1. September 26, 2012 DATA EVALUATION AND ANALYSIS IN SYSTEMATIC REVIEW

2. Stages of Systematic Review 1.Define the Problem 2.Literature Search 3.Data Evaluation 4.Data Analysis 5.Interpretation of Results 6.Presentation of Results

3. Data Evaluation What retrieved research should be included or excluded from your review? Are the methods used in retrieved literature suitable to study your research question? Are there problems in research implementation? Evaluating quality of retrieved literature Coding literature for inclusion and exclusion Coder reliability and avoiding coding error

4. Data Evaluation: Study Quality What makes a high quality study?

5. Data Evaluation: Study Quality What makes a high quality study? Validity Relevance Study design Reporting quality

6. Data Evaluation: Validity Internal Validity (experimental validity) Validity of causal inference Does the cause lead to the effect? Randomized controlled experiment is gold standard Requirements: Cause precedes effect Cause and effect are related Other plausible explanations are ruled out

7. Data Evaluation: Validity Threats to internal validity: Ambiguous timeline of cause and effect Other plausible explanations Uncontrolled circumstances Confounding Bias

8. Data Evaluation: Validity Example: organic dairy Does organic dairy farming (effect) produce higher quality milk (outcome)? What are threats to internal validity? Other explanations: differences in diet, climate, breed

9. Data Evaluation: Validity External validity Degree to which a causal inference can be generalized How would you be wrong to make a generalization? Does an experiment resemble the real world? Does it apply in other populations? Other regions? Example: organic dairy Can a cause and effect relationship between organic dairy farming and quality of milk in a study apply more generally? Ecological validity – are study conditions like those in natural conditions?

10. Data Evaluation: Validity Construct validity Degree to which operational definitions represent concepts Does the study measure the variable in a valid way? Example: organic dairy What is the concept of milk quality and how is it measured?

11. Data Evaluation: Validity Statistical conclusion validity Validity of statistical inferences used to assess the strength of relationship between cause and effect Does the data meet the assumptions of statistical tests used in the study? Examples: Study uses a t test, but data are not normally distributed Study uses linear regression but variables do not have a linear relationship

12. Data Evaluation: Study Design Which study designs should be included in your review? Design influences validity Randomized vs non-randomized Cohort Case-control Cross sectional Case reports

13. Data Evaluation: Relevance Degree to which a retrieved study applies to the review question High quality ≠ Relevant Does a retrieved study have features that make it irrelevant to the review? Population studied Methods used Definitions of variables (construct validity) Determine criteria for relevance to your question

14. Data Evaluation: Reporting Quality How a study is reported affects inclusion in review Poor reporting quality makes analysis difficult Incomplete data Missing information Space restriction in publishing

16. Data Evaluation: Strategies A priori Rules are determined ahead of time for what studies will be included Rules determined before data is examined or outcomes are known Need to consider implications of all rules Place specific values on rules Provide reasons why rules remove bias from review Post hoc Determines the impact of study quality on the review to make inclusion decisions How will inclusion of certain studies impact the results of the review? Does not rely on arbitrary rules Many systematic reviews use a blend of strategies

17. Data Evaluation: Strategies A priori Exclusion Deciding to exclude all studies that do not meet a certain criteria excluding all non-randomized studies Quality Scales Criteria included in scale has to be adapted to the field and research question Not based on empirical evidence Often not an evidence base for predictions of bias for quality indicators

18. Data Evaluation: Strategies Post hoc Quality is handled as an empirical question Attempts to avoid problems with a priori assignments How will the review results be influenced if certain studies are included? Can compare bias introduced by certain types of studies Example: inclusion of non-randomized studies A priori – include only randomized studies Post hoc – does inclusion of the non-randomized studies influence the results? If not, then include

19. Data Evaluation: Coding the Literature Once you have a set of retrieved studies, code them for inclusion in final review Coding components: Eligibility criteria Study features defined: Eligible study designs Eligible methods Sampling criteria Statistical criteria Geographical and linguistic Time frame

20. Data Evaluation: Coding the Literature Develop a coding protocol Develop like a questionnaire: Clearly define what you want to measure – concepts and study characteristics May need multiple coding questions to evaluate each concept Develop a matrix of all studies after retrieval Way to organize post hoc Reflects many characteristics of retrieved studies May help if unsure what to code before examining studies

21. Data Evaluation: Coding the Literature Develop a coding form All reviewers will use Allows efficient record keeping by whole team Include a report identification – assign each study a number, etc

22. Report Characteristics 1. First author 2. Journal 3. Volume 4. Pages Inclusion Criteria 5. Is this study an empirical investigation of the effects of teacher expectancies? 0. No 1. Yes 6. Is the outcome a measure of IQ?0. No 1. Yes 7. Are the study participants in grades 1-5 at the start of the study? 0. No 1. Yes Table 7.2 Handbook of Research Synthesis and Meta-Analysis Relevance Screen

23. 9. Sampling Strategy1.Randomly sampled from a defined population 2.Stratified sampling from a defined population 3.Cluster sampling 4.Convenience sample 5.Can’t tell 10. Group assignment mechanism1.Random assignment 2.Haphazard assignment 3.Other nonrandom assignment 4.Can’t tell 11. Assignment mechanism1.Self-selected into groups 2.Selected into groups by others on a basis related to outcome 3.Selected into groups by others not known to be related to outcome 4.Can’t tell Table 7.3 Handbook of Research Synthesis and Meta-Analysis Coding for Internal Validity

24. 18. IQ measure used in study1.Stanford-Binet 5 2.Wechsler (WISC) III 3.Woodcock Johnson III 4.Other 5.Can’t tell 19. Score reliability for given IQ measure________________ 20. Metric for score reliability1.Internal consistency 2.Split-half 3.Test-retest 4.Can’t tell 5.None given 21. Source of score reliability1.Current sample 2.Citation from another study 3.Can’t tell 4.None given 22. Is the validity of the IQ measure mentioned? 0. No 1. Yes Table 7.4 Handbook of Research Synthesis and Meta-Analysis Coding Construct Validity

25. BMC Medical Research Methodology 2008, 8:21

26. Data Evaluation: Coding the Literature Assess coding reliability Intra and inter-coder reliability Intra – consistency of a single coder (avoid coder drift) Inter – consistency between coders Sources of error in coding decisions Deficient reporting in the study Judgments made by coders Coder bias Coder mistake

27. Data Analysis What procedures should be used to summarize and integrate the research results? Quantitative analysis (meta-analysis) Statistical techniques to synthesize data from studies Qualitative analysis Allows synthesis and interpretation of non-numerical data

28. Data Analysis: Qualitative Methods of Qualitative Analysis: 1.Content Analysis Synthesis of the content of studies Organization of content with keywords or concepts 2.Meta-Ethnography Ethnography – study of a whole culture Focuses on the culture as a system, understanding the whole 3.Grounded Theory Formation of a theory from synthesis of data Reverse of most hypothesis driven research

29. Content Analysis Defining categories with keywords from studies ELO S. & KYNGA ELO S. & KYNGAS H. (2008) ¨ S H. (2008) The qualitative content analysis process. Journal ofAdvanced Nursing 62(1), 107–115

30. Meta-Ethnography BMC Medical Research Methodology 2008, 8:21

31. Grounded Theory