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Observational Studies: Critical Appraisal Course

Learn Theoretical & Practical Aspects of Observational Studies. Assess Strength, Temporality, Consistency, Plausibility, Bias, & More. Study Different Designs & Methodological Grading. Understand Associations & Multivariate Analysis.

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Observational Studies: Critical Appraisal Course

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  1. OBSERVATIONAL STUDIES Instructor: Fabrizio D’Ascenzo fabrizio.dascenzo@gmail.com www.emounito.org www.metcardio.org Role MD

  2. CONFLICT OF INTEREST None

  3. AIM OF THE COURSE A critical appraisal • Theorical • Practical of observational studies

  4. TODAY’S PROGRAM: FIRST PART • Literature: clinical general concepts • Literature: clinical methodological concepts • Quick assessment of an observational study • Complete assessment of on observational study

  5. HOW TO READ and WRITE A STUDY Two points of view: • Clinical • Methodological

  6. CLINICAL

  7. Strenght of association • Temporality • Consistency • Theorical Plausibility • Coherence • Specificity in the cause • Dose-response • Experimental evidence • Analogy

  8. STRENGHT OF ASSOCIATION Size of the association as measured by appropriate statistical tests Example Odds Ratio, Relative Risk But strength of association depends on the prevalence of other potential confounding factors

  9. TEMPORALITY Exposure should always precede the outcome

  10. CONSISTENCY The association is consistent when results are replicated in studies in different settings using different methods.  If a relationship is causal, we would expect to find it consistently in different studies and among different populations. 

  11. THEORICAL PLAUSIBILITY andCOHERENCE The association agrees with currently accepted understanding of pathological processes.  A causal association is increased if a biological gradient or dose-response curve can be demonstrated. The association should be compatible with existing theory and knowledge. 

  12. IS THIS ENOUGH?

  13. RELIABLE EVIDENCE?

  14. METHODOLOGICAL

  15. GRADING THE EVIDENCE

  16. WHY TO PERFORM AND READ NOT RANDOMIZED EVIDENCE? • to save economical resources • to create hypothesis, especially for non randomizable patients • to shed light on the generalizability of results from existing randomized experiments

  17. HOW TO EVALAUTE NON RANDOMIZED EVIDENCE?

  18. QUICK ASSESSMENT OF AN OBSERVATIONAL STUDY

  19. 3 CRUCIAL CONCEPTS • DESIGN OF THE STUDY • BIAS • MULTIVARIATE ANALYSIS

  20. THREE DIFFERENT DESIGNS

  21. COHORT Advantages: chances to appraise different outcomes Disvantages: if events/outcomes are unfrequent, large number of patient is needed

  22. CASE-CONTROL Advantages: studies for infrequent outcomes Disvantages: controls patients need to be selected from the whole population

  23. CROSS SECTIONAL Advantages: easy to perform Disvantages: limited function

  24. OR EASIER • Retrospective>means testing an hypothesis on datasets - already present - built for that hypothesis but not at the time of patients’assessment • Prospective>means testing an hypothesis on datasets built for it, to evaluate, study and insert data of the patients at the moment of their hospitalization/drug assumption/intervention

  25. REASON FOR ASSOCIATIONS

  26. REASON FOR ASSOCIATIONS • Bias • Confounding • Chance • Cause

  27. BIAS Measure of association between exposure and outcome is systematically wrong Two directions: - bias away from the null - bias towards the null

  28. SELECTION BIAS Unintended systematic difference between the two or more groups, which is associated with the exposure.

  29. FOR EXAMPLE Inclusion of too selected patients: > patients with more severe disease presentation are often excluded TO obtain larger benefits

  30. ATTRITION BIAS If reported: How many patients attain a complete follow up> if a patient is lost at follow up, he/her may have dead (more probably) or alive

  31. 1192 consecutive patients undergoing PCI in our center betweenJanuary 2009 and January 2011 1116 patients with follow up data derived from PiedmontRegiondedicatedregistry (AURA) Medicalfoldersofeachpatient, and forre-hospitalizationswerere-analyzedby a physician 76 patients not recorded in Piedmont Region dedicated registry: 39 recovered through phone call 37 not detectable (30 not European….) 1155 at follow up of 787 days (median;474-1027) Figure 1.

  32. ADJUDICATION BIAS If reported: who adjudicate the events: • A blinded central committee • Non blinded researchers

  33. ANALITICAL/INFORMATION BIAS an error in measuring exposure or outcome may cause information bias>lower risk if the study is multicenter

  34. IF REPORTED….

  35. CHANCE The precision of an estimate of the association between exposure and outcome is usually expressed as a confidence interval (usually a 95% confidence interval)

  36. The width of the confidence interval is determined by the number of subjects with the outcome of interest, which in turn is determined by the sample size.

  37. With 200 pts With 1000 pts

  38. CONFOUNDING The aim of an observational study is to examine the effect of the exposure, but sometimes the apparent effect of the exposure is actually the effect of another characteristic which is associated with the exposure and with the outcome.

  39. MULTIVARIATE ANALYSIS Multivariable analysis aims to explore the relationship between a dependent variable and two or more independent variables appraised simultaneously.

  40. ARE ALL MULTIVARIATE ANALYSIS THE SAME? • Logistic regression • Cox Multivariate adjustement • Propensity score

  41. HOW TO CHOOSE VARIABLES To avoid:- automatic algorithms with stepwise selection To choose established association from: • prior well conducted experimental or clinical studies • strong associations (e.g.p<0.10 or p<0.05 at univariate analysis)

  42. LOGISTIC REGRESSION: THE SIMPLEST ONE The logit function transforms a dependent variable ranging between 0 and 1 such as a probability of an event into a variable stemming from −∞ to +∞.

  43. LOGISTIC REGRESSION: THE SIMPLEST ONE Thus, event probabilities can be appraised as a linear regression function to appraise the logit of the probability of an event (dependent variable) given one or more dependent variables

  44. LOGISTIC REGRESSION: THE SIMPLEST ONE: LIMITS • Overfit model can be highly predictive in the dataset in which the model was developed, but not in one in which it is validated or tested. • Multicollinearity, whereby covariate present in the model are unduly associated • Does not correct for time

  45. COX PROPORTIONAL HAZARD ANALYSIS: THE MOST USED ONE • It addresses differences in follow-up duration and censored data • It is based on The hazard function, which forms the basis of Cox analysis: the event rate at time t conditional on survival until time t or late

  46. CENSORED DATA Censored patients are exploited to compute hazards and are assumed in the Cox model to fail at the same rate as the non censored, but are not supposed to survive to the next time point.

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