Scientific Writing. how to complement results with tables ? Part-5. What does complement mean?. TABLES THAT,TELL WHAT HAPPENED. Tables that tell what happened can quickly become filled with superfluous detail.
how to complement
results with tables ?Part-5
Tables that tell what happened can quickly become filled with superfluous detail.
For example, if you present the number of fatal myocardial infarctions and strokes, and the total number of deaths due to cardiovascular disease, you need not present the number of other cardiovascular deaths.
Again, the percentages should all refer to the same denominator (all deaths).
If you want to indicate what proportion of cardiovascular deaths was due to strokes, do so in text: "Stroke was responsible for 28% of the deaths due to cardiovascular diseases"(Table 6.15).
There is another way to present the same data, this time emphasizing the proportions of death due to each cause, rather than the absolute risk of each cause (Table 6.16). In this table, it is somewhat easier to see that cardiovascular diseases caused about half of the deaths, and cancer about one-third.
When you are presenting comparisons of two or more groups, you are presenting two types of information:
the measurements themselves in each of the groups, and
the differences between the groups.
You must decide which of those two types of information is more important because that will influence how you organize the table.
Consider emphasizing the Table 6.17, which compares the characteristics of patients with asthma and those with chronic obstructive pulmonary disease in a study of the effects of intensive vacuuming of the subjects' carpets.
Here the emphasis is on the characteristics themselves, rather than on the differences between the two types of patients. Everyone already knows that the two types of patients are very different. A simple indication that there were statistically significant differences is all that is needed.
When the emphasizing the emphasis is on the comparison-as in a randomized trial comparing a drug and a placebo, or in a study to determine the predictors of response to vacuuming-more is required.
The reader will not only want to know if there is a statistically significant difference, but the size of that difference. In this case, the table must include a measure of that effect size, and an estimate of how precisely the effect size was measured (its confidence interval; Table 6.18).
Comparing emphasizing the an intervention group and a control group works fine for a randomized trial, but problems arise when one applies the same idea to a case-control or cohort study to compare cases who developed the outcome with those who did not (Table 6.19).
Table 6.19 emphasizing the may look great, but it is terribly misleading. The reader will have to work hard to realize that the table does not indicate that 33% ofthe subjects with a history of diabetesdeveloped strokes during the study; rather, 33%ofthose with strokeshad diabetes.
It emphasizing the is even harder to grasp the relation between stroke and the number of previous myocardial infarctions. The table fails to present what the reader wants to know: namely ,whether the number of previous myocardial infarctions affects the incidence of stroke. Table 6.20 does a better job.
Putting the emphasizing the numbers in parentheses keeps the reader from being distracted from the purpose of the table, which is to demonstrate the incidence of stroke according to selected characteristics.
The emphasizing the text that accompanies the table might simply read, "There were similar numbers of men and women in the study (Table 6.11);33% of the subjects were over 65years of age, and 25% were more than 10 kg above ideal body weight. Most were free of chronic medical problems."
The emphasizing the extremely high rate of stroke in subjects with two or more prior myocardial infarctions can be disclosed in the text: "The risk of stroke was 56% (20/36) among those subjects with two or more previous myocardial infarctions."
Tables that are intended to show more complicated effects-such as the effects of smoking in men and in women-should use column subheaders for these subgroup or "nested" comparisons.
The nested effects-such as the effects of smoking in men and in women-should use comparison should be the one that is more important, because the data in the table will be displayed side by side, and thus will be easier to compare. Table 6.21, for example, emphasizes the differences between smokers and nonsmokers: Smokers weigh less, and have higher hemoglobin levels and leukocyte counts. This is true for both men and women.
Reversing effects-such as the effects of smoking in men and in women-should use the nesting process yields a table with the same information, but in a much less accessible format. While Table 6.22 shows that men are heavier-and have greater hemoglobin concentrations-than women, the effects of smoking are obscured.
Neither effects-such as the effects of smoking in men and in women-should use Table 6.21 nor Table 6.22 highlights another important result: that the effects of smoking status on body weight are greater in women (smokers weigh 11 kg less than nonsmokers) than in men (a difference of 4 kg). In this case, because this is the only sex-specific difference, it will suffice to mention it in text.
Neither Table 6.21 nor effects-such as the effects of smoking in men and in women-should use Table 6.22 highlights another important result: that the effects of smoking status on body weight are greater in women (smokers weigh 11 kg less than nonsmokers) than in men (a difference of 4 kg). In this case, because this is the only sex-specific difference, it will suffice to mention it in text.
If there are effects-such as the effects of smoking in men and in women-should use several such differences, then two more columns should be added to the table, and labeled as the difference between smokers and nonsmokers for both men and women.
But if these differences, or lack thereof are very important-for example, if your research question addresses them-then a separate table of the sex-specific differences, with confidence intervals, should be included (Table 6.23).
In some circumstances, there may not be a "control" group, so there is no "control column." Instead, a final column of averages should be included. Similarly, a final row of averages may also be useful (Table 6.24).
Table 6.24 is challenging, but with a little effort, one can determine what percentage of fourth-year students were women (46%), what percentage elected each of the possible training choices (14% chose pediatrics), and the differences by type of training program. The table does not tell you the percentage of women who chose psychiatry training programs; if that information was of primary interest, then the rows and columns should have been reversed.
Look at Table 6.31, which compares pediatric patients less than 2 years old under- going surgery for congenital abnormalities at two university hospitals (Group I) and three community hospitals (Group II). Now, ask yourself, how can this table be made easier to follow and more informative?
Informative without text: group Vs given name
Topic or point
the independent variable(s) (X),
the dependent variable(s) (Y),
the animal or population, the material described, or both (Z).
Dependent and independent variables
Similar to the text
the kind of data
the number of patients studied
the observation points
the statistical significance level, and
the statistics used.
Assess the title and the arrangement of the table below.
Also compare the table with the relevant results (paras. 2 and 3 of Results).
Then revise the table to make the point clearer.
The question this paper asks is, “Do peritoneal dialysis and hemodialysis have similar effects on plasma cholesterol metabolism in patients with end-stage renal disease?”
The answer is “no.”
The concentrations of plasma total and free cholesterol and the phospholipid content were significantly lower in the hemodialysis patients than in the peritoneal dialysis patients or the control group (Table I). These lower values were partly reflected by the lower concentrations of high-density lipoprotein (HDL) and the lower HDL cholesterol in the hemodialysis patients.
Consistent with the lower HDL concentrations, the major HDL apolipoprotein, apo A-I, was much lower in the hemodialysis patients than in the control group, whereas the value for the peritoneal dialysis patients was intermediate (Table II). Apo A-II concentrations were very similar in all three groups. Apo B and apo E were in the normal range in both groups of patients. Apo D was slightly higher in the two groups of patients than in the controls.
The ratio of high-density lipoprotein and low-density lipoprotein (expressed here as the ratio between their major apolipoproteins, apo A-I and apo B, respectively) was significantly lower in the hemodialysis patients than in the controls (Table II). Values were intermediate in the peritoneal dialysis patients.
The original table is generally clear, but it can be made clearer.
Title and Column Headings.
In the revision, to make the title complete, the independent variables (peritoneal dialysis and hemodialysis) have been added and the control subjects have been omitted.
As a result, the key terms in the title correlate with the key terms in the first column on the left (peritoneal dialysis, hemodialysis).
Title and Column Headings.
In addition, the column heading “Plasma Apoprotein” has been added, correlating with that term in the title, and the unit of measurement (mg/dl) is included after this general heading rather than being stated after each individual apoprotein.
Instead of a title in the form “Effects of X on Y in Z,” the title could be in the form “Y after X in Z,” and the point (“Greater Changes”) could be included:
Plasma Apoproteins After Peritoneal Dialysis or Hemodialysis in Patients Who Have End-Stage Renal Disease
Changes in Plasma Apoproteins After Peritoneal Dialysis or Hemodialysis in Patients Who Have End-Stage Renal Disease
Greater Changes in Plasma Apoproteins After Hemodialysis than After Peritoneal Dialysis in Patients Who Have End-Stage Renal Disease.
Relation to the Text.
To make the table show the decreases in apo A-I and in apo A-I/apo B described in the text, the control values have been moved to the first row (as is conventional), peritoneal dialysis values are in the middle (“intermediate”), and the hemodialysis values are last (“much lower”).
In addition, the patients are described fully in the title, as in the question (“patients who have end-stage renal disease”).
Showing Significant Differences.
To show statistically significant differences, symbols (*, †) have been placed after the values that are different, and footnotes have been added to state the P values and what numbers are being compared.
If a table is too large:
delete unnecessary columns (for example, a column of p values) and rows;
avoid repetition of information;
keep titles, headings, and subheadings brief;
use abbreviations (and explain them in the footnotes); and
consider splitting one excessively large table into two smaller tables
reorienting the table
Consider combining tables that have the same or similar column headings.
Not only does this save space, but juxtaposing data often suggests new ways of looking at your results. See, for example, Tables 6.28 and 6.29.
Combining the two tables (Table 6.30) makes it clear that the side effects of treatment are of a magnitude and severity commensurate (proportionate) with its benefits, an observation that was less obvious when there were two tables.
Combining tables of benefits and side effects would not make sense, however, if the side effects of a therapy, such as nausea or skin rash, were not commensurate with its benefits, such as reduced cancer deaths.
Thank you Hemodialysis in Patients Who Have End-Stage Renal Disease