Transcript Densitometry and Diagnosis of Osteoporosis

Follow-up and compliance
• Compliance/adherence
– How to measure
– Why bother?
• Follow-up
– Importance of complete follow-up
– Analysis issues: ITT, etc.
Follow-up in RCT’s
• What happens after randomization
• Carefully lay out procedures to be followed
• Describe on forms and in Operations Manual
• First reaction: do everything on everyone at every visit
– e.g. labs at all visits
– But great opportunities for efficiencies
– Ask the following:
• Do only at some visits?
• Do only on a subset?
• Don’t do at all
Large and Simple Trials
• Get a whole lot of people
• Randomize, do as few follow-up measurements as
possible
• Difficult to carry out in practice
• Examples
– Physicians’ Health study: Randomize to aspirin or placebo, mail
out drugs, follow-up by mail
– Use data collected for other purposes for follow-up/endpoints
• Population mortality
• Medical info (Medicare, Kaiser)
Compliance or (mpc) adherence
• Trial is meaningless unless participants adhere to
interventions
• Two aspects
– 1. Adherence to medications/interventions
– 2. Adherence to visit schedules/reporting
• Lack of adherence leads to:
– Bias
– Decreased power
– Uninterpretable results
Effect of incomplete visit follow-up on
results in clinical trials
Fracture Intervention Trial (alendronate vs. placebo)
X-rays obtained at baseline, 2 years, 3 years
Vertebral fractures defined from changes in radiographs
FU radiographs on 97% of participants @ year 3
Time (yrs)
Relative risk (CI)
BL to 2
0.34 (66% reduction)
BL to 3
0.49 ( 51% reduction)
Effect of Incomplete Follow-up:
Virtual Experiment
• FIT I: Follow-up x-rays on 97% of surviving
participants at year 3
• What if follow-up less complete?
• Randomly “lose” 50% between year 2 and 3
Use of Survival Analysis for X-Rays
in FIT I:Virtual Experiment
Time (yrs)
Relative risk
2
0.34
3
0.49
3 (50% LTFU)
0.37
LTFU = Lost to follow-up
Effect of High Rate of Loss to
Follow-up on Results
• If early results differ from later results, could create bias
when comparing one study to another
• Even a “random” (therefore unbiased) loss to follow-up can
affect results
Measuring adherence
• Medication-taking
–
–
–
–
Just ask! (self report)
Pill counts
Biochemical assays for some drugs
High tech pill bottles
• Visit schedule
– N missed visits
– Visits within schedule
– etc.
Adherence goals
• Ideal: all participants continue to take medication
(perfectly) throughout the trial and attend all follow-up
visits until the very end
• Why might participants stop medication?
– Side effects (real or perceived)
– Complex regimens
– Want to take true active medication
• New info on old medication
• New competing medication
– Want to stop active medication
• New info on old medication (e.g, ERT increases BC risk)
Some Examples of “Bad Adherence Days”
• Women’s Health Initiative
– After first year, letter sent to all participants “observed a small
increase in cardiovascular disease among ppts on HRT”…
– Many stopped medications
• PROOF trial (effect of Calcitonin on osteoporosis)
– 1994 to 1999
– 1997: Alendronate approved with significant marketing and
excellent results
Effect of stopping medication:
Classical interpretation
• Placebo’s start active medication==>become more like
actives
• Actives stop active medication and start
“inactive”==>become more like placebo
• Two groups become more similar
• Treatment effect is underestimated/conservative
– Comforting
• “Classical interpretation” may not hold:
– Example: patients stop study meds to take a medication that is
better than active study medication
Strategies to enhance compliance
• Warm and fuzzy stuff
– Participants to feel appreciated
– Staff in clinic spend enough time
– Sensitive to ppts. scheduling needs
• Parties/events with all participants
• Ease of logistics/transportation to clinics
• Birthday cards
• Gifts
• Information, Newsletters, other
Strategies to enhance compliance II
• Most drop outs occur in early study period
– FIT (4 years total); 2/3 of drop outs occurred in first year, most of
those in first 6 months
• Make certain that ppt’s understand study requirements
• Run-in period
– Trial run of drug/treatment
– Typically 2-4 weeks, usually of placebo (not always)
– Value controversial
Study adherence: follow-up visits
• Goal: visits all on time (within window)
• Set appointments flexibly
• Reminders prior to appt.
• Give study calendar
• Listen to concerns/problems
Need for consideration of compliance:
Coronary Drug Project (CDP, NEJM 1980)
Clofibrate (n=1065)
5 year mortality
Overall Adherence
> 80% (2/3) < 80% (1/3)
18%
15%
25%
Need for consideration of compliance:
Coronary Drug Project (CDP, NEJM 1980)
Clofibrate (n=1065)
Placebo (n=2695)
5 year mortality
Overall
Adherence
> 80% (2/3) < 80% (1/3)
18%
15%
25%
19%
15%
28%
Lessons
• Unknown/unmeasured confounders associated with
compliance
• Differ in placebo and active groups
Adherence of medication is not the same as
adherence to visit schedule
• “Drop out” is very vague term
• Can have perfect visit adherence (come to all visits on
time) but-– Not take a single study med pill
– Take only 60% of pills
• If miss visits or stop coming to visits, then generally
don’t take study medication
– Exceptions do occur: Trial of once-yearly infusion treatment.
May have perfect medication compliance but poor visit
compliance
Follow-up visits for those who have stopped
study medications?
• Practice varies dramatically across studies
• Option 1: Stop follow-up as soon as drug stops
• Option 2: Continue to collect follow-up info
• Advantages of each
– ??
Follow-up visits for those who have stopped
study medications?
• Practice varies dramatically across studies
• Option 1: Stop follow-up as soon as drug stops
• Option 2: Continue to collect follow-up info
• Advantages of each
– O-2: Biased per previous slides (generally conservative)
– O-1: Biased, but cannot predict direction
– Choice related to analysis (ITT)
Intention to Treat Analysis (ITT)
• ITT coined by AB Hill in textbook on Stat (1961)
• One of the main Commandments of RCT bible
• Original definition “All subjects will be analyzed
according to the treatment group they were originally
intended by the randomization process”
• All: Analyze even if no pills taken or later found to be
ineligible…
• Originally intended: Regardless of compliance, analyze
according to original assignment.
– Alternative: randomized to treatment, took no pills. Analyze as a
placebo
Beware of “we did an ITT analysis”
• Generally considered sacred, almost god-like virtue
• The term “ITT” used differently in different studies
• ITT does NOT always mean that people were followed
beyond stopping study medications
• Examples where ITT may not guarantee holiness:
– Patient stopped meds after 1 week and she was discontinued
from study (including further follow-up) at that time.
– Patient stopped meds after 1 week and follow-up continued. But
in analysis, only follow-up until stopped meds is counted.
Alternatives in Analysis
• per protocol or as treated analysis
• If all ppts. are followed regardless of adherence to
medications, several types of options
• Include only those patients who took all study
medications and completed all protocol visits (still ITT)
• Include all patients but only for the time that they
remained on study medications (still ITT)
• If obtain complete follow-up on all ppts., can run several
different types of analyses and any discrepancies could
be informative.
Analysis based on post-randomization
variables
• Per-protocol limits analysis to adherers
• Per-protocol is one example of analysis which stratifies
based on post-randomization experience
– Other examples?
• More generally, subgroup analyses by post-rand. factors
are biased
Problems with ITT/full follow-up approach
• ITT/full follow-up not holy grail
• Does not estimate full biologic efficacy of
drug/intervention
– Advising individual patients may depend on efficacy
– Utility underestimated
• May be anti-conservative for adverse effects
– per-protocol may be preferred
Subgroups
• After primary analysis, want to look at subgroups
• Does effectiveness vary by subgroup
• If drug effective, is it more effective in some
populations?
• If results overall show no effect, does drug work in
subgroup of participants?
Example: Efficacy of alendronate
• FIT II: Women with BMD T-score < -1.6
(osteopenic--only 1/3 osteoporotic)
– Women without existing vertebral fractures (2)
• Overall results: 14% reduction, p=.07
• Wimpy
RR for clinical fracture of alendronate
(FIT II, Cummings, JAMA 1999)
Relative Risk
1.5
0.86
(0.73 - 1.01)
1
B
B
B
0
Overall
P=0.07
RR for clinical fracture of alendronate
by baseline BMD groups
1.03
Relative Risk
1.5
(0.77B - 1.39)
0.86
(0.73 - 1.01)
1
1.14
(0.82B - 1.60)
B
B
B
B
B
B
B
B
B
B
0.64
(0.50 - 0.82)
????
0
Overall
T < -2.5
-2.5 < T < -2.0
T > -2.0
Baseline Femoral Neck BMD, by T-score
Subgroup analysis in HERS
• Overall no effect of HRT or perhaps harm in year 1
• Is there a subgroup who benefit?
• Is there subgroup with significant harm?
• Look at relative hazard (RH) within subgroups defined by
baseline variables
–
–
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–
Medication use at baseline
Prior disease
Health habits
Compare RH in those with and without risk factor
• RH in those using beta blockers compared to those not using
• RH > 1 ==> harm
• Get p-value for significance of difference of RH in those w and
without
HERS: 4 years of HRT increased
then decreased CHD Events
Year
E+P
1
57
2
3
4+5
>5
RH
p-value
38
1.5
.04
47
48
1.0
1.0
35
41
0.9
.6
49
0.7
.07
33
Placebo
???
P for trend = 0.009
Subgroups: the final frontier in HERS
•
Relative hazard (E vs. placebo)
Subgroup
Within
Among
Subgroup
Others
1712 (62)
1.01
3.39
.01
current smoker
360 (13)
0.55
1.92
.03
digitalis use
275 (10)
4.98
1.26
.04
1616 (58)
1.09
2.72
.04
775 (28)
2.97
1.14
.05
1409 (51)
2.14
0.93
.05
899 (33)
2.89
1.15
.06
1019 (37)
2.65
1.14
.06
Subgroup
history of smoking
>= 3 live births
lives alone
prior mi by chart review
beta-blocker use
age >= 70 at randomization
N (%)
p*
Lots of subgroups were analyzed in HERS
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history of smoking (at rv)
1712 (62)
current smoker (at rv)
360 (13)
digitalis use (at rv)
275 (10)
>= 3 live births
1616 (58)
lives alone (at rv)
775 (28)
prior mi by chart review (cr)
1409 (51)
beta-blocker use (at rv)
899 (33)
age >= 70 at randomization
1019 (37)
prior mi in most distant tertile
447 (16)
walk 10m or in exercise program (at rv) 1770 (64)
prior ptca by chart review (cr)
1189 (43)
prior mi within 2 years
420 (15)
tg > median (at rv)
1377 (50)
rales in the lungs (at rv)
80 ( 3)
digitalis or ace-inhibitor use (at rv)
653 (24)
previous ert for >= 12 months
302 (11)
serious medical conditions
1028 (37)
age >= 53 at lmp
578 (21)
hdl > median (at rv)
1315 (48)
lp(a) > median (at rv)
1378 (50)
use of non-statin llm (at rv)
420 (15)
married (at rv)
1588 (57)
lvef <= 40%
178 ( 6)
prior mi within 4 years
765 (28)
previous ert use for >= 1 year
327 (12)
prior mi within 1 year
194 ( 7)
chest pain (at rv)
982 (36)
dbp >= 90 mmhg (at rv)
149 ( 5)
prior ptca within 1 year
206 ( 7)
prior mi within 3 years
612 (22)
prior ptca within 4 years
838 (30)
use of any llm (at rv)
1296 (47)
diuretic use (at rv)
775 (28)
signs and symptoms of chf (at rv)
118 ( 4)
ace inhibitor use (at rv)
483 (17)
total cholesterol > median (at rv)
1377 (50)
l-thyroxine use (at rv)
414 (15)
poor/fair self-rated health (at rv)
665 (24)
heart murmur (at rv)
540 (20)
sbp >= 140 mmhg (at rv)
1051 (38)
prior ptca within 3 years
695 (25)
s3 heart sounds (at rv)
19 ( 1)
htn by physical exam (at rv)
557 (20)
>= 2 severely obstructed main vessels
1312 (47)
statin use (at rv)
1004 (36)
have you ever been pregnant
2564 (93)
calcium-channel blocker (at rv)
1511 (55)
previous hrt for >= least 12 months
132 ( 5)
ldl > median (at rv)
1373 (50)
prior ptca within 2 years
475 (17)
baseline left bundle branch block
212 ( 8)
white
2451 (89)
ever told you had diabetes
634 (23)
aspirin use (at rv)
2183 (79)
any alcohol consumption (at rv)
1081 (39)
gallstones or gallbladder dis.
633 (23)
•
baseline atrial fibrillation/flutter
33 ( 1)
1.01
0.55
4.98
1.09
2.97
2.14
2.89
2.65
2.64
2.35
0.92
3.20
2.02
0.43
2.33
4.19
1.05
3.19
1.18
1.26
0.89
1.26
2.16
2.07
2.86
2.88
1.25
0.91
3.94
2.05
1.15
1.23
1.89
0.94
2.05
1.32
2.29
1.30
1.89
1.37
1.27
2.74
1.32
1.53
1.34
1.55
1.61
1.24
1.44
1.35
1.31
1.48
1.48
1.51
1.54
1.55
3.39
1.92
1.26
2.72
1.14
0.93
1.15
1.14
0.93
1.11
1.98
1.28
1.05
1.65
1.24
1.41
1.81
1.38
1.95
2.08
1.69
1.98
1.01
1.32
1.41
1.43
1.88
1.62
1.46
1.37
1.70
1.76
1.33
1.60
1.40
1.80
1.43
1.72
1.42
1.72
1.61
1.50
1.62
1.26
1.59
1.15
1.38
1.60
1.63
1.56
1.55
1.62
1.53
1.56
1.57
1.52
-
1.50
0.30
0.29
3.96
0.40
2.60
2.30
2.51
2.32
2.82
2.12
0.46
2.50
1.93
0.26
1.88
2.98
0.58
2.31
0.61
0.60
0.52
0.64
2.13
1.57
2.03
2.02
0.67
0.56
2.71
1.50
0.68
0.70
1.42
0.58
1.46
0.74
1.60
0.76
1.34
0.80
0.78
1.82
0.81
1.22
0.84
1.35
1.17
0.78
0.89
0.87
0.85
0.92
0.97
0.97
0.98
1.02
.01
.03
.04
.04
.05
.05
.06
.06
.07
.08
.08
.11
.12
.13
.16
.18
.21
.23
.24
.25
.25
.29
.31
.32
.32
.33
.33
.35
.38
.40
.40
.40
.41
.42
.44
.47
.47
.51
.53
.59
.62
.63
.64
.69
.71
.72
.73
.77
.77
.81
.82
.88
.94
.95
.97
.97
Total subgroups examined: 102
Total subgroups with p< .05: 6
-
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Subgroups: conclusions
• Subgroups are full of statistical problems
– Multiple comparisons may lead to erroneous conclusions
• Limited power in for subgroup analyses
• Subgroups based on baseline variables are less bad
• Subgroups based on post-randomization variables is
more problematic
Follow-up and analysis: summary
• Best trial:
– All participants remain on medication
– All participants are followed until end of study
– Pre-planned analysis
• Where possible, minimize subjectivity and adhoc-ness