- The hierarchy of studies for obtaining evidence is:
- Systematic reviews of randomised controlled trials
- Randomised controlled trials
- Controlled observational studies – cohort and case control studies
- Uncontrolled observational studies – case reports
- When looking at the relevance of studies or reviews, the following issues should be considered:
- Type of study or analysis: RCT, prospective vs retrospective, multi-centred, blinding
- Type of intervention.
- Size of the sample.
- Type of person included and excluded.
- Type of control group used for comparison (ideally placebo).
- How reliable is the methodology?
- Results – P value, confidence limits? What is the rate of loss of follow-up during the study? Are there possible alternative explanations for the results?
- Type of outcome; objective e.g. mortality rate or subjective – pain assessment or use of validated scales (QALY, HAD etc)
- Is there a conflict of interest?
Hierarchical systems for levels of evidence and recommendations
- A variety of grading systems for evidence and recommendations are currently in use.
Grading of evidence
- Ia: systematic review or meta-analysis of randomised controlled trials
- Ib: at least one randomised controlled trial
- IIa: at least one well-designed controlled study without randomisation
- IIb: at least one well-designed quasi-experimental study, such as a cohort study
- III: well-designed non-experimental descriptive studies, such as comparative studies, correlation studies, case–control studies and case series
- IV: expert committee reports, opinions and/or clinical experience of respected authorities
Grading of recommendations
- A: based on hierarchy I evidence
- B: based on hierarchy II evidence or extrapolated from hierarchy I evidence
- C: based on hierarchy III evidence or extrapolated from hierarchy I or II evidence
- D: directly based on hierarchy IV evidence or extrapolated from hierarchy I, II or III evidence
A simpler system of A, B or C is recommended by the US Government Agency for Health Care Policy and Research (AHCPR)
- A: requires at least one randomised controlled trial as part of the body of evidence.
- B: requires availability of well-conducted clinical studies but no randomised controlled trials in the body of evidence.
- C: requires evidence from expert committee reports or opinions and/ or clinical experience of respected authorities. Indicates absence of directly applicable studies of good quality.
Numbers Needed To Treat
- The Number Needed to Treat (NNT) is the number of patients you need to treat to prevent one additional bad outcome.
- The NNT is the inverse of the Absolute Risk Reduction (ARR).
- The ARR is the Control Event Rate (CER) minus the Experimental Event Rate (EER), or ARR = CER – EER.
- NNTs are always rounded up to the nearest whole number and accompanied by its 95% confidence interval.
- A nomogram can be used to find the NNT by using the proportion of events in the control group and the relative risk reduction.
Benefits of NNT
- NNTs used for summarising a therapeutic trial or for medical decision on an individual
- The NNT is more clinically useful for an active intervention than the use of the relative risk, the relative risk reduction or the odds ratio.
- Although NNTs are easy to interpret, they cannot be used for performing a meta-analysis. Pooled numbers needed to treat derived from meta-analyses can be seriously misleading because the baseline risk often varies appreciably between the trials.
- Applying the pooled relative risk reductions calculated from meta-analyses or individual trials to the baseline risk relevant to specific patient group produces a useful number needed to treat.
Sensitivity & Specificity
- SnOut – Sensitive tests rule out diagnoses
- SpIn – Specific tests rule in diagnoses