Glossary Terms for Critical Summaries

a b c d e f g h i j k l m n o p q r s t u v w x y z

a
 
a priori A priori comparisons are planned in advance of any data analysis. They are more reliable than post-hoc comparisons.
absolute risk reduction/increase The absolute arithmetic difference in rates of bad outcomes between experimental and control participants in a trial, calculated as the experimental event rate (EER) and the control event rate (CER), and accompanied by a 95% CI. Depending on circumstances it can be reduction in risk (death or cardiovascular outcomes, for instance, in trials of statins), or an increase (pain relied, for instance, in trials of analgesics).
adverse event An adverse outcome occurring during or after the use of a drug or other intervention but not necessarily caused by it.
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b
 
bias Systematic deviation of study results from the true results, because of the way(s) in which the study is conducted.
blinded The process used in epidemiological studies and clinical trials in which the participants, investigators and/or assessors remain ignorant concerning the treatments which participants are receiving.
blinding The process used in epidemiological studies and clinical trials in which the participants, investigators and/or assessors remain ignorant concerning the treatments which participants are receiving.
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c
 
case-control A study which involves identifying patients who have the outcome of interest (cases) and control patients who do not have that same outcome, and looking back to see if they had the exposure of interest. The exposure could be some environmental factor, a behavioural factor, or exposure to a drug or other therapeutic intervention.
CI Quantifies the uncertainty in measurement. It is usually reported as 95% CI, which is the range of values within which we can be 95% sure that the true value for the whole population lies. For example, for an NNT of 10 with a 95% CI of 5 and 15, we would have 95% confidence that the true NNT value was between 5 and 15.
cohort Involves identification of two groups (cohorts) of patients one which received the exposure of interest, and one which did not, and following these cohorts forward for the outcome of interest.
Confidence interval (CI) Quantifies the uncertainty in measurement. It is usually reported as 95% CI, which is the range of values within which we can be 95% sure that the true value for the whole population lies. For example, for an NNT of 10 with a 95% CI of 5 and 15, we would have 95% confidence that the true NNT value was between 5 and 15.
CONSORT CONSORT comprises a checklist and flow diagram to help improve the quality of reports of randomized controlled trials. It offers a standard way for researchers to report trials. The intent is to make the experimental process more clear, flawed or not, so that users of the data can more appropriately evaluate its validity for their purposes.
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d
 
degrees of freedom This is the number of independent comparisons that can be made between the members of a sample. It refers to the number of independent contributions to a sampling distribution (such as chi-square distribution). In a contingency table it is one less than the number of row categories multiplied by one less than the number of column categories; e.g. a 2 x 2 table comparing two groups for a dichotomous outcome, such as death, has one degree of freedom.
duplication Trials can be reported more than once, a process known as duplication. Duplication can be justified, for instance where results from a study at two years are followed later by results at four years. Another example might be reporting different results from a single trials (clinical or economic, for instance). But multiple publication can also be covert, and lead to over-estimation of the amount of information available.
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e
 
early detection Early detection of a disease is the goal of screening for it. Early detection can reduce mortality. Early detection, however, does not imply mortality reduction. For instance, if there is no effective therapy, then early detection, including treatment, will not reduce mortality.
effect size This is the standardised effect observed. By standardising the effect, the effect size becomes dimensionless (and that can be helpful when pooling data). The effect size then becomes: A generic term for the estimate of effect for a study. A dimensionless measure of effect that is typically used for continuous data when different scales (e.g. for measuring pain) are used to measure an outcome and is usually defined as the difference in means between the intervention and control groups divided by the standard deviation of the control or both groups. The effect size can be just the difference between the mean values of the two groups, divided by the standard deviation, as below, but there are other ways to calculate effect size in other circumstances. Effect size = (mean of experimental group - mean of control group)/standard deviation Generally, the larger the effect size, the greater is the impact of an intervention.
empirical Empirical results are based on experience (or observation) rather than on reasoning alone.
epidemiology The study of the distribution and determinants of health-related states or events in specified populations.
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f
 
false negative A test result in which the test is negative (for example, a pregnancy test finds no sign of pregnancy) but the event is actually there (the woman is pregnant) also called a 'miss'.
false positive A test result in which the test is positive (for example, a positive pregnancy test) but the event is not extant (the woman is not pregnant): also called a 'false alarm'. The trouble with false positives is that the more you test the more apparent disease you find.
fixed effect model This is a statistical model that stipulates that the units under analysis (people in a trial or study in a meta-analysis) are the ones of interest, and thus constitute the entire population of units. Only within-study variation is taken to influence the uncertainty of results (as reflected in the confidence interval) of a meta-analysis using a fixed effect model. Variation between the estimates of effect from each study (heterogeneity) does not effect the confidence interval in a fixed effect model.
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g
 
gold standard A method, procedure or measurement that is widely accepted as being the best available.
guideline A systematically developed statement designed to assist clinician and patient decisions about appropriate health care for specific clinical circumstances. Guidelines should be based on evidence, combined with local knowledge to ensure that they are appropriate for local conditions.
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h
 
heterogeneity In systematic reviews heterogeneity refers to variability or differences between studies in the estimates of effects. A distinction should made between "statistical heterogeneity" (differences in the reported effects), "methodological heterogeneity" (differences in study design) and "clinical heterogeneity" (differences between studies in key characteristics of the participants, interventions or outcome measures). Where there are large differences in clinical or methodological nature between studies, the simplest question to ask is whether there is any good reason for pooling data from these studies in a meta-analysis, where heterogeneity is known to exist. More difficult is the occurrence of statistical heterogeneity where there is methodological and clinical homogeneity. Statistical tests of heterogeneity are used to assess whether the observed variability in study results (effect sizes) is greater than that expected to occur by chance. These tests have low statistical power, and the boundary for statistical significance is usually set at 10%, or 0.1. Some people think that if these tests are used, then a value of 1%, or 0.01 makes more sense.
homogeneity In systematic reviews homogeneity refers to the degree to which the results of studies included in a review are similar. Clinical homogeneity means that, in trials included in a review, the participants, interventions and outcome measures are similar or comparable. Studies are considered statistically homogeneous if their results vary no more than might be expected by the play of chance, though most statistical tests would say that 10% of a perfectly homogeneous data set was heterogeneous, because that is the usual set-point for the tests.
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i
 
incidence The proportion of new cases of the target disorder in the population at risk during a specified time interval. It is usual to define the disorder, and the population, and the time, and report the incidence as a rate.
intention-to-treat A method of analysis for randomized trials in which all patients randomly assigned to one of the treatments are analysed together, regardless of whether or not they completed or received that treatment.
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l
 
longitudinal study A study of the same group of people at more than one point in time. (This type of study contrasts with a cross-sectional study, which observes a defined set of people at a single point in time.)
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m
 
meta-analysis A meta-analysis is where we pool all the information we have from a number of different (but similar) studies. Combining small, poor, trials, with few events will mislead.
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n
 
null hypothesis The statistical hypothesis that one variable (e.g. whether or not a study participant was allocated to receive an intervention) has no association with another variable or set of variables (e.g. whether or not a study participant died), or that two or more population distributions do not differ from one another. In simplest terms, the null hypothesis states that the results observed in a study are no different from what might have occurred as a result of the play of chance.
number needed to treat The inverse of the absolute risk reduction or increase and the number of patients that need to be treated for one to benefit compared with a control. The ideal NNT is 1, where everyone has improved with treatment and no-one has with control. The higher the NNT, the less effective is the treatment. But the value of an NNT is not just numeric. For instance, NNTs of 2-5 are indicative of effective therapies, like analgesics for acute pain. NNts of about 1 might be seen by treating sensitive bacterial infections with antibiotics, while an NNT of 40 or more might be useful, as when using aspirin after a heart attack.
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o
 
observational study In research about diseases or treatments, this refers to a study in which nature is allowed to take its course. Changes or differences in one characteristic (e.g. whether or not people received a specific treatment or intervention) are studied in relation to changes or differences in other(s) (e.g. whether or not they died), without the intervention of the investigator. There is a greater risk of selection bias than in experimental studies.
odds The ratio of the odds of having the target disorder in the experimental group relative to the odds in favour of having the target disorder in the control group (in cohort studies or systematic reviews) or the odds in favour of being exposed in subjects with the target disorder divided by the odds in favour of being exposed in control subjects (without the target disorder).
odds ratio The ratio of the odds of having the target disorder in the experimental group relative to the odds in favour of having the target disorder in the control group (in cohort studies or systematic reviews) or the odds in favour of being exposed in subjects with the target disorder divided by the odds in favour of being exposed in control subjects (without the target disorder).
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p
 
prevalence This is a measure of the proportion of people in a population who have a disease at a point in time, or over some period of time.
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r
 
RCT A group of patients is randomised into an experimental group and a control group. These groups are followed up for the variables/outcomes of interest. The point about using randomisation is that it avoids any possibility of selection bias in a trial. The test that randomisation has been successful is that different treatment groups have same characteristics at baseline. For instance, there should be the same number of men and women, or older or younger people, or different degrees of disease severity.
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s
 
sensitivity Proportion of people with the target disorder who have a positive test. It is used to assist in assessing and selecting a diagnostic test/sign/symptom.
sensitivity-analysis An analysis used to determine how sensitive the results of a study or systematic review are to changes in how it was done. Sensitivity analyses are used to assess how robust the results are to uncertain decisions or assumptions about the data and the methods that were used.
specificity Proportion of people without the target disorder who have a negative test. It is used to assist in assessing and selecting a diagnostic test/sign/symptom.
systematic review A summary of the medical literature that uses explicit methods to perform a thorough literature search and critical appraisal of individual studies and that uses appropriate statistical techniques to combine these valid studies.
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t
 
trial A research study conducted with patients which tests out a drug or other intervention to assess its effectiveness and safety. Each trial is designed to answer scientific questions and to find better ways to treat individuals with a specific disease. This general term encompasses controlled clinical trials and randomized controlled trials.
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v
 
validity This term is a difficult concept in clinical trials, bute refers to a trial being able to measure what it sets out to measure. A trial that set out to measure the analgesic effect of a procedure might be in trouble if patients had no pain.
variable A measurement that can vary within a study, e.g. the age of participants. Variability is present when differences can be seen between different people or within the same person over time, with respect to any characteristic or feature that can be assessed or measured.
variance A measure of the variation shown by a set of observations, defined by the sum of the squares of deviations from the mean, divided by the number of degrees of freedom in the set of observations.
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