Session 6 Flashcards
Antacids: Aluminium hydroxide (Maalox), Gaviscon (Compound Alginate)
Anti-spasmodics: Hyoscine, Mebeverine Histamine
H2 Antagonists: Ranitidine
PPIs: Omeprazole, Lansoprazole, Esomeprazole
Anti-motility Drugs: Loperamide
Laxatives: Ispaghula Husk, Senna, Lactulose, Macrogols (e.g. Movicol), Glycerol Supps, Phosphate enemas
Inflammatory bowel disease: mesalazine, infliximab
- Explain the circumstances in which a systematic review should be undertaken and its purpose
- Define & describe a systematic review
- appraise, summarise and communicate large quantities of research on a similar topic
(qualitative or quantitative)
- An overview of primary studies (RCT, can be cohort or case control studies) that is used explicit and reproducible methods
- Clearly focused question
- Explicit statements about:
– types of study, participants, interventions, outcome measures
- Systematic literature search
- Selection of the materials
- Appraisal
- Synthesis (possibly including a meta-analysis)
Credible source of evidence – it is explicit, transparent and reproducible
- Define a meta analysis LO
- Purpose of a meta analysis LO
- Quality Criteria LO
- a quantitative synthesis of the results of two or more primary studies that addressed the same hypothesis in the same way
- • To facilitate the synthesis of a large number of study results
- To systematically collate study results
- To reduce problems of interpretation due to variations in sampling
- To quantify effect sizes and their uncertainty as a pooled estimate
- Meta-analysis should have a formal protocol specifying:
- compilation of complete set of studies
- identification of common variable or category definition
- standardised data extraction
- analysis allowing for sources of variation
Calculate the MHRC Odds ratio
Meta analysis has many two or more primary studies thus we need to pool the CI and Odds ratios together. How do we do this?
- Odds ratios and their 95% CIs are calculated for all studies in meta-analysis
- These are then combined to give a pooled estimate odds ratio using a statistical computer program
- Studies are weighted according to their size and the uncertainty of their odds ratio (narrower CI → greater weight to result)
Interpret the forest plot LO
- 6 out of the 7 RCTs had an OR > 1.00 indicating greater odds for survival amongst patients taking aspirin after MI
- Only 1 RCT (the largest) had a statistically significant result, but its OR was less than the other RCTs with an OR > 1.00
- Pooled estimate OR = 1.11 (95% CI: 1.04 to 1.19) leads to the conclusion that aspirin increases the chance of surviving after a MI (p<0.05)
List common difficulties in systematic reviews and meta-analyses LO
- Problems in a meta analysis
• Heterogeneity between studies:
– Modelling for variation (Fixed effect model vs. Random effects model)
– Analysing the variation (Sub-group analysis)
• Variable quality of the studies
• Publication bias in selection of studies
How do we calculate a pooled estimated odds ratio?
- Fixed effect model – assumes that the studies are estimating exactly the same true effect size
- Random effects model – assumes that the studies are estimating similar, but not the same, true effect size
Fixed effects model – assumes the studies used are homogenous and any variation between data comes from within-study variation
Random effects model – assumes the studies are heterogeneous and variation between data comes from within-study variation and between-study variation
state the general differences in results between the random effects model and the fixed effects model
• Between study variance (τ2) was 0.01, i.e. heterogeneity between studies was low
• Fixed effect model:
– pooled estimate OR = 1.11 (95% CI: 1.04 to 1.19)
• Random effects model:
– pooled estimate OR = 1.14 (95% CI: 1.01 to 1.29)
Fixed Effect vs. Random Effects
• Point estimate (e.g. odds ratio):
– is often similar (but not always) in both the Random Effects Model
and Fixed Effect Model
• 95% Confidence Interval (95% CI):
– is often wider in the Random Effects Model than in the Fixed Effect
Model
• Weighting of the studies:
– is more equal between the studies in the Random Effects Model than in the Fixed Effect
Model, i.e. greater weighting towards small studies
• Hypothesis test for heterogeneity:
– low statistical power to detect heterogeneity, often use 10%
significance level
Much debate over which model is superior
- Random effects modelling can only account for variation but not explain it
- Sub-group analysis can help to explain heterogeneity which may provide further insight into the effect of a treatment or exposure.
What are the two types of sub group analysis?
• Stratification by study characteristics – where subsets of ‘whole’ studies defined by, e.g.
– study design, e.g. length of follow-up
– participant profile, e.g. recruitment criteria
• Stratification by participant profile – where data is analysed by types of participants, e.g. age group (young, old), sex (female, male)
– although this has greater statistical power than for individual studies, data is often unavailable
What do we mean by Variable Quality of the Studies: The Issues?
how do we overcome the issues associated with variable quality of the studies?
• define a basic quality standard and only include studies satisfying this criteria, e.g. Cochrane reviews used to include only RCTs
• score each study for its quality and then
– incorporate the quality score into the weighting allocated to each study during the modelling, so that higher quality studies have a greater influence on the pooled estimate
– use sub-group analyses to explore differences, e.g. high quality studies vs. low quality studies (– allocation methods, e.g. randomisation?
– blinding and outcome assessment
– patient attrition, e.g. <10% & Intention-to-Treat (ITT))
Publication Bias in Selection of Studies: Reasons and Consequences
- What is publication bias?
- Methods of identification
• Studies with statistically significant or ‘favourable’ results are more likely to be published than those studies with non- statistically significant or ‘unfavourable’ results – this applies particularly to smaller studies
• Any systematic review or meta-analysis can be flawed by such bias
– publication bias leads to a biased selection of studies towards demonstration of effect
- • Check meta-analysis protocol for method of identification of studies
– it should include searching and identification of unpublished studies
• Plot results of identified studies against a measure of their size (e.g. inverse of standard error), i.e. a Funnel Plot
• Use a statistical test for publication bias – they tend to be weak statistical tests
Interpretation
- A plot of some measure of study size against measure of effect (e.g. odds ratio)
- If no publication bias, then the plot will be a ‘balanced’/symmetrical funnel
- Smaller studies can be expected to vary further from the ‘central’ effect size
- Publication bias is likely to exist if there are few small studies with results indicating small or ‘negative’ measure of effect
Variation
Variation always occurs in an epidemiological study whereby there is a difference between the ‘observed’ and the ‘actual’ value.
To allow for these variations that occur in any epidemiological study, an error factor is produced and from that confidence intervals are produced.
Confidence intervals are a range of values that we can say (with confidence) that the actual value will lie inbetween this range in 95% of cases.
What is a cohort study?
- disease free individuals
- classifying them according to their exposure status
- followed up (for extended periods, disease progress is monitored, & incidence rates are calculated)
Good for rare exposures or long time for disease to develop
Can be either:
Prospective – disease free individuals recruited and followed up
Retrospective –disease free individuals recruited then exposure status calculated from historical documentation and followed up
What is a case control study?
Used for rare diseases
Error factor is calculated by e2√((1÷a) + (1÷b) + (1÷c) + (1÷d)) for the odds ratio
The number of controls used is usually 5 times the amount of cases, as controls are easier to find for rare diseases and this allows the error factor to be minimised
