tables-screen-shot-2015-12-10-at-6-45-12-pm-png
    Last edited by TomHsiung; Thu 10th December '15 at 6:45pm.
    B.S. Pharm, West China School of Pharmacy, Class of 2007, Health System Pharmacist, RPh. Hematology, Infectious Disease.

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    PharmD Year 1 TomHsiung's Avatar
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    Frequency tables - ordinary data

    When the data in question are ordinal, we can allocate them into ordered categories. As an example, 475 psychiatric in-patients were questioned about their level of satisfaction with their psychiatric nursing care. "Level of satisfaction" is clearly an ordinal variable. "Satisfaction" cannot be properly measured, and has no units, but the categories can be meaningfully ordered, as they have been ordered here. The resulting data is shown in Figure 2.5.

    [Biostatistics] Tables-screen-shot-2015-12-10-at-6-53-37-pm-png
    B.S. Pharm, West China School of Pharmacy, Class of 2007, Health System Pharmacist, RPh. Hematology, Infectious Disease.

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    PharmD Year 1 TomHsiung's Avatar
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    Frequency tables - discrete metric data

    Discrete metric data result from counting. This means that the number of possible values is limited; the number of cells in the human body may be very large, but it is not infinite. Parity, for example, is a discrete metric variable and is counted as 0, 1, 2, 3 and so on.

    If our question is, "How many women in the sample had a parity of 0?" or "How many a parity of 1?", we can very easily answer these questions, and similar questions, if we arrange these data into a frequency table, which is shown in Figure 2.9.

    [Biostatistics] Tables-screen-shot-2015-12-10-at-6-59-23-pm-png

    Cumulative frequency

    Suppose that we want to know what was the percentage of lesions among the patients receiving biolimus-eluding stent that required fewer than three stents? A question like this is more easily answered if we add a percentage cumulative frequency column to the respective frequency table. The procedure, using data from before, is as follows:

    Step 1. Calculate the cumulative frequencies by adding up successively the values in the frequency column: 1805 + 553 = 2358, 2358 + 168 = 2526, and so on.

    Step 2. Calculate the percentage cumulative frequencies by dividing each cumulative frequency value by the total (2638) and then multiplying by 100.

    The results are shown in Figure 2.11. The answer to the question, "What was the percentage of patients receiving the biolimus-eluting stent that had lesions that required fewer than three stents?", is thus 89.38 per cent. We can also easily calculate how many patients had lesions that required three or more stents as 100 - 89.38 = 10.62%.

    [Biostatistics] Tables-screen-shot-2015-12-10-at-7-13-01-pm-png
    Last edited by TomHsiung; Thu 10th December '15 at 7:13pm.
    B.S. Pharm, West China School of Pharmacy, Class of 2007, Health System Pharmacist, RPh. Hematology, Infectious Disease.

  4. #4

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    Frequency tables - continuous metric data

    Constructing frequency tables for continuous metric data is often more of a problem than constructing with discrete metric data because the number of possible values which the data can take is infinite.

    Organizing raw metric continuous data into a frequency table is usually impractical because there are such a large number of possible values. Indeed, there are may well be no value that occurs more than once - particularly true if the values have decimal places. This means that the corresponding frequency table is likely to have a large, and thus unhelpful, number of rows. Not of much help in uncovering any pattern in the data therefore!

    The most useful approach with metric continuous data is to group them first and then construct a frequency distribution of the grouped data.

    The choice of the number of groups is arbitrary but you do not want too few groups or too many. Experience will help but as a very rough rule of thumb, no fewer than five groups and no more than 10. Of course, particular circumstances may cause these values to vary.

    [Biostatistics] Tables-screen-shot-2015-12-22-at-9-54-04-pm-png

    Frequency table with cumulative frequencies for continuous metric data

    [Biostatistics] Tables-screen-shot-2015-12-22-at-9-59-10-pm-png
    Last edited by CheneyHsiung; Tue 22nd December '15 at 10:00pm.
    Clinical Pharmacy Specialist - Hematology

  5. #5

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    Cross-tabulation - contingency tables

    Each of the frequency tables in prior posts provides us with a description of the frequency distribution of single variable. Sometimes, however, you will want to examine the association between two variables, within a single group of individuals. You can do this by putting the data into a contingency table, also called a table of cross-tabulations.

    In these tables, the rows represent the categories of one variable, usually an "outcome" of some sort, and the columns represent the groups within a second variable.

    To illustrate this idea, look at Figure 2.16. This is a contingency table of the cross-tabulation of the variable "smoked while pregnant" (Yes or No), against three categories of the variable "birthweight": <2500 g, 2500 g - 3999 g, and >=4000 g, for a random sample of 500 newborn babies. Here, the outcome (the rows) is birthweight, and the groups (the columns) are the mothers who smoked while pregnant, and those who didn't. This table would be called 2 X 2 table because there are two rows and two columns, although tables with more rows and columns are not unusual.

    [Biostatistics] Tables-screen-shot-2015-12-22-at-10-22-33-pm-png
    Clinical Pharmacy Specialist - Hematology

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