basic biostatistics in medical research lecture 2: … · biostatistics collaboration center ......
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Basic Biostatistics in Medical Research
Lecture 2: Two Group Comparisons
Leah J. Welty, PhDBiostatistics Collaboration Center (BCC)
Department of Preventive MedicineNU Feinberg School of Medicine
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Objectives
• Assist participants in interpreting statistics published in medical literature
• Highlight different statistical methodology for investigators conducting own research
• Facilitate communication between medical investigators and biostatisticians
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Two Group Comparisons• Compare on continuous or categorical?
– Compare SBP between two groups– Compare rate of Hodgkin’s between two groups
• Samples paired or independent?– Measure on right and left hand each subject– Measure right hand one group, left hand other group
• Parametric or nonparametric?– Data satisfy necessary assumptions or not
• Looking for association or agreement?– Tonsillectomy a risk factor for Hodgkin’s (association)– Two radiologists evaluating mammograms for cancer (agreement)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Two Group Comp (Association)
Continuous or Categorical
Paired or Independent
Pairedor Independent
Binary or > 2 Categories
Parametric or Nonparametric
Parametric or Nonparametric
Parametric or Nonparametric
Parametric or Nonparametric
Paired t-test
Wilcoxon Signed Rank Sum
Two Sample t-test
Mann Whitney Test(Wilcoxon Rank Sum)
McNemar’s Test
Exact Binomial Methods (Exact McNemar)Pearson’s chi-squared test
Fisher’s Exact Test
Parametric or Nonparametric
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Examples of Paired Data
• Each subject measured before and after tx
• Subjects recruited in matched pairs– match age, postal code, diagnosis– one of pair gets tx A, the other gets tx B
• Twins or child/parent pairs
• Lab experiment with control and treatment samples handled in parallel
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Paired Procedures: When to Use
• If experimental design is paired, should use stats that account for pairing.
• Pairing arranged before data collected.
• If use stats that don’t account for pairing on paired data, you’re throwing away good information!
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Independent Groups• Most common analyses for comparing two
independent groups of observations
• Observations in one group independent of observations in other group
• Many clinical trials – treatment group independent of control group
• Observational Studies– sample individuals independently of each other
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
SBP and OC Examples: Paired & Independent
• Longitudinal Study– Recruit nonpregnant, premenopausal women age 16-49 who are
not currently OC users– Measure their baseline BP– Re-screen 1 year later. Measure BP on women who have
become OC users.– Examine mean difference between baseline and follow-up SBP.– Values are paired
• Cross-Sectional Study– Recruit OC users and non-OC users among study population– Compare SBP of OC group to SBP of non-OC group– Groups are independent
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Continuous Variable Between
Two Paired Groups
Paramertic Methods (Comparing Means)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
OC and SBP Example I (Paired)
• Does OC use change SBP?Subj SBP pre OC SBP while OC Diff
1 115 128 132 112 115 33 107 106 -14 119 128 95 115 122 76 138 145 77 126 132 68 105 109 49 104 102 -2
10 115 117 210/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Paired t-test
• Mean difference = 4.8, sd difference = 4.6
• Estimated SE = SD/√n = 1.45
• Assuming differences in SBP are normally distributed, the mean differences for a sample of 10 has t9 distribution
• 95% CI: 4.8 ± 2.25 * SE = (1.5, 8.1) mm HgFrom t9 distribution
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Paired t-test Con’t• Hypothesis Test
H0: mean difference = 0 (no change in SBP)Ha: mean difference ≠ 0 (some change in SBP)
• If mean difference is 0, what is probability of observing a sample of 10 with a mean difference as or more extreme as 4.8 mm Hg?
p-value = 0.01
• Reject H0 in favor of Ha.
• Note: 0 not in 95% CI (1.5, 8.1) and p-value < 0.05
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Assumptions Paired t-test
• Assume differences are normally distributed (or n is large) for t-test
• Measurements themselves do not need to be normally distributed
• What if we can’t assume differences are normally distributed and/or n is not large?
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing Continuous Variable Between
Two Paired Groups
Nonparamertic Methods (Comparing Medians, Ranks)
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Wilcoxon Signed Rank Sum• Nonparametric analog to paired t-test
• For each pair, compute difference
• Rank (small to large) the absolute differences– Also ignore any differences = 0
• Add up ranks of positive differences, negative differences
• Compare the sums
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Wilcoxon Signed Rank Sum
Diff Absolute Diff Sign Ranks13 13 + 10
3 3 + 4-1 1 - 19 9 + 97 7 + 7.57 7 + 7.56 6 + 64 4 + 5
-2 2 - 2.52 2 + 2.5
sum “+” ranks = 51.5
sum “-” ranks = 3.5
• SBP and OC use
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Wilcoxon Signed Rank Sum
• If effect of OC on SBP, would we expect to see a difference in the rank sums as or more extreme?
• 3.5 vs. 51.5• p-value = 0.02
• Caveats for Wilcoxon Signed Rank Sum– p-value changes if transform data (e.g. take logs)– assumes differences symmetric about zero – approximate for ties in ranks– sometimes used for comparison of ordinal data
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Continuous Variable Between
Two Independent Groups
Paramertic Methods (Comparing Means)
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
OC and SBP Example II
• Does OC use change SBP?• Sample 1
– 8 35-39 year old women nonpregnant OC users• Sample 2
– 21 35-39 year old women nonpregnant not OC users
n mean sd
OC 8 132.86 mm Hg 15.34 mm Hg
non-OC 21 127.44 mm Hg 18.23 mm Hg
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
T-test for Independent Groups
• The difference in sample means will follow a tn1+n2–2 distribution IF– observations in pop 1 normally distributed– observations in pop 2 normally distributed– populations have same variance (or true std dev)
• n1 + n2 – 2 degrees of freedom• Estimated SE of difference in sample means:
2121
222
211 11
2)1()1(
nnnnsdnsdn
Pooled SD Analogous to √n for one sample10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
T-test for Independent Groups
• For OC and non-OC groups
Diff in means = 132.86–127.44 = 5.42 mm HgSE = 7.282 mm Hgn1 + n2 – 2 = 8 + 21 – 2 = 27t27 distribution
– 95% CI for mean diff:5.42 ± 2.052 * SE = (-9.5, 20.4) mm Hg
From t27 distribution10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Paired vs Independent Comparisons of SBP
• Paired– Eliminate heterogeneity in baseline SBP levels by
subtracting before and after values– Drift over time?
• Independent– Heterogeneity in baseline SBP levels implies
additional variation to account for– Two samples have different SBP levels because
• they’re different subjects• possibly because they differ by OC/non-OC use
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
T-test Assumptions
• Ways to test equal variance assumption– F-test with ratio of standard deviations
• t-procedures exist to compare means for populations with unequal variances– debate over usefulness
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Continuous Variable Between
Two Independent Groups
Nonparamertic Methods (Comparing Ranks)
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Mann-Whitney Test
• What if we can’t assume the underlying distributions are normal and/or n is small?
• Mann-Whitney U Test– Compares ranks between groups– Analogous to Wilcoxon Rank Sum
• Same result, slightly different method • Wilcoxon Rank Sum ≠ Wilcoxon Signed Rank Sum
– Complicated corrections if lots of ties
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Receptors on Lymphocytes
• Independent (unmatched) samples
• Number of receptors on lymphocytes
• small sample sizes
• Underlying distributions may not be normal
Control Drug
1162 892
1095 903
1327 1164
1261 1002
1103 961
1235 875
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Mann Whitney Test
• Rank all values (small to large) ignore grouping
• Sum ranks in each group
• Test compares sums of ranks
• Software or tables for p-values
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Mann Whitney Test
Control Drug
1162 (8) 892 (2)
1095 (6) 903 (3)
1327 (12) 1164 (9)
1261 (11) 1002 (5)
1103 (7) 961 (4)
1235 (10) 875 (1)
Sum of ranks = 56
Sum of ranks = 24
p = 0.015
(ranks shown in parenthesis)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Side Note on Mann Whitney Test
• Also useful for some ordinal or rank data– e.g. visual acuity
– Can assign ranks, but not take means
Treatment Group Control Group
20-30 20-40
20-20 20-240
…
20-100 20-60
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Binary Categorical Variable Between Two Groups
A Few More Summary Statistics
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Two Group Binary Comparison
• Typical data format
• Often coded by 0s and 1s
Subject Exposure Disease1 yes yes2 yes no3 no yes…n no no
Subject Exposure Disease1 1 12 1 03 0 1…n 0 0
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
2 x 2 Contingency Tables
• Classic Setup
• Is proportion diseased (binary variable) different between exposed and unexposed groups?
Exposed Unexposed
Disease A B A + B
Not Diseased C D C + D
A + C B + D A + B + C + D
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Additional Summaries: Relative Risk
• Cohort Study: Vitamin A & Blindness– Sommer & colleagues followed >25,000
children in Indonesia
– Prop died among Vit A = 101/12,991 ~ 0.8%– Prop died among Plcbo = 130/12,209 ~ 1.1%
Vitamin A PlaceboDied 101 130Survived 12,890 12,079
12,991 12,209
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Relative Risk
• Relative Risk (RR)
• RR = 0.8/1.1 = 0.73• Children taking Vitamin A were about 27% less likely to
die than those not taking Vitamin A.• Difference due to sampling variability, or is Vitamin A
protective for mortality?
RR = risk of disease in exposed grouprisk of disease in unexposed group
Vitamin A PlaceboDied 101 130Survived 12,890 12,079
12, 991 12,209
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Additional Summary: Odds Ratio
• Odds Ratio (OR)
• OR = (101/12,890)/(130/12,079) = 0.73• The odds of disease in the exposed group are
27% smaller than those for the unexposed group.
OR = odds of disease in exposed groupodds of disease in unexposed group
Vitamin A PlaceboDied 101 130Survived 12,890 12,079
12, 991 12,209
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Notes on the Odds Ratio• 0 < OR < ∞
– OR > 1 exposure has promoting effect for disease– OR < 1 exposure has protective effect for disease– OR = 1 no effect of exposure on disease
• If a disease is rare, OR ~ RR
• OR works for any study you can compute an RR for
• RR cannot be computed in a case-control study, OR can
• OR is modeled in logistic regression10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Binary Categorical Variable Between
Two Independent Groups
Parametric Methods(Comparing Proportions)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Hodgkin’s Example I
• Compare proportion Hodgkin’s for groups w/ and w/o tonsillectomies
• Suspect that tonsils are protective• Case-control study (controls unmatched)• Vianna, Greenwald, and Davies (1971)
Tonsillectomy Hodgkin’syes yesyes no…no no
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Hodgkin’s Example I Con’t
• prop of tonsillectomy among cases = 67/101 = 66%
• prop of tonsilectomy among controls = 43/107 = 40%
• OR of Hodgkin’s comparing tonsillectomy group to group w/ tonsils
= ( 67/ 43) / (34 / 64) = 2.93
• Note: we cannot compute relative risk (RR).
Tonsillectomy No TonsillectomyHodgkins 67 34Control 43 64
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Null Hypotheses• Two hypothesis tests
H0: no association Ha: assoc btw Hodgkin’s & tonsillectomy
Or (in some cases) equivalently
H0: prop of tonsillectomy in cases (p1)= prop of tonsillectomy in controls (p2)
Ha: proportion of tonsillectomy in cases (p1) ≠ prop of tonsillectomy in controls (p2)
• Pearson’s chi-squared for association is equivalent to normal theory method for testing difference in proportions (pooled version)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Null Hypotheses• Two hypothesis tests
H0: no association Ha: assoc btw Hodgkin’s & tonsillectomy
Or (in some cases) equivalently
H0: prop of tonsillectomy in cases (p1)= prop of tonsillectomy in controls (p2)
Ha: proportion of tonsillectomy in cases (p1) ≠ prop of tonsillectomy in controls (p2)
• Pearson’s chi-squared for association is equivalent to normal theory method for testing difference in proportions (pooled version)
(e.g. OR = 1)
(e.g. OR ≠ 1)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Pearson’s Chi-squared test
• Compute expected cell counts if independent• Observed:
• Expected:Tonsillectomy No Tonsillectomy
Hodgkins 53.4 47.6 101Control 56.6 50.4 107
110 98 208
Tonsillectomy No TonsillectomyHodgkins 67 34 101Control 43 64 107
110 98 208
Expected count = row total x column totaltotal
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Chi-squared statistic
• Compute Chi-squared statistic, based on weighted squared differences Σ (expected – observed)2/expected
• Our chi-squared statistic will have a Chi-squared distribution with 1 degree of freedom IF – expected counts all > 5
AND– total number of subjects > 20 or 40
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Chi-squared results for Hodgkin’s I
• Chi-squared statistic = 14.46• Smallest expected cell is ~ 47• Have more than 20 subjects
• Therefore our statistic has Chi-squared 1 distribution
0 14.26
p-value = 0.0002Therefore reject H0 in favor of Ha.
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Chi-squared results for Vitamin A
• Chi-squared statistic = 5.74• Smallest expected cell is ~ 112• Have (way) more than 20 subjects
• Therefore our statistic has Chi-squared 1 distribution
0 5.74
p-value = 0.016
Therefore reject H0 (Vitamin A no effect on mortality) in favor of Ha(assoc between Vitamin A and mortality).
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Notes on Chi-Squared Distribution
• Many different applications of Chi-squared– Testing categorical vars between two groups
• 2 x k tables• Similar to Chi-squared procedures for 2 x 2 tables
• Degrees of freedom depend on application– 2 x 2 contingency tables always 1 df
• Yates continuity correction (not recommended)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Binary Categorical Variable Between
Two Independent Groups
Nonarametric Methods(Comparing Proportions)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Alternative to Chi-squared
• What if– Expected cell count(s) ≤ 5?– Total subjects ≤ 20?
• Traditional advice: Fisher’s Exact Test– Nonparametric Alternative to Chi-squared
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Fisher’s Exact Test
• Without changing row or column totals, create all other possible tables– Observed
– Another possibility
Tonsillectomy No TonsillectomyHodgkins 67 34 101Control 43 64 107
110 98 208
Tonsillectomy No TonsillectomyHodgkins 30 71 101Control 80 27 107
110 98 208
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Fisher’s Exact Test
• What percent of tables have association as strong or stronger than ours?
Generated table:
p-value = 0.0002
OR = 0.14 Tonsillectomy No TonsillectomyHodgkins 30 71 101Control 80 27 107
110 98 208
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Chi-squared vs. Fisher’s Exact• Fisher’s Exact Test computationally intensive
• Fisher’s Exact Test and the Chi-squared test will give (approximately) the same p-values if Chi-squared test valid
• OLD ADVICE:
– For small-medium data sets use Fisher’s Exact
– For large samples (1000s) use Chi-squared
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
• NEW ADVICE
– Many times, Fisher’s exact test will give “the right” value
– Fisher’s exact test conditions on marginal counts that may not be fixed by design
• e.g. numbers of Hodgkin’s and controls were (approximately) fixed, but the tonsillectomy counts were not
– Possible p-values become discrete (e.g. either p < 0.03 or p > 0.08)
– If sensitive/important/borderline, talk to a statistician
Chi-squared vs. Fisher’s Exact
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Comparing a Binary Categorical Variable Between
Two Paired Groups
Parametric Methods(Comparing proportions of discordant pairs)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Hodgkin’s Example II• Again Hodgkin’s and tonsillectomies
• Case-control study (controls matched)– 85 Hodgkin’s who had sibling w/in 5 yrs age and same sex– sibling was matched control
• Johnson & Johnson (1972)
Tonsillectomy(Hodgkin’s) Tonsillectomy(Sibling)yes yesyes no…no no
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
WRONG Analysis Hodgkin’s II
• Create 2 x 2 contingency table
• Pearson’s chi-squared test• Chi-squared statistic = 1.53• p-value = 0.22• No evidence tonsillectomy assoc w/ Hodgkin’s• Contradicts earlier study!
Tonsillectomy No TonsillectomyHodgkins 41 44Control 33 52
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
INCORRECT SLIDEWhat went wrong?
• Contingency table ignored pairings!
• Better contingency table shows pairings
Tonsillectomy No TonsillectomyHodgkins 41 44Control 33 52
Tonsillectomy No TonsillectomyTonsillectomy 37 7No Tonsillectomy 15 26
Sibling
Hodgkin’s
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
What went wrong? (correction shown in green)
• Contingency table ignored pairings!
• Better contingency table shows pairings
Tonsillectomy No TonsillectomyHodgkins 41 44Control 33 52
No Tonsillectomy No TonsillectomyNo Tonsillectomy 37 7No Tonsillectomy 15 26
Sibling
Hodgkin’s
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
McNemar’s Test Hodgkin’s II
• The concordant pairs have the same exposure (tonsillectomy, tonsillectomy) or (no, no) & don’t tell you anything about the association between exposure & disease.
• Need to look at the discordant pairs.No Tonsillectomy Tonsillectomy
No Tonsillectomy 37 7Tonsillectomy 15 26
Sibling
Hodgkin’s
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
McNemar’s Test Hodgkin’s II
• Compare – prop of pairs in which sibling had
tonsillectomy but patient did not 7/85 = 8%TO
– prop of pairs in which sibling did not have tonsillectomy but patient did 15/85 = 17%
No Tonsillectomy TonsillectomyNo Tonsillectomy 37 7Tonsillectomy 15 26
Sibling
Hodgkin’s
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
McNemar’s Test Hodgkin’s II• If there was no association between tonsillectomy, we would expect
these 7 + 15 pairs to be equally divided between the two cells (so expect 11 in each).
• Is the distribution of the discordant pairs extreme enough for us to reject the null?
• p = 0.09
• Less doubt about previous results?
No Tonsillectomy TonsillectomyNo Tonsillectomy 37 7Tonsillectomy 15 26
Sibling
Hodgkin’s
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
McNemar’s Test
• Confession: McNemar’s also uses a chi-squared distribution, but different than previous calcs
• What to do if # discordant pairs ≤ 10?
• Nonparametric versions of McNemar
– Using exact binomial procedures– Sometimes called “Exact McNemar”– Use computer program, talk to statistician
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Testing for Agreement
Categorical Variable (Kappa)Continuous Variable (Intraclass Correlation)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Agreement on Categorical Variable
• Two radiologists examine 100 mammograms
• Rate by ‘normal’ or ‘not normal’• Want measure of agreement, not
association• Chi-squared and McNemar for association• Use Kappa statistic
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Agreement on Categorical Variable
• Observed concordance = (35 + 35)/100 = 70%
• Expected concordance (if independent) = 45/100 * 55/100 + 55/100 * 45/100 = 49.5%
• Kappa = (obs conc – exp conc) / (1 – exp conc) = 0.406
Normal Not NormalNormal 35 10Not normal 20 35
Radiologist 2
Radiologist 1
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Agreement on Categorical Variable
• 0 ≤ Kappa ≤ 1• Reproducibility Guidelines
Kappa > 0.75 Excellent0.40 ≤ Kappa ≤ 0.75 Good
0 ≤ Kappa < 0.40 Marginal/Poor
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Agreement on Continuous Variable
• Blood pressure highly variable w/in person
• How does measurement of BP at a single doctor’s visit relate to ‘true’ BP (i.e. average BP over a period of time)?
• Compute intraclass correlation, or reliability coefficient (rho)
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Agreement on Continuous Variable
• (Random effects ANOVA)• Reproducibility interpretation
rho < 0.40 Poor0.40 ≤ rho < 0.75 Fair/Good
rho ≥ 0.75 Excellent
• rho for BP at single visit and “true” BP = 0.89• rho for average BP at 3 visits and “true” = 0.92
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Agreement on Continuous Variable
• Cannot use `regular’/Pearson correlation!
Person 1 2 3 4 5 6 7 8 9 10Single BP 80 81 82 83 84 85 86 87 88 89`True’ BP 90 91 92 93 94 95 96 97 98 99
• Pearson correlation = 1• But agreement is poor; rho = 0.15
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Brief words on power and sample size
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Why power/sample size?
• Granting agencies require a justification of sample size.
• Too much power (e.g. too many N) may be more costly, and may claim “significant” results that are not clinically relevant.
• A study that lacks power (e.g. not enough N) will not be “significant” – even if results are clinically meaningful. There is a known publication bias against studies with negative findings.
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Fundamental point
• [Studies] should have sufficient statistical power (usually 80%) to detect differences considered to be of clinical interest between groups.
• To be assured of this without compromising levels of significance, a sample size calculation should be considered early in the planning stages.
Friedman, L.M., Furberg, C.D., and DeMets, D.L. Fundamentals of Clinical Trials, 3rd Edition. New York: Springer-Verlag, 1998.
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Power and Sample Size
Power is related to a testing a specific hypothesise.g. clinical trial (Is drug A better than drug B?)
For descriptive studies, there may be no central hypothesis e.g. estimate the prevalence of autismbase sample size calculations on margin of error
In practice, the power section of grants is typically some combination of both.
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Power Defined
Power = the probability that you reject the null hypothesis, given that the (specific)
alternative is true
= Pr (reject H0 | H1 true)
Acceptable power generally 80-90%. If your alternative hypothesis is true, you want to have a ‘good chance’ of detecting it.
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What you need for power/sample size
1. Null hypothesis and (a specific) alternative hypothesis.
2. The appropriate statistical method to test the null hypothesis.
3. Effect size, or variability
4. Level of statistical significance (usually α = 0.05; this should be decided before starting a study)
5. EITHER power or sample size (solve for the other)
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Power Example: Smoking & Depression
Research Question: Do elderly smokers have a greater incidence of depression than elderly nonsmokers?
Literature Review:5-year incidence of depression among elderly nonsmokers is 0.20.
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis
3. Effect size, or variability
Incidence among elderly nonsmokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squared test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
Two-sided alternative
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squared test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squared test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
Talk to your friendly neighborhood
statistician
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squared test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squared test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
From literature, your past studies, pilot data, educated guess. Cannot come from the study you’re trying to power!
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squred test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squred test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)Typically 0.05. Sometimes 0.01, for example some clinical trials.
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squred test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Power/Sample Size Example1. Null hypothesis and (a specific) alternative hypothesis.
H0: incidence of depression is same in elderly smokers and elderly nonsmokers
H1: incidence of depression is different in elderly smokers and elderly nonsmokers
2. The appropriate statistical method to test the null hypothesis chi-squred test
3. Effect size, or variability
Incidence among elderly non-smokers = 0.2Incidence among elderly smokers = 0.3
4. Level of statistical significance α = 0.05
5. EITHER power or sample size 80% power (1 – power = β = 20%)
Usually 80% or 90%.
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Power/Sample Size Example
• Your friendly neighborhood statistician
• Software (SAS, STATA, R, PASS)
• Tables • Simulations
#1 - 5 Sample size or power
293 elderly nonsmokers & 293 elderly smokers
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Power depends on …• The effect you are trying to detect
smaller effect → harder to detect → larger n
• The level of significanceharder to reject the null (α = 0.01, say), then power decreases
• The sample sizebigger n → more power
• The design of the studySome designs (e.g. pairing) can get you more power for the same n
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Recommendations
• Well defined research question(s).
• Descriptive or hypothesis driven?
• Identify relevant statistical method
• Literature or pilot studies for effect sizes
• Talk to your friendly neighborhood statistician
10/11/11 Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Next Time
Linear RegressionLogistic Regression
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Useful References• Intuitive Biostatistics, Harvey Motulsky, Oxford
University Press, 1995– Highly readable, minimally technical
• Practical Statistics for Medical Research, Douglas G. Altman, Chapman & Hall, 1991– Readable, not too technical
• Fundamentals of Biostatistics, Bernard Rosner, Duxbury, 2000– Useful reference, somewhat technical
10/11/11
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Biostatistics Collaboration Center (BCC) Lecture 2: Two Group Comparisons
Contact BCC
• Please go to our web site at:
• Fill out our online request form for further collaboration!
http://www.feinberg.northwestern.edu/depts/bcc/
10/11/11