·
**Null hypothesis**: There is no difference between the two groups
being analyzed

·
**P-value**: The probability of obtaining the observed
result—or one more extreme—if the null hypothesis were true. A small P-value
would make is reject the null hypothesis.

·
**Confidence interval (CI)**: Quantifies the uncertainty in measurement. It is
usually reported as a 95% CI, which is the range of values within which we can
be 95% confident that the true value for the whole population lies.

·
**Power:** The probability of detecting a statistically
significant difference between the control and experimental groups. Power = 1 –
β

·
**Type 1 Statistical error (α):** The probability of rejecting the null hypothesis,
when it is actually true.

·
**Type 2 Statistical error (β):** The probability of accepting the null hypothesis,
when it is actually false.

·
**Confounder:**
A third variable, associated with both the exposure and the outcome, does not
mediate their relationship, and should be adjusted for when analyzing
exposure-outcome relationship (example: smoking for the coffee
drinking—cardiovascular risk association). It must be considered in small RCTs.
We can never rule out some residual confounding in observational studies
(case-control, longitudinal cohort).

·
**Effect modifier:** A third variable, which is NOT associated with either the exposure or
the outcome, but changes significantly the exposure-outcome relationship
(example: smoking for the contraceptive—thrombosis association). Effect
modification is also known as interaction.

·
**Intention to treat analysis (or “analyze as
randomized”)**: We analyze outcomes based on the initial randomized
group assignment, regardless of whether participants received the corresponding
intervention. This maintains the random treatment assignment given at the outset
of the study, and thus preserves the study from bias.

·
**Relative Risk**:
The ratio of risks between an experimental and control group, in cohort and RCT
studies.

·
**Absolute Risk Reduction**: Absolute difference between the exposed
(experimental group) vs. the unexposed group (control group); used to estimate
the Number Needed to Treat.

·
**Relative Risk Reduction**: The
proportion of the baseline risk reduced by the intervention (Absolute risk
reduction/absolute risk in control population)

·
**NNT (number needed to treat): 1/ARR**.
The number of patients needed to treat to avoid one bad outcome (or to prevent
one event).

·
**NNH (number needed to harm): 1/ARI**
(where ARI is the absolute risk increase) The number of patients needed to
expose to cause one additional bad outcome.

·
**Sensitivity**: The probability of a positive test in people
with disease

·
**Specificity**: The probability of a negative test in people
without disease

·
**Positive Predictive Value: **Proportion
of people with a positive test who truly have the disease

·
**Negative Predictive Value**: Proportion of
people with a negative test who truly do not have the disease

·
**Likelihood ratio of a positive test**: Probability
of an abnormal result in the population with the disease**, **divided over** **the probability
of that result in the population without the disease. LR (+) of 6 means that this
test result is 6 times more likely to occur in a patient with the disease than
in a patient without the disease. LR(+) = Sensitivity/(1-Specificity)

·
**Likelihood ratio of a negative test**: The
probability of a normal result in the population without the disease**, **divided over** **the probability of that result in the population with the disease.
LR(-) = (1- Sensitivity)/Specificity

·
**Censoring**: In
RCTs, censoring means that follow up length is not the same for all surviving
subjects, either because of losses to follow-up, or because the study ended and
those randomized last were observed for a shorter time. Survival methods
(Kaplan Meier curves, log-rank tests, Cox models, etc.) take censoring into
consideration when comparing two groups.

·
**Kaplan-Meier Curve: **(survival curve) It usually plots the cumulative probability of
survival in each study arm, starting from 100%, although it may plot cumulative
risk.

·
**Cox proportional Hazards Model:** Analyzes time-to-event data, comparing hazard
functions between arms. Hazard = risk per unit of time. It applies the
principle of censoring, and allows adjustment for potential confounders.

·
**Case-Control Study**: Examines associations between
exposure and outcome by sampling subjects with the outcome of interest,
matching them against subjects without the outcome, and then comparing the
exposure in each. Most important concern is bias, such as recall bias.

·
**Longitudinal Cohort study**: Measures potential exposures at baseline, and then
follows up subjects to determine the occurrence of outcomes by exposure group.
Non-randomized design may cause bias, including confounding by indication.

**Randomized Controlled Study (RCT) Checklist **

·
Who were the
study participants? • Was randomization
adequate?

·
Was it blinded? • How many were lost to follow-up? • How
many crossed over? • Were all patients who entered the trial accounted for at
its conclusion? • Was it analyzed on an
intention to treat basis?

·
What was the
number needed to treat? • What were the
adverse effects?

·
Do the results
apply to your patients?

**Negative RCT Checklist **

·
Did they recruit
the number of patients they intended to?

·
Was the event
rate in the control group as high as expected?

·
Were losses to
follow up and cross-overs as infrequent as expected?

·
Was there
intervention fidelity?

ŕIf the answer is “NO” to any of the above questions, the possibility of
lower than expected power should be considered. But remember: if the treatment
were efficacious, there should be at least a trend towards improvement in the
intervention arm.

**Non-Inferiority RCT **

·
**Placebo-controlled design is not ethical** because a treatment is available. The new treatment is expected to have similar
efficacy, and have better safety, convenience, tolerability, acceptability, or
cost.

·
The threshold for
non-inferiority significance (null hypothesis line) should be **clinically and ethically appropriate**. Monitoring
and pre-specified stopping rules are also essential.

·
The test
statistic is the same as for superiority analysis (e.g., Relative Risk), but we
examine the one-sided confidence interval (CI) in the direction of possible
harm.

·
The null
hypothesis line represents the amount of harm we are willing to accept included
in the one-sided CI. We reject the null hypothesis if the CI does not include
that line.

·
Losses to follow
up, cross-overs, and low intervention fidelity increase the probability of **type 1 error** (as opposed to superiority
RCTs, in which they increase the prob. of type 2 error). **Quality of care in control arm** should be state-of-the art. **As treated analysis** must be included.

**Meta-Analysis Checklist**

- Was there evidence of
**Publication Bias**? (funnel plot, regression tests). - Was the
**Quality**of the individual studies appropriate (with low risk of bias)? - Was there excessive
**Heterogeneity**for the outcome among selected studies? (I^{2}statistic) • How were the individual results pooled into a single estimate of effect?**Random Effects vs. Fixed Effect.** - What was the
**pooled effect estimate**? Was that estimate**consistent**in sensitivity and sub-group analyses?