Types of Data. Systematic Reviews: Data Synthesis Professor Jodie Dodd 4/12/2014. Acknowledgements: Emily Bain Australasian Cochrane Centre

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1 Early Nutrition Workshop, December 2014 Systematic Reviews: Data Synthesis Professor Jodie Dodd 1 Types of Data Acknowledgements: Emily Bain Australasian Cochrane Centre 2 1

2 What are dichotomous outcomes? When the outcome for every participant is one of two possibilities or events Alive or Dead Vaginal birth or Caesarean section Pregnant or Not pregnant 3 What are the chances of that? Risk and odds Express chance in numbers For dichotomous outcomes, express the chance within a group of being in one of two states Particular statistical meanings, calculated differently 4 2

3 Risk 24 people drank coffee 6 developed a headache Risk of a headache = 6 headaches / 24 people who could have had one = 6/24 = ¼ = 0.25 = 25% Risk = no. participants with event of interest total no. participants 5 Odds 24 people drank coffee 6 developed a headache Odds of a headache = 6 headaches/18 without headaches = 6/18 = 1/3 = 0.33 = 1:3 (not usually as %) Odds = no. participants with event of interest no. participants without event of interest 6 3

4 Expressing it in words Risk the risk of having a headache was one in four, or 25% Odds the odds of having a headache were one third of the odds of not having a headache one person had a headache for every three that didn t the odds of having a headache were 3 to 1 against 7 Comparing two groups Headache No headache Total Caffeine Decaf Total

5 Comparing two groups Effect measures risk ratio (RR) (relative risk) odds ratio (OR) risk difference (RD) (absolute risk reduction) ALL estimates are uncertain, and should be presented with a confidence interval 9 Risk ratio Risk of event with intervention =17/68 Risk of event with control = 9/64 Headache No headache Total Caffeine Decaf Total Risk ratio = intervention risk control risk =17/68 = 0.25 = / Where risk ratio = 1, there is no difference between the groups 10 5

6 Expressing it in words Risk ratio 1.79 the risk of having a headache with treatment was 179% of the risk in the control group intervention increased the risk of headache by 79% For a reduction in risk: Risk ratio 0.79 the risk of having a headache with treatment was 79% of the risk in the control group intervention reduced the risk of headache by 21% 11 Odds ratio Odds of event with intervention = 17/51 Odds of event with control = 9/55 Headache No headache Total Caffeine Decaf Total Odds ratio = intervention odds control odds =17/51 = 0.33 = / Where odds ratio = 1, there is no difference between the groups 12 6

7 Expressing it in words Odds ratio 2.06 intervention doubled the odds of headache intervention increased the odds to 206% of the odds in the control group intervention increased the odds of headache by 106% For a reduction in odds: Odds ratio 0.06 intervention reduced the odds of headache to 6% of the odds in the control group intervention reduced the odds of headache by 94% 13 Risk difference Risk of event with intervention = 17/68 Risk of event with control = 9/64 Headache No headache Total Caffeine Decaf Total Risk difference = risk with intervention risk with control = 17/68 9/64 = = 0.11 When risk difference = 0, there is no difference between the groups 14 7

8 Expressing it in words Risk difference 0.11 intervention increased the risk of headache by 11 percentage points 14 out of 100 people experienced a headache in the control group. 11 more people experienced a headache with caffeine. For a reduction in risk: Risk difference intervention reduced the risk of headache by 11 percentage points 14 out of 100 people experienced a headache in the control group. 11 fewer people experienced a headache with caffeine. 15 What are continuous outcomes? Can take any value in a specified range Intervals between values are equally spaced e.g. height, weight a person can be cm tall distance from 1 to 2 cm is the same as 171 to 172 cm Other numerical scales commonly assessed as continuous e.g. quality of life, pain, depression 16 8

9 Comparing two groups Effect measures mean difference (MD) (difference of means) standardised mean difference (SMD) (effect size) ALL estimates are uncertain, and should be presented with a confidence interval 17 Mean difference When all studies use the same measurement scale Irritability score Mean SD N Caffeine Decaf Mean difference = mean of intervention mean of control = = -13 points When mean difference = 0, there is no difference between the groups 18 9

10 Interpreting mean difference How should we interpret a score of -13? Depends on: direction of the scale length of the scale minimally important difference good or bad outcome 19 Expressing it in words Mean difference -13 on average, participants with the intervention scored 13 points lower on the irritability scale on average, the intervention reduced irritability by 13 points on the irritability scale For an increase: Mean difference 13 on average, participants with the intervention scored 13 points higher on the irritability scale on average, the intervention increased irritability by 13 points on the irritability scale 20 10

11 Standardised mean difference When different scales used to measure the same outcome SMD standardises the results units of standard deviation does not correct for direction SMD =mean intervention group mean control group pooled standard deviation of both groups When mean difference = 0, there is no difference between the groups 21 Interpreting standardised mean difference Irritability score Mean SD N MD SMD Caffeine Decaf How should we interpret a score of -1.5? Can be difficult to interpret convert results to a specific scale for reporting 22 11

12 What is a meta-analysis? Combines the results from two or more studies Estimates an average or common effect Optional part of a systematic review Systematic reviews Metaanalyses 23 Why perform a meta-analysis? Quantify treatment effects and their uncertainty Increase power Increase precision Explore differences between studies Settle controversies from conflicting studies Generate new hypotheses 24 12

13 When not to do a meta-analysis Mixing apples with oranges each included study must address same question consider comparison and outcomes requires your subjective judgement combining a broad mix of studies answers broad questions answer may be meaningless and genuine effects may be obscured if studies are too diverse 25 When not to do a meta-analysis Garbage in garbage out a meta-analysis is only as good as the studies in it if included studies are biased: meta-analysis result will also be incorrect will give more credibility and narrower confidence interval if serious reporting biases present: unrepresentative set of studies may give misleading result 26 13

14 When can you do a meta-analysis? More than one study has measured an effect The studies are sufficiently similar to produce a meaningful and useful result The outcome has been measured in similar ways Data are available in a format that can be used 27 Steps in a meta-analysis Identify comparisons to be made Identify outcomes to be reported and statistics to be used Collect data from each relevant study Combine the results to obtain the summary of effect Explore differences between the studies Interpret the results 28 14

15 Selecting comparisons Hypothetical review: Caffeine for daytime drowsiness caffeinated coffee vs decaffeinated coffee Break the topic down into pair-wise comparisons Each review may have one or many Your judgement is needed to decide what to group together, and what should be a separate comparison 29 Selecting outcomes & effect measures Hypothetical review: Caffeine for daytime drowsiness caffeinated coffee vs decaffeinated coffee asleep at end of trial (RR) irritability (MD/SMD) headaches (RR) for each comparison, select outcomes for each outcome, select an effect measure may depend on the available data from included studies 30 15

16 Calculating the summary result Collect a summary statistic from each contributing study How do we bring them together? treat as one big study add intervention & control data? breaks randomisation, will give the wrong answer simple average? weights all studies equally some studies closer to the truth weighted average 31 Weighting studies More weight to the studies which give more information more participants, more events, narrower confidence interval calculated using the effect estimate and its variance Inverse-variance method: weight 1 1 = 2 variance of estimate = SE sum of(estimate weight) pooled estimate = sum of weights 32 16

17 For example Headache Caffeine Decaf Weight Amore-Coffea /31 10/34 Deliciozza /40 9/40 Mama-Kaffa /53 9/61 Morrocona /15 1/17 Norscafe /68 9/64 Oohlahlazza /35 2/37 Piazza-Allerta /35 6/37 33 For example Headache Caffeine Decaf Weight Amore-Coffea /31 10/34 6.6% Deliciozza /40 9/ % Mama-Kaffa /53 9/ % Morrocona /15 1/17 2.9% Norscafe /68 9/ % Oohlahlazza /35 2/37 5.1% Piazza-Allerta /35 6/ % 34 17

18 A forest of lines 35 Headache at 24 hours Forest plots Headings explain the comparison 36 18

19 Headache at 24 hours Forest plots List of included studies 37 Headache at 24 hours Forest plots Raw data for each study 38 19

20 Headache at 24 hours Forest plots Total data for all studies 39 Headache at 24 hours Forest plots Weight given to each study 40 20

21 Headache at 24 hours Forest plots Effect estimate for each study, with CI 41 Headache at 24 hours Forest plots Effect estimate for each study, with CI 42 21

22 Headache at 24 hours Forest plots Scale and direction of benefit 43 Headache at 24 hours Forest plots Pooled effect estimate for all studies, with CI 44 22

23 Interpreting results What is the direction of effect? What is the size of effect? Is the effect consistent across studies? Heterogeneity What is the strength of evidence for the effect? Risk of bias Statistical uncertainty 45 What is heterogeneity? Variation or differences Three broad types: Clinical Methodological Statistical 46 23

24 Clinical diversity Participants e.g. condition, age, gender, location, study eligibility criteria Interventions intensity/dose, duration, delivery, additional components, experience of practitioners, control (placebo, none, standard care) Outcomes follow-up duration, ways of measuring, definition of an event, cut-off points 47 Methodological diversity Design e.g. randomised vs non-randomised, crossover vs parallel, individual vs cluster randomised Conduct e.g. risk of bias (allocation concealment, blinding, etc.), approach to analysis 48 24

25 Statistical heterogeneity There will always be some random (sampling) variation between the results of different studies Heterogeneity is variation between the effects being evaluated in the different studies caused by clinical and methodological diversity alternative to homogeneity (identical true effects underlying every study) study results will be more different from each other than if random variation is the only reason for the differences between the estimated intervention effects 49 Exploring the results What factors appear to modify the effect? Clinical diversity (population, interventions, outcomes) Methodological diversity (study design, risk of bias) Plan your strategy Identify a limited number of important factors to investigate Subgroup analysis Group data by pre-specified factors Look for differences in results and heterogeneity Sensitivity analysis Group data by methodological factors High vs Low risk of bias; Randomised vs Quasi-randomised Look for differences in results and heterogeneity 50 25

26 51 26

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