Download Additional file 2 Virological and immunological responses to

Additional file 2
Virological and immunological
responses to efavirenz or boosted
lopinavir as first-line therapy for
patients with HIV
Jim Young, Heiner C Bucher, Huldrych F Guenthard, Martin Rickenbach, Christoph A
Fux, Bernard Hirschel, Matthias Cavassini, Pietro Vernazza, Enos Bernasconi, Manuel
Battegay and the Swiss HIV Cohort Study
Antiviral Therapy 2009 14:777–779 (doi: 10.3851/IMPxx)
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Supplementary material
Here we give minimum rates of increase in CD4 cell count that at least 80% of patients
should achieve (calculated from EuroSIDA statistics), describe the two sets of inverse probability
weights used for confounder control in our Cox models, and give the results of three sensitivity
analyses.
Appendix 1: Minimum rates of increase in CD4 cell count
From tabled means and confidence intervals (Table 3 in [1]), we calculate minimum rates of
increase in CD4 cell count that at least 80% of patients should achieve (Table A1). We assume that
individual annual rates of increase follow a normal distribution with mean and variance calculated from
the 95% confidence interval for the mean of this distribution and from the number of viral load pairs
used to estimate this confidence interval. Because individual annual rates are much more variable than
the tabled mean annual rates, these individual rates of increase are always negative and hence represent
a decline in CD4 cell count that should not be exceeded. Note too that our calculation gives minimum
individual rates of increase per year, but the median time between measurements in our study is
approximately three months. So for example, patients starting therapy with a CD4 cell count ≤200
cells/mm3 would be classed as immunologic failures if their CD4 count declined by 47 cells/mm3 or
more over a three month period on two consecutive occasions during the first year of therapy.
Note that there is a typographical error in the second row of Table 3 in [1] (Mocroft, A;
personal communication, 13 May 2009). In this row – for patients starting therapy with a CD4 cell
count ≤200 cells/mm3 during the period from one to three years after starting therapy – the mean (95%
confidence interval) annual rate of CD4 cell count increase is given as 69 (63 to 76) cells/mm3. This
should read 63 (49 to 76) cells/mm3.
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Table A1. Minimum rates of increase in CD4 cell count (cells/mm3) that at least 80% of patients should
achieve calculated from EuroSIDA statistics (see Table 3 in [1]*).
CD4 cell count
Number of years
Mean annual rate of increase
Individual rate of increase
when starting
since starting
from Table 3 in [1]
achieved by at least
therapy
therapy
≤200
>200 to ≤350
>350
80% of patients^
95 % CI
n
Annual
3 months
≤1
53-99
711
-187
-47
>1 to ≤3
49-76*
2124
-204
-51
>3 to ≤5
36-69
2109
-273
-68
>5
18-46
2164
-248
-62
≤1
91-166
539
-245
-61
>1 to ≤3
25-74
1158
-308
-77
>3 to ≤5
24-69
1088
-272
-68
>5
2-44
1184
-287
-72
≤1
37-144
343
-335
-84
>1 to ≤3
18-82
851
-351
-88
>3 to ≤5
-17-51
791
-394
-98
>5
-12-54
747
-366
-92
* There is a typographical error in the second row of Table 3 in [1] (Mocroft, A; personal
communication, 13 May 2009): the mean (95% CI) annual rate of increase is given as 69 (63 to 76),
but should read 63 (49 to 76).
^ Assumes that individual annual rates of increase follow a normal distribution with mean and variance
calculated from the 95% confidence interval (95% CI) for the mean of this distribution and from the
number of viral load pairs (n) used to estimate this confidence interval.
Appendix 2: Inverse probability weights
The following two sets of inverse probability weights are multiplied together for confounder
control. However when calculating an estimate equivalent to that reported in ACTG 5142, we use just
the first set of weights to adjust for differences between the patients starting each therapy without using
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the second set of weights to adjust for informative censoring because missing data (due to patient drop
out or censoring) were ignored in the ‘intent-to-treat’ analysis of this trial [2].
The first set of inverse probability weights are point treatment weights that adjust for
differences at baseline between the patients starting each of the two therapies. We fit a logistic
regression model for the probability of receiving therapy, with one therapy or the other as the response
and as predictors: gender, likely mode of transmission and at baseline, advanced HIV infection (CDC
group C), chronic hepatitis B infection, hepatitis C infection, age, viral load, CD4 cell count and
number of years since 1998. Note that this, our full set of covariates, is the same set as used in two
previous cohort studies, except in our study there is no need to adjust for differences in the NRTI
backbone and in one of the two previous studies no adjustment is made for co-infection with hepatitis
[3,4]. The resulting weights are the inverse of the probability of receiving the therapy the patients
initially started, stabilised by multiplying this weight by the proportion of patients in the study on that
therapy [5].
The second set of inverse probability weights are time dependent weights that adjust for
differences between the patients who remain in the analysis and the patients who are censored. We fit a
logistic regression model for the probability of remaining in the analysis each month, with whether a
patient is censored that month or not as the response and the same full set of covariates as predictors
plus therapy at baseline and the most recent measurements of viral load and CD4 cell count. The
resulting weights are the inverse of the probability of remaining in the analysis in that month and in all
previous months, stabilised by multiplying this weight by the same probability calculated without
including the last two (time dependent) covariates [6].
Appendix 3: Patients starting therapy after 15 January 2001
In a first sensitivity analysis we restrict our data to patients starting therapy after 15 January
2001 when LPV/r became widely available with the cost of its use covered by the Switzerland’s
compulsory health insurance. Use of EFV has been covered by Swiss health insurance since 1 February
1999 and so the first patients receiving EFV might have viral load measured by less sensitive methods,
and then virologic failure for such patients might not be detected or might take longer to detect.
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In 1999, only 54% of viral load measurements were made using an ultra-sensitive method
capable of detecting a viral load of 50 copies/ml. But from 2001 on, after LPV/r became available, at
therapy (Figure A1). Restricting our analysis to patients starting therapy after 15 January 2001 gives a
0.8
0.9
1.0
sample of 1025 patients and a HR for the effect of therapy on virologic failure of 0.56 (0.44-0.71).
0.6
0.7
EFV
LPV/r
0.5
Proportion of measurements made using an ultra-sensitive method
least 80% of viral load measurements were made using an ultra-sensitive method for patients on either
1998
2000
2002
2004
2006
2008
Year
Figure A1. Proportion of viral load measurements made each year using an ultra-sensitive method for
patients starting first therapy with either efavirenz (EFV) or lopinavir boosted with low
dose ritonavir (LPV/r) plus lamivudine and zidovudine (3TC and AZT).
Appendix 4: Adding prior information
In a second sensitivity analysis we add prior information to our Cox models. This can be
particularly helpful when estimating unstable model coefficients such as interaction terms [7]. One can
impose a prior on a model coefficient by adding a pair of prior observations to the data and adding a
new covariate to a logistic regression to indicate that these observations are prior data, not real data [7].
As a prior for the relative effect of therapy on virologic failure, we use a HR of 0.60 (0.400.90), similar to the estimate from ACTG 5142 (0.63, 0.45-0.87) [2] but slightly less precise. As a prior
for the interaction between therapy and baseline CD4 cell count, we use a HR of 1.00 (0.25-4.0) to
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represent both our expectation that any modifying effect will be weak at best and our lack of
information about the direction of any modifying effect [8]. With these priors, the posterior HR for
therapy is 0.62 (0.52-0.74) and the posterior estimate for the interaction term shows that for each 100
cells/mm3 decrease in baseline CD4 cell count, this HR is then multiplied by a factor of 1.00 (0.901.11).
In ACTG 5142, greater CD4 cell increases are reported for patients on LPV/r but no hazard
rates are reported for immunologic events [2]. Therefore as a prior for the relative effect of therapy on
immunologic failure, we use a HR of 2.0 (0.5-8.0) to represent our expectation that this ratio is
probably positive (with an 83% probability of being above 1 [7]). As a prior for the interaction between
therapy and baseline CD4 cell count, we again use a HR of 1.00 (0.25-4.0) to represent both our
expectation that any modifying effect will be weak at best and our lack of information about the
direction of any modifying effect [8]. With these priors, the posterior HR for therapy is 0.70 (0.520.94) and the posterior estimate for the interaction term shows that for each 100 cells/mm3 decrease in
baseline CD4 cell count, this HR is then multiplied by a factor of 1.29 (1.14-1.46).
We can also ask what sort of prior would be needed before these data would not provide
convincing evidence of an interaction between baseline CD4 cell count and therapy [9]. In fact we
would have to be very sceptical about an interaction between baseline CD4 count and therapy (with
prior HR 1.00, 0.91-1.09) before these data would not lead us to accept that the relative benefit of
therapy for immune recovery depends on a patient’s CD4 cell count when starting therapy (posterior
HR 1.09, 1.00-1.19). Hence this modifying effect of baseline CD4 cell count is well supported by the
data and probably not due to some artefact of the data or the instability of estimates.
Appendix 5: Virologic and immunologic events analysed in other studies
In a third sensitivity analysis we consider virologic and immunologic events that have been
analysed in other studies. When comparing our results with others, note that some studies use LPV/r as
the reference therapy as we do [2,4], while others use EFV [3,10].
Our estimate of the relative effect of therapy on the risk of virologic failure (0.64, 0.51-0.80)
is similar to the estimate reported in ACTG 5142 (0.63, 0.45-0.87 [2]). Our estimate remains the same
even if we use an analytic method equivalent to the method used in this trial (using the first set of
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weights to adjust for differences between the patients starting each therapy without using the second set
of weights to adjust for informative censoring; adding covariates for high baseline viral load and
coinfection with hepatitis B or C). The definition of virologic failure used in this trial is not suitable for
our data – because patients followed for longer in our study are less likely to fail to suppress viral load
below 200 copies/ml. However other definitions of virologic failure used in previous longitudinal
cohort studies also suggest that immunologic failure is less likely for patients starting first therapy with
EFV. Estimates of the relative effect of therapy are 0.69 (0.55-0.88) with virologic failure defined as
the first of two consecutive measurements above 50 copies/ml after at least six months of therapy, or
0.81 (0.62-1.07) if the threshold for failure is raised from 50 to 400 copies/ml.
Previous cohort studies have had a virological follow up of around 1000 patient years [3,4],
and figure 1 suggests little difference between therapies in the probability of virologic failure during
the first 20 months of therapy. If we restrict our data to the first 1000 patient years of follow up, our
estimates for these two definitions are very imprecise and therefore inconclusive (1.60, 0.92-2.77 and
1.67, 0.86-3.23 for thresholds of 50 and 400 copies/ml respectively). Estimates reported in previous
cohort studies suggest no difference between the two therapies in the risk of virologic failure (1.08,
0.89-1.30 and 1.16, 0.58-2.32 for thresholds of 50 and 400 copies/ml respectively) [3,4].
Note that an even higher threshold for virologic failure of 500 copies/ml was used in a study
of drug resistance in the SHCS [11]. This study included treatment naive patients starting therapy with
any PI or NNRTI plus any two NRTIs. With boosted PIs as the reference, the adjusted HR for the
virologic failure of NNRTIs in this study is 0.84 (0.50-1.41 – see [11], p. 1786). Hence our patients are
a subset of the patients included in this earlier study and our estimate (HR 0.64, 0.51-0.80), contained
entirely within the confidence interval of this earlier estimate, is more precise because here we focus on
just two specific therapies.
Previous cohort studies consider immune recovery events such as an increase of 100 or 200
CD4 cells/mm3 above baseline [3,4]. These events occur relatively quickly for most of our patients,
with median time of 4 and 3 months to an increase of 100 cells/mm3 for patients starting EFV and
LPV/r respectively, and 11 and 7 months to an increase of 200 cells/mm3 for patients starting EFV and
LPV/r respectively. Using these events, we find no evidence that immune recovery is more likely under
one therapy or the other. Estimates of the relative effect of therapy are 1.02 (0.87-1.19) and 0.96 (0.82-
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1.11) for an immune recovery of 100 and 200 cells/mm3 respectively, similar to those reported in
previous cohort studies (0.90, 0.77-1.05 and 0.93, 0.66-1.30 respectively) [3,4].
Previous studies comparing patient subgroups report inconsistent results [2,4,12,13].
Virologic failure was less likely with EFV only in patients with a baseline viral load of 100,000
copies/ml or more [2]; but patients were more likely to achieve an undetectable viral load with EFV
only when starting therapy with a viral load of less than 100,000 copies/ml [13]. With our data, the
estimate of the interaction between the effect of therapy and baseline viral load shows that for each log
10 increase in baseline viral load, the HR for therapy (0.64, 0.51-0.81) is multiplied by a factor of 0.89
(0.70-1.08). Here there is no clear evidence that the effect of therapy on the risk of virologic failure
depends on the viral load when starting therapy, although our estimate of this interaction is rather
imprecise. Our results are consistent with data comparing EFV based therapy with triple NRTI based
therapy where the relative risk of virologic failure seems relatively independent of pre-treatment viral
load and CD4 cell count [14].
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