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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

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


Meta analysis sports medicine

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


Meta analysis sports medicine

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!


Meta analysis sports medicine

INTERPRETATIONS

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


Meta analysis sports medicine

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


Meta analysis sports medicine

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


Meta analysis sports medicine

HOW DO WE THINK?

Clue: want

  • crave, covet, yearn, fancy

(Vandenbroucke et al, 2001)


Meta analysis sports medicine

HOW DO WE THINK?

Clue: want

  • crave, covet, yearn, fancy

(Vandenbroucke et al, 2001)


Meta analysis sports medicine

HOW DO WE THINK?

Queues

Clue: want

  • crave, covet, yearn, fancy

(Vandenbroucke et al, 2001)


Meta analysis sports medicine

HOW DO WE THINK?

Queues

Clue: want

  • crave, covet, yearn, fancy

(Vandenbroucke et al, 2001)


Meta analysis sports medicine

HOW DO WE THINK?

Queues

Clue: want

  • crave, covet, yearn, fancy

(Vandenbroucke et al, 2001)


Meta analysis sports medicine

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!


Meta analysis sports medicine

Does Stretching Prevent Injury?

(adapted from Shrier, Evidence-Based Sports Medicine 2007)


Meta analysis sports medicine

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


Meta analysis sports medicine

Does Stretching Prevent Injury?

(adapted from Shrier, Evidence-Based Sports Medicine 2007)


Meta analysis sports medicine

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


Meta analysis sports medicine

Acute Stretching: Force (MVC/1RM)

% Unstretched Condition

(adapted from Shrier, Clin J Sport Med 2004)


Meta analysis sports medicine

Regular Stretching: Force (MVC/1RM)

PNF

Static

% Unstretched Condition

(adapted from Shrier, Clin J Sport Med 2004)


Meta analysis sports medicine

Slow (30-60 deg/s)

Fast (>180 deg/s)

Acute Stretching: Force (Isokinetic)

% Unstretched Condition

(adapted from Shrier, Clin J Sport Med 2004)


Meta analysis sports medicine

Slow (30-60 deg/s)

Fast (>180 deg/s)

Regular Stretching: Force (Isokinetic)

% Unstretched Condition

(adapted from Shrier, Clin J Sport Med 2004)


Meta analysis sports medicine

Acute Stretching: Jump Height

Static

CMJ

% Unstretched Condition

(adapted from Shrier, Clin J Sport Med 2004)


Meta analysis sports medicine

Regular Stretching: Jump Height

Static

CMJ

% Unstretched Condition

(adapted from Shrier, Clin J Sport Med 2004)


Stretching and force

  • 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)


Regular stretching force

  • 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)


Meta analysis sports medicine

Regular Stretching: Injury

(adapted from Shrier, Evidence-Based Sports Medicine 2007)


Meta analysis sports medicine

Acute Stretching: Injury

Excluding multiple co-intervention studies

(adapted from Shrier, Evidence-Based Sports Medicine 2007)


Meta analysis sports medicine

Acute Stretching: Injury

Excluding multiple co-intervention studies

(adapted from Shrier, Evidence-Based Sports Medicine 2007)


Meta analysis sports medicine

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


Meta analysis sports medicine

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


Meta analysis sports medicine

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!


Meta analysis sports medicine

Effect of RCT on Outcomes

Clinical Trials and Meta-Analysis 1994;29:41–47


Meta analysis sports medicine

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


Meta analysis sports medicine

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!


Meta analysis sports medicine

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)


Meta analysis sports medicine

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)


Meta analysis sports medicine

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)


Meta analysis sports medicine

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!


Meta analysis sports medicine

IF YOU WANT THE BLUE PILL….

turn on your IPOD now!


Forms of bias

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)


Confounding bias

OA

Activity

X

M

Y

X

C

Y

Gait Disorder

Osteoarthritis

(indirect)

Gait Disorder(direct)

Activity

X

CONFOUNDING BIAS


Confounding bias1

Smoking

Spont. Abortion

Previous Sp. Ab.

CONFOUNDING BIAS?

  • Exposure: smoking

  • Outcome: spont. abortion

  • Confounding?: previous spont. abortion

(Weinberg Am J Epid 1993)


Confounding bias2

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)


Confounding bias3

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)


Confounding bias4

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)


Confounding bias5

C

Ex

Outcome

C

Ex

U

Outcome

C

Ex

Outcome

C

Ex

Outcome

U

U

CONFOUNDING BIAS?

(Hernán Am J Epid 2002)


Condition on c

U1

U2

C

E

Outcome

Condition on C?

(Cole & Hernán Int J Epid 2002)


Forms of bias1

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)


Pearl s rules explanation

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)


Unbiased effect estimate

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)


Unbiased effect estimate1

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)


Unbiased effect estimate2

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?

If X is disconnected from Outcome (d-separation), there is no confounding

(Pearl. Causality Book)


Unbiased effect estimate3

UNBIASED EFFECT ESTIMATE?

Z1

Z2

X

Outcome

(Pearl. Causality Book)


Unbiased effect estimate4

UNBIASED EFFECT ESTIMATE?

Z1

Z3

Z2

X

Outcome

Which measurements should be included in the model if we are interested in the relation between X and Outcome? Do Z1, Z2 and Z3 remove confounding?

X is NOT disconnected from Outcome

INCLUDING Z3 INTRODUCES BIAS!

(Pearl. Causality Book)


Selection bias examples

SELECTION BIAS EXAMPLES

  • Observational Specific

    • Berkson’s Bias

    • Volunteer / Self-selection Bias

    • Healthy worker Bias

  • Meta-analysis specific

    • Reporting bias

    • Publication bias

  • RCT or Observational

    • Differential loss to follow-up

    • Non-response / Missing data bias

    • Adjustment for variables affected by previous exposure


Attrition bias

RCT Complex Attrition bias

Side effects

Drop Out

Treatment

Mild disease

Death

ATTRITION BIAS

Condition on common effect


Effect of bias

Probability of Bias

Biased Against Benefit

Bias Towards Benefit

Treatment Beneficial

Treatment Harmful

EFFECT OF BIAS

  • Can one sum probability distributions for different risks of bias?

    • Already being done “intuitively” and informally

    • Some beginnings: response-surface estimation (Greenland), multiple bias modeling (Greenland), adjusted likelihoods (Wolpert), bias against bias (Kaufman)


Estimating bias

Unknown Variable

ESTIMATING BIAS?

Censored

X

Outcome

(Pearl. Causality Book)


Summary

SUMMARY

  • Objective is to obtain an unbiased estimate of the parameter of interest

  • Study design is only one source of bias

  • Mathematics underlying statistical analyses do not care what the names of the nodes are

  • Causal maps make assessing bias more transparent

  • Meta-analyses should be able to treat all potential biases regardless of cause

    • Estimating the wrong parameter - RCT, ITT?

    • Conditioning on a variable that lies along the causal path or is a marker for a variable lying along the causal path

    • Absence of conditioning on a common cause

    • Conditioning on a common effect


Interpretations

INTERPRETATIONS


Meta analysis sports medicine

Canadian Academy of Sport Medicine

L’Académie Canadienne de Médecine du Sport


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