# Study Types

Describe the features of evidence-based medicine, including

~~levels of evidence (e.g. NHMRC),~~meta-analysis, and systematic review

## Randomised Control Trial

A prospective randomised controlled trial is the **gold standard** of experimental research.

It involves allocating patients randomly to either an intervention or a reference (control) group, and measuring the outcome of interest. Allocation can be performed in three ways:

- Simple

Individuals allocated randomly. This may lead to uneven group sizes. - Block

Allocation is performed within blocks such that group sizes will remain close in size - Stratified

Groups are randomised within a category (i.e. men and women are randomised separately).

### Strengths

- Only study design which can establish causation
- Eliminates confounding

Randomisation controls for both known and*unknown*confounding factors, as these should be randomly allocated between groups. - Blinding can be performed in a standardised fashion
- Decreases selection bias

### Weaknesses

- Costly
- Time-consuming
- Not appropriate for all study designs
- Ethical concerns

e.g. Adrenaline in ALS - Practical concerns

Small patient population or uncommon disease may cause recruitment difficulties

- Ethical concerns

## Systematic Review

The process of evaluating all of the (quality) literature to answer a specific clinical question. This:

- Does not necessarily involve statistical analysis

If it involves statistical analysis of multiple trials to generate a combined estimate of effect, it is known as a**meta-analysis**.

## Meta-analysis

Mathematical technique of combining the results of different trials to derive a single pooled estimate of effect. Can be performed by:

- Pooling the results of each trial
- Combining all of the raw data and conducting a reanalysis

- Meta-analyses usually use
**random-effects models**, which assumes there will be a variety of similar treatment effects - Individual trials are
**summarised with an odds ratio**, and**weighted**, usually predominantly by sample size

### Stages of a [meta-analysis] and systematic review:

- Inclusion and exclusion criteria are predefined
- Search: including online databases, reference lists, citations, and experts
- Validation of potentially eligible trials (critique of interval validity, i.e. trial quality)
- [Heterogeneity Analysis]
- [Meta-analysis]
- Reliability of result determined

i.e. Consistency accross studies, statistical significance, large effect size, biological plausibility. - Sensitivity analysis

Repeating the analysis with an alternative model, excluding borderline trials or outliers. If the result is unchanged, then the findings are**robust**.

### Heterogeneity

For the pooling of results to be valid, the trials need to be similar. Differences between trials is known as **heterogeneity**. Heterogeneity can be either:

**Statistical**; where the effects of the intervention are more different than would be expected to occur through chance alone. Heterogenety analysis affects the type of model that can be used (**fixed**or**mixed effects**) and highly heterogenous data is not appropriate for meta-analysis.**Clinical**; where, due to trial design, it would be inappropriate to pool the results. For example, conducting a meta-analysis on the effects of the same drug in a paediatric and adult population would be inappropriate, as these are two different populations.**Methodological**; Where the methods used in different trials are too different to allow pooling of the data.

### Forest Plots

Results of meta-analyses are presented in a blobbogram, or more boringly, a **Forest Plot**.

Where:

- The
**x-axis**plots the odds ratio, remembering that an OR of 1 indicates no difference - The
**y-axis**lists the studies included, and the overall summary statistic - The
**square**indicates the**point estimate**(from its x-location) and the**weight**given to the study (by its size) - The
**horizontal line**indicates the upper and lower bounds of the**confidence interval** - The
**diamond**indicates the**overall point estimate**and (by its width) the**confidence interval**for the point estimate - The result of the heterogeneity test should also be displayed. P < 0.1 indicates significant heterogeneity.

### Funnel Plots

A graphical tool to detect publication bias. Due to statistical power, larger studies should be a closer representation of the true effect. When evaluating an number of studies, one would expect that large studies cluster around the 'true effect' and smaller studies to have more scatter.

### Strengths and weaknesses of meta-analyses

Strengths | Weaknesses |
---|---|

Enhanced precision of estimates of effect | Publication bias |

Useful when large trials have not been done or are not feasible | Duplicate publication |

Generate clinically relevant measures (NNT, NNH) | Heterogeneity |

Inclusion of outdated studies |

Because of these weaknesses, positive meta-analyses should be considered largely hypothesis-generating, and should be confirmed by (a large) RCT. Negative meta-analyses can probably be accepted.

## References

- Myles PS, Gin T. Statistical methods for anaesthesia and intensive care. 1st ed. Oxford: Butterworth-Heinemann, 2001.