Biostatistics series module 8: Assessing risk - Avijit Hazra; Nithya Gogtay (Wolters Kluwer Medknow Publications, 2017)
In observational studies, as well as in interventional ones, it is frequently necessary to estimate risk that is the association between an observed outcome or event and exposure to one or more factors that may be contributing to the event. Understanding incidence and prevalence are the starting point in any discussion of risk assessment. Incidence rate uses person-time as the denominator rather than a simple count. Ideally, rates and ratios estimated from samples should be presented with their corresponding 95% confidence intervals (CIs). To assess the importance of an individual risk factor, it is necessary to compare the risk of the outcome in the exposed group with that in the nonexposed group. A comparison between risks in different groups can be made by examining either their ratio or the difference between them. The 2 × 2 contingency table comes in handy in the calculation of ratios. Odds ratio (OR) is the ratio of the odds of an event in the exposed group, to the odds of the same event in the nonexposed group. It can range from zero to infinity. When the odds of an outcome in the two groups are identical, then the OR equals one. OR >1 indicates exposure increases risk while OR <1 indicates that exposure is protecting against risk. The OR should be presented with its 95% CI to enable more meaningful interpretation - if this interval includes 1, then even a relatively large OR will not carry much weight. The relative risk (RR) denotes the ratio of risk (probability) of event in exposed group to risk of same event in the nonexposed group. Its interpretation is similar (but not identical) to the OR. If the event in question is relatively uncommon, values of OR and RR tend to be similar. Absolute risk reduction (ARR) is a measure of the effectiveness of an intervention with respect to a dichotomous event. It is calculated as proportion experiencing the event in control group minus the proportion experiencing the event in treated group. It is often used to denote the benefit to the individual. The reciprocal of ARR is the number needed to treat (NNT), and it denotes the number of subjects who would need to be treated to obtain one more success than that obtained with a control treatment. Alternatively, this could also denote the number that would need to be treated to prevent one additional adverse outcome as compared to control treatment. Extended to toxicity, the NNT becomes a measure of harm and is then known as the number needed to harm (NNH). NNT and NNH are important concepts from the policy makers perspective and ideally should be calculated in all trials of therapeutic or prophylactic intervention.