Primary Literature Evaluation - NAPLEX

Rejecting a true null hypothesis (false positive). A Type I error occurs when the null hypothesis is incorrectly rejected, leading to a false claim of a significant effect.

Intention-to-treat analysis. ITT maintains the balance of confounders achieved through randomization by including all randomized participants in their original groups.

Probability of results as or more extreme than observed. The $P$ value quantifies the probability of observing data at least as extreme as the actual results, assuming the null hypothesis is true.

$95%$ of such intervals would contain the true value. In frequentist statistics, a $95%$ CI means that if the experiment were repeated many times, $95%$ of the calculated intervals would include the true population parameter.

Not statistically significant at $\alpha=0.05$. Inclusion of 0 in the $95%$ CI for mean difference indicates the possibility of no true difference, failing significance at $0.05$.

Not statistically significant at $\alpha=0.05$. Inclusion of 1 in the $95%$ CI for RR suggests no significant difference between groups, as it encompasses no effect.

$NNH=20$. With ARI as $0.10 - 0.05 = 0.05$, NNH is the reciprocal, meaning 20 patients treated to cause one extra adverse event.

$NNH=\frac{1}{ARI}$. NNH indicates the number of patients exposed to treatment to cause one additional harm, computed as the inverse of ARI.

$ARI=\text{EER}-\text{CER}$. ARI quantifies the absolute increase in harmful event rate in the experimental group compared to control.

ITT analyzes as randomized; PP analyzes adherent completers. ITT preserves randomization by analyzing all participants in their assigned groups regardless of compliance, while PP focuses only on those who fully adhered to the protocol.

$RR=0.80$. RR is the ratio of event rates, indicating the experimental group has $80%$ of the control group's risk.

$ARR=0.06$. ARR is the difference in event rates, showing a $6%$ absolute reduction in risk due to the intervention.

$NNT=10$. With ARR calculated as $0.20 - 0.10 = 0.10$, NNT is the reciprocal, indicating 10 patients treated to prevent one event.

$NNT=\frac{1}{ARR}$. NNT represents the number of patients needing treatment to prevent one additional adverse outcome, based on the reciprocal of ARR.

$RRR=1-RR$. RRR expresses the proportional reduction in risk attributable to the treatment, derived from the relative risk.

$RR=\frac{\text{EER}}{\text{CER}}$. RR compares the probability of an event in the experimental group to the control group, providing a measure of relative effect size.

$ARR=\text{CER}-\text{EER}$. ARR measures the absolute difference in event rates between control and experimental groups, indicating the treatment's direct impact on risk.

$1-\beta$; probability of detecting a true effect. Power, denoted as $1-\beta$, represents the likelihood of correctly rejecting a false null hypothesis when a specific effect exists.

Balance known and unknown confounders between groups. Randomization distributes both measured and unmeasured confounding variables evenly across treatment groups to minimize bias in estimating treatment effects.

Selection bias from predicting upcoming assignments. Allocation concealment ensures investigators cannot foresee treatment assignments, preventing selective enrollment that could introduce bias into group compositions.

Performance and detection (assessment) bias. Blinding minimizes biases where knowledge of treatment assignment could influence participant behavior or outcome evaluation by investigators.

High false-positive risk from multiple comparisons. Unplanned subgroup analyses increase the chance of Type I errors due to multiple testing without adjustments, leading to spurious findings.

Factor associated with exposure and outcome, not on pathway. A confounder is an extraneous variable linked to both the exposure and outcome, potentially distorting the observed association if not controlled.