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Meta-Analysis: Sports Medicine

Canadian Academy of Sport Medicine. L’Académie Canadienne de Médecine du Sport. Meta-Analysis: Sports Medicine. Ian Shrier MD, PhD, Dip Sport Med, FACSM. Centre for Clinical Epidemiology and Community Studies, SMBD-Jewish General Hospital and McGill University

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Meta-Analysis: Sports Medicine

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  1. Canadian Academy of Sport Medicine L’Académie Canadienne de Médecine du Sport Meta-Analysis: Sports Medicine Ian ShrierMD, PhD, Dip Sport Med, FACSM Centre for Clinical Epidemiology and Community Studies, SMBD-Jewish General Hospital and McGill University Past-president, Canadian Academy of Sport Medicine

  2. Canadian Academy of Sport Medicine L’Académie Canadienne de Médecine du Sport Meta-Analysis: Sports Medicine Ian ShrierMD, PhD, Dip Sport Med, FACSM Centre for Clinical Epidemiology and Community Studies, SMBD-Jewish General Hospital and McGill University Past-president, Canadian Academy of Sport Medicine

  3. OBJECTIVES How do we think? A Workshop Example: Does Stretching Prevent Injury? What parameter are you estimating? RCT vs Obs studies in meta-analyses It’s all about Bias!

  4. INTERPRETATIONS “It’s a rather interesting phenomenon. Every time I press this lever, the graduate student breathes a sigh of relief”

  5. INTERPRETATIONS Shrier, Platt, Steele. Mega-trials vs. meta-analysis: Precision vs. heterogeneity? Contemp Clin Trials 2007 Shrier et al. Should Meta-Analyses of Interventions Include Observational Studies in Addition to Randomized Controlled Trials? A Critical Examination of Underlying Principles. Am J Epi 2007 Shrier et al. The interpretation of systematic reviews with meta-analyses: an objective or subjective process? BMC Med Inform Dec Making 2008

  6. INTERPRETATIONS 415 597 3,685 63,047 69,505 0.40 (0.19-0.83) 0.40 (0.28-0.61) 0.64 (0.52-0.79) 1.02 (0.96-1.08) 1.01 (0.96-1.07) 0.40 (0.18-0.86) 0.38 (0.21-0.66) 0.66 (0.53-0.81) 0.65 (0.48-0.87) 0.75 (0.61-0.92) 0% 0% 14% 61% 59% Ag Ag Ag Ag Ag SA SA SA Ag - - - DA SD DA DA Ag DA SD Ag Ag Ag Ag Ag Ag Ag Ag Ag Ag - - DA DA DA SD DA - Ag Ag I believe magnesium has now been shown to be beneficial for patients during the post-MI period (SD-SA) # RCTs 1-3 1-5 1-10 1-20 1-23 N Fixed OR Rand. OR I2 Rev 1 Rev 2 Rev 3 Rev 4 Rev 5 Rev 6 Rev 7 Rev 8

  7. HOW DO WE THINK? Clue: want • crave, covet, yearn, fancy (Vandenbroucke et al, 2001)

  8. HOW DO WE THINK? Clue: want • crave, covet, yearn, fancy (Vandenbroucke et al, 2001)

  9. HOW DO WE THINK? Queues Clue: want • crave, covet, yearn, fancy (Vandenbroucke et al, 2001)

  10. HOW DO WE THINK? Queues Clue: want • crave, covet, yearn, fancy (Vandenbroucke et al, 2001)

  11. HOW DO WE THINK? Queues Clue: want • crave, covet, yearn, fancy (Vandenbroucke et al, 2001)

  12. OBJECTIVES How do we think? A Workshop Example: Does Stretching Prevent Injury? What parameter are you estimating? RCT vs Obs studies in meta-analyses It’s all about Bias!

  13. Does Stretching Prevent Injury? (adapted from Shrier, Evidence-Based Sports Medicine 2007)

  14. Yes  No  I will now tell my patients to stretch to prevent injury Yes  No  I will now tell my patients not to stretch to prevent injury Yes  No  I will now tell my patients that I have no idea whether they should stretch to prevent injury Does Stretching Prevent Injury? Analysis

  15. Does Stretching Prevent Injury? (adapted from Shrier, Evidence-Based Sports Medicine 2007)

  16. Does Stretching Prevent Injury? Analysis Yes  No  I will now tell my patients to stretch to prevent injury Yes  No  I will now tell my patients not to stretch to prevent injury Yes  No  I will now tell my patients that I have no idea whether they should stretch to prevent injury

  17. Acute Stretching: Force (MVC/1RM) % Unstretched Condition (adapted from Shrier, Clin J Sport Med 2004)

  18. Regular Stretching: Force (MVC/1RM) PNF Static % Unstretched Condition (adapted from Shrier, Clin J Sport Med 2004)

  19. Slow (30-60 deg/s) Fast (>180 deg/s) Acute Stretching: Force (Isokinetic) % Unstretched Condition (adapted from Shrier, Clin J Sport Med 2004)

  20. Slow (30-60 deg/s) Fast (>180 deg/s) Regular Stretching: Force (Isokinetic) % Unstretched Condition (adapted from Shrier, Clin J Sport Med 2004)

  21. Acute Stretching: Jump Height Static CMJ % Unstretched Condition (adapted from Shrier, Clin J Sport Med 2004)

  22. Regular Stretching: Jump Height Static CMJ % Unstretched Condition (adapted from Shrier, Clin J Sport Med 2004)

  23. Results Stretching and Force • Overstretching occurs with as little as 20% stretch • Protocol • Skinned bullfrog muscle fibers stretched and released to different lengths (adapted from Higuchi et al, J Mus Res Cell Motil 1988)

  24. Results Regular Stretching: Force • Protocol • Weights attached to left wing of Japanese Quail x 30 days • Animals killed and ant. lat. dorsi. placed in vitro (adapted from Alway, J Appl Physiol 1984)

  25. Regular Stretching: Injury (adapted from Shrier, Evidence-Based Sports Medicine 2007)

  26. Acute Stretching: Injury Excluding multiple co-intervention studies (adapted from Shrier, Evidence-Based Sports Medicine 2007)

  27. Acute Stretching: Injury Excluding multiple co-intervention studies (adapted from Shrier, Evidence-Based Sports Medicine 2007)

  28. Does Stretching Prevent Injury? Analysis Yes  No  I will now tell my patients to stretchbefore exercise to prevent injury Yes  No  I will now tell my patients not to stretch before exercise to prevent injury Yes  No  I will now tell my patients that I have no idea whether they should stretch before exercise to prevent injury

  29. Does Stretching Prevent Injury? Analysis Yes  No  I will now tell my patients to stretch regularly to prevent injury Yes  No  I will now tell my patients not to stretch regularly to prevent injury Yes  No  I will now tell my patients that I have no idea whether they should stretch regularly to prevent injury

  30. OBJECTIVES How do we think? A Workshop Example: Does Stretching Prevent Injury? What parameter are you estimating? RCT vs Obs studies in meta-analyses It’s all about Bias!

  31. Effect of RCT on Outcomes Clinical Trials and Meta-Analysis 1994;29:41–47

  32. RCT vs. Observational: Theoretical % Complete Abstainment Nico # Days Smoked Plac Blind Blind Nico Nico Plac Plac • Hughes • Balanced placebo/traditional design • RCT Informed: pts told Nicotine or Placebo • Balanced placebo: Pts randomized to be told Nicotine or Placebo, but random 50% given what they were told (4 groups) ITT: Patient wants to know effect of intervention conditional on them receiving the intervention (per protocol?) Psychopharmacology 1989

  33. OBJECTIVES How do we think? A Workshop Example: Does Stretching Prevent Injury? What parameter are you estimating? RCT vs Obs studies in meta-analyses It’s all about Bias!

  34. more extreme RCT vs. Observational: Evidence Linde: Observational studies about 10-20% better for acupuncture/headache(J Clin Epi 2002) Concato: No difference in well-designed studies(NEJM 2000) MacLehose: discrepancies for high quality studies were small but discrepancies for low quality studies were large (HTA 2000) Benson: No difference after 1984(Am J Opthalmol 2000) Britton: “Non-randomized overestimated magnitude of effect”(HTA 1998)

  35. RCT vs. Observational: Theoretical Design Pro Con • All Studies • Adjust for known confounders RCT • Unknown confounders likely to be equally distributed Concealed randomization specifically removes the possibility of selection bias or confounding in RCTs, i.e. any differences between the groups are attributable to chance or to the intervention, all else being equal. Deeks et al, Health Tech Asess 2003 Based on assumption of randomization in infinite population, or opposite distribution of confounders if many trials examined Example: Confounder present in 20% of population. N= 400 95% Prob. Distr. = 15.6%-24.4%. If 5 confounders, 23% chance that at least one is outside the range (95% Prob. Dist. = 14.2%-25.8%) (Shrier et al, AJE 2007)

  36. RCT vs. Observational: Theoretical • All Studies • Adjust for known confounders There may be important prognostic factors that the investigators do not know about or have not measured which are unbalanced between groups and responsible for differences in outcome. Deeks et al, Health Tech Asess 2003 (Shrier et al, AJE 2007)

  37. OBJECTIVES How do we think? A Workshop Example: Does Stretching Prevent Injury? What parameter are you estimating? RCT vs Obs studies in meta-analyses It’s all about Bias!

  38. IF YOU WANT THE BLUE PILL…. turn on your IPOD now!

  39. FORMS OF BIAS Structural Approach to Bias • Confounding Bias • Failure to condition on a common cause • Do not condition on a variable (or marker of a variable) that lies along the causal pathway • Selection Bias • Conditioning on a common effect (Pearl, Hernán, Greenland)

  40. OA Activity X M Y X C Y Gait Disorder Osteoarthritis (indirect) Gait Disorder(direct) Activity X CONFOUNDING BIAS

  41. Smoking Spont. Abortion Previous Sp. Ab. CONFOUNDING BIAS? • Exposure: smoking • Outcome: spont. abortion • Confounding?: previous spont. abortion (Weinberg Am J Epid 1993)

  42. Tissue Abnormality Smoking Spont. Abortion Previous Sp. Ab. CONFOUNDING BIAS? • Exposure: smoking • Outcome: spont. abortion • Confounding?: previous spont. abortion • Underlying abnormality: intrinsic tissue abnormality (Weinberg Am J Epid 1993)

  43. Tissue Abnormality CONFOUNDING BIAS? • Exposure: smoking • Outcome: spont. abortion • Confounding?: previous spont. abortion • Underlying abnormality: intrinsic tissue weakness Smoking Spont. Abortion X X Previous Sp. Ab. (Weinberg Am J Epid 1993)

  44. Smoking Spont. Abortion Tissue Abnormality X X Previous Sp. Ab. CONFOUNDING BIAS? • Univariate RR for smoking/no smoking =1.85 • Stratified RR • RR for smoking/no smoking (Previous Sp. Ab.) =1.32 • RR for smoking/no smoking (No Previous Sp. Ab.) =1.32 • However, whether or not someone had a previous spontaneous abortion does not change the effects of smoking • Including this covariate results in an invalid estimate (Weinberg Am J Epid 1993)

  45. C Ex Outcome C Ex U Outcome C Ex Outcome C Ex Outcome U U CONFOUNDING BIAS?    (Hernán Am J Epid 2002)

  46. U1 U2 C E Outcome Condition on C? (Cole & Hernán Int J Epid 2002)

  47. FORMS OF BIAS Structural Approach to Bias • Confounding Bias • Failure to condition on a common cause • Do not condition on a variable (or marker of a variable) that lies along the causal pathway • Selection Bias • Conditioning on a common effect (Pearl, Hernán, Greenland)

  48. Season X1 X3 X2 X4 X5 Sprinkler Rain Wet Slippery PEARL’S RULES - EXPLANATION Step 4: Connect any two parents sharing a common child. • Including “colliders” opens up path for confounding If one knows the value of the “collider”, the parents are associated. If wet: the sprinkler is more likely to be on if there was no rain. (Pearl. Causality Book)

  49. UNBIASED EFFECT ESTIMATE? X Outcome Which measurements should be included in the model if we are interested in the relation between X and Outcome? (Pearl. Causality Book)

  50. UNBIASED EFFECT ESTIMATE? Z1 Z2 X Outcome Which measurements should be included in the model if we are interested in the relation between X and Outcome? Do Z1 and Z2 remove confounding? (Pearl. Causality Book)

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