Are the results valid?

1. Can the results be applied to my patients?

2. Were all clinically relevant outcomes considered?

3. Are the benefits worth the harms and costs?

REMEMBER:  Evidence makes an important contribution to good clinical decision making but evidence,
though necessary, is not always sufficient. A clinician must also deal with a patient's values and preferences.

In addition to clinical expertise, a good clinician must demonstrate compassion, sensitive listening skills,
and broad perspectives from the humanities and social sciences. These attributes allow for an understanding
of patients' illnesses in the context of their experience, personalities, and cultures.
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Absolute risk: The observed or calculated probability of an event in the population under study.

Adjustment: A summarizing procedure for a statistical measure in which the effects of differences in composition of the populations being
compared have been minimized by statistical methods.

Association: Statistical dependence between two or more events, characteristics, or other variables. An association may be fortuitous or may
be produced by various other circumstances; the presence of an association does not necessarily imply a causal relationship.

Bias (Syn: systematic error): Deviation of results or inferences from the truth, or processes leading to such deviation. See also Referral Bias,Selection Bias.

Blind(ed) study (Syn: masked study): A study in which observer(s) and/or subjects are kept ignorant of the group to which the subjects are assigned, as
in an experimental study, or of the population from which the subjects come, as in a nonexperimental or observational study. Where both observer and
subjects are kept ignorant, the study is termed a double-blind study. If the statistical analysis is also done in ignorance of the group to which subjects
belong, the study is sometimes described as triple blind. The purpose of "blinding" is to eliminate sources of bias.

Case-series: Report of a number of cases of disease.

Case-control study: Retrospective comparison of exposures of persons with disease (cases) with those of persons without the disease (controls)
(see Retrospective study).

Causality: The relating of causes to the effects they produce. Most of epidemiology concerns causality and several types of causes can be distinguished.
It must be emphasized, however, that epidemiological evidence by itself is insufficient to establish causality, although it can provide powerful circumstantial evidence.

Co-interventions: Interventions other than the treatment under study that are applied differently to the treatment and control groups. Cointervention is a
serious problem when double blinding is absent or when the use of very effective non-study treatments is permitted.

Cohort study: Follow-up of exposed and non-exposed defined groups, with a comparison of disease rates during the time covered.

Comparison group: Any group to which the index group is compared. Usually synonymous with control group. 

Co-morbidity: Coexistence of a disease or diseases in a study participant in addition to the index condition that is the subject of study.

Confidence interval (CI): The range of numerical values in which we can be confident (to a computed probability, such as 90 or 95%) that the population
value being estimated will be found. Confidence intervals indicate the strength of evidence; where confidence intervals are wide, they indicate less precise
estimates of effect. The larger the trial's sample size, the larger the number of outcome events and the greater becomes the confidence that the true relative
risk reduction is close to the value stated. Thus the confidence intervals narrow and "precision" is increased. In a "positive finding" study the lower boundary
of the confidence interval, or lower confidence limit, should still remain important or clinically significant if the results are to be accepted. In a "negative finding"
study, the upper boundary of the confidence interval should not be clinically significant if you are to confidently accept this result.

Confounding variable, Confounder: A variable that can cause or prevent the outcome of interest, is not an intermediate variable, and is associated with the
factor under investigation. A confounding variable may be due chance or bias. Unless it is possible to adjust for confounding variables, their effects cannot be
distinguished from those of factor(s) being studied.

Control event rate(CER): The percentage of the control/nonexposed group who experienced outcome in question.  

Dose-response relationship: A relationship in which change in amount, intensity, or duration of exposure is associated with a change-either an increase or
decrease-in risk of a specified outcome.
 

Determinant: Any definable factor that effects a change in a health condition or other characteristic.
 

Effectiveness: a measure of the benefit resulting from an intervention for a given health problem under usual conditions of clinical care for a particular group;
this form of evaluation considers both the efficacy of an intervention and its acceptance by those to whom it is offered, answering the question, "Does the
practice do more good than harm to people to whom it is offered?" See Intention to treat.
 

Efficacy: a measure of the benefit resulting from an intervention for a given health problem under the ideal conditions of an investigation; it answers the question,
"Does the practice do more good than harm to people who fully comply with the recommendations?"

Exclusion Criteria: Conditions which preclude entrance of candidates into an investigation even if they meet the inclusion criteria.

Experimental event rate(EER): The percentage of intervention/exposed group who experienced outcome in question.  

Follow-up: Observation over a period of time of an individual, group, or initially defined population whose relevant characteristics have been assessed in order
to observe changes in health status or health-related variables.

Gold standard: A method, procedure, or measurement that is widely accepted as being the best available.

Incidence: The number of new cases of illness commencing, or of persons falling ill, during a specified time period in a given population. See also Prevalence.

Intention to treat analysis: A method for data analysis in a randomized clinical trial in which individual outcomes are analyzed according to the group to which
they have been randomized, even if they never received the treatment they were assigned. By simulating practical experience it provides a better measure of
effectiveness (versus efficacy).

Interviewer bias: Systematic error due to interviewer's subconscious or conscious gathering of selective data.

Lead-time bias: If prognosis study patients are not all enrolled at similar, well-defined points in the course of their disease, differences in outcome over time may
merely reflect differences in duration of illness.

Likelihood ratio: Ratio of the probability that a given diagnostic test result will be expected for a patient with the target disorder rather than for a patient without the disorder.

Number Needed to Treat (NNT): the number of patients who must be exposed to an intervention before the clinical outcome of interest occurred; for example, the
number of patients needed to treat to prevent one adverse outcome.  

Odds: a proportion in which the numerator contains the number of times an event occurs and the denominator includes the number of times the event does not occur.

Odds Ratio (Syn: cross-product ratio, relative odds): a measure of the degree of association; for example, the odds of exposure among the cases compared with the
odds of exposure among the controls.  

Precision: The range in which the best estimates of a true value approximate the true value. See Confidence interval.

Predictive value: In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., does have the disease), or that a person
with a negative test truly does not have the disease. The predictive value of a screening test is determined by the sensitivity and specificity of the test, and by the
prevalence of the condition for which the test is used.
 

Prevalence: the proportion of persons with a particular disease within a given population at a given time.

Prognosis: the possible outcomes of a disease or condition and the likelihood that each one will occur.

Prognostic factor: Demographic, disease-specific, or co-morbid characteristics associated strongly enough with a condition's outcomes to predict accurately the
eventual development of those outcomes. Compare with risk factors. Neither prognostic or risk factors necessarily imply a cause and effect relationship.

Prospective study: Study design where one or more groups (cohorts) of individuals who have not yet had the outcome event in question are monitored for the
number of such events which occur over time. 

Randomized controlled trial: Study design where treatments, interventions, or enrollment into different study groups are assigned by random allocation rather
than by conscious decisions of clinicians or patients. If the sample size is large enough, this study design avoids problems of bias and confounding variables by
assuring that both known and unknown determinants of outcome are evenly distributed between treatment and control groups. 

Recall bias: Systematic error due to the differences in accuracy or completeness of recall to memory of past events or experiences. 

Referral filter bias: The sequence of referrals that may lead patients from primary to tertiary centres raises the proportion of more severe or unusual cases,
thus increasing the likelihood of adverse or unfavorable outcomes.

Relative risk (RR): the ratio of the probability of developing, in a specified period of time, an outcome among those receiving the treatment of interest or
exposed to a risk factor, compared with the probability of developing the outcome if the risk factor or intervention is not present.   

Relative risk reduction (RRR): the extent to which a treatment reduces a risk, in comparison with patients not receiving the treatment of interest. 

Reliability (Repeatability, Reproducibility): the results of a test or measure are identical or closely similar each time it is conducted.

Retrospective study: study design in which cases where individuals who had an outcome event in question are collected and analyzed after the outcomes
have occurred (see also Case-control study).

Risk factor: patient characteristics or factors associated with an increased probability of developing a condition or disease in the first place. Compare with
prognostic factors. Neither risk or prognostic factors necessarily imply a cause and effect relationship. 

Selection Bias: a bias in assignment or a confounding variable that arises from study design rather than by chance. These can occur when the study
and control groups are chosen so that they differ from each other by one or more factors that may affect the outcome of the study. 

Sensitivity (of a diagnostic test): the proportion of truly diseased persons, as measured by the gold standard, who are identified as diseased by the test under study.  

Specificity (of a diagnostic test): the proportion of truly nondiseased persons, as measured by the gold standard, who are so identified by the diagnostic test under study.  

Stratification: division into groups. Stratification may also refer to a process to control for differences in confounding variables, by making separate estimates for
groups of individuals who have the same values for the confounding variable.

Strength of Inference: the likelihood that an observed difference between groups within a study represents a real difference rather than mere chance or
the influence of confounding factors, based on both p values and confidence intervals. Strength of inference is weakened by various forms of bias and by small sample sizes.

Survival curve: A graph of the number of events occurring over time or the chance of being free of these events over time. The events must be discrete and the
 time at which they occur must be precisely known. In most clinical situations, the chance of an outcome changes with time. In most survival curves the earlier
follow-up periods usually include results from more patients than the later periods and are therefore more precise.

Validity: the extent to which a variable or intervention measures what it is supposed to measure or accomplishes what it is supposed to accomplish.