in simple to understand terms, if someone can?
I read about it, but i think i need a simpler language version
I have to present a journal article, and the authors used a LMM to do the statistics between treatment results in 3 different grps of patients who received 3 different drugs in sequence.
I just need to explain to my audience what they did with this LMM, why they did it, etc, in simple terms.
here's the passage from the article where they talk about it:
Statistical analysis
The preplanned main analysis compared pain scores for
combination treatment versus monotherapy for patients
on the maximum tolerated dose. On the basis of previous
variance estimates and accounting for two pairwise
comparisons (combination treatment vs gabapentin or
nortriptyline), we calculated that 40 patients would
provide an 80% probability of detecting a mean diff erence
between treatments of about half of a clinically
signifi cant20 amount of pain reduction (α=0·05, twosided).
Dropout rates in previous studies were about 10%
per 4–6-week treatment period, therefore we anticipated
that enrolment of 58 patients would yield 40 who
completed the study.
Patients completing at least two study treatment
periods (providing one pairwise comparison) were
included in the effi cacy analysis; analysis was by intention
to treat. Patients receiving at least one dose of any study
drug were included in analyses of adverse events. Mean
pain intensity was calculated from patient diaries while
patients were on the maximum tolerated dose. For
inclusion, more than 50% of the scores had to be
available; otherwise, the mean daily pain intensity was
treated as missing. A linear mixed model21 was formed
with fi xed eff ects of drug treatment, treatment sequence,
treatment period, and the fi rst-order carryover term, and
the random eff ect as patient (nested in sequence); the
model was fi rst fi tted with the pain intensity data. If the
carryover eff ect was not signifi cant, then a reduced model
excluding the carryover term was refi tted.
According to Jones and Kenward,21 the extent of the
carryover factor in the second and third treatment
period was defi ned as treatment received in the fi rst
and second period, respectively; the extent of carryover
in the fi rst period was the same but an arbitrary
treatment for all patients. The model was identifi able
since treatment period was another factor in the model.
The eff ect of carryover was fi rst tested, and if it was not
statistically signifi cant, this term was dropped from the
linear mixed model. The least-square mean (SD)
estimated from the initial or reduced model was
calculated for every drug treatment. For treatment
eff ects, according to Fisher’s least signifi cant diff erence
method for multiple comparisons,22 the global diff erence
between all the treatment groups was fi rst tested in the
model. Only when this test was signifi cant, all three
pairwise comparisons were made with the estimated
contrast from the initial or reduced model. As a
secondary analysis, change in pain during each
treatment period was calculated as the diff erence
between pain at treatment period baseline (mean of last
3 days of baseline before study start, or mean of last
3 days of washouts preceding periods B and C) and pain
on treatment (mean of last 3 days on maximum
tolerated dose). The percentage change in pain (change
in pain/treatment period baseline) was analysed as per
the above linear mixed model. Secondary continuous
outcome measures were analysed in the same way with
baseline scores included as an additional fi xed eff ect
in the model. Proportion data were analysed by
Fisher’s exact method.23 All reported p values are twosided.
All analyses were done with SAS software
(version 8.0)
I read about it, but i think i need a simpler language version
I have to present a journal article, and the authors used a LMM to do the statistics between treatment results in 3 different grps of patients who received 3 different drugs in sequence.
I just need to explain to my audience what they did with this LMM, why they did it, etc, in simple terms.
here's the passage from the article where they talk about it:
Statistical analysis
The preplanned main analysis compared pain scores for
combination treatment versus monotherapy for patients
on the maximum tolerated dose. On the basis of previous
variance estimates and accounting for two pairwise
comparisons (combination treatment vs gabapentin or
nortriptyline), we calculated that 40 patients would
provide an 80% probability of detecting a mean diff erence
between treatments of about half of a clinically
signifi cant20 amount of pain reduction (α=0·05, twosided).
Dropout rates in previous studies were about 10%
per 4–6-week treatment period, therefore we anticipated
that enrolment of 58 patients would yield 40 who
completed the study.
Patients completing at least two study treatment
periods (providing one pairwise comparison) were
included in the effi cacy analysis; analysis was by intention
to treat. Patients receiving at least one dose of any study
drug were included in analyses of adverse events. Mean
pain intensity was calculated from patient diaries while
patients were on the maximum tolerated dose. For
inclusion, more than 50% of the scores had to be
available; otherwise, the mean daily pain intensity was
treated as missing. A linear mixed model21 was formed
with fi xed eff ects of drug treatment, treatment sequence,
treatment period, and the fi rst-order carryover term, and
the random eff ect as patient (nested in sequence); the
model was fi rst fi tted with the pain intensity data. If the
carryover eff ect was not signifi cant, then a reduced model
excluding the carryover term was refi tted.
According to Jones and Kenward,21 the extent of the
carryover factor in the second and third treatment
period was defi ned as treatment received in the fi rst
and second period, respectively; the extent of carryover
in the fi rst period was the same but an arbitrary
treatment for all patients. The model was identifi able
since treatment period was another factor in the model.
The eff ect of carryover was fi rst tested, and if it was not
statistically signifi cant, this term was dropped from the
linear mixed model. The least-square mean (SD)
estimated from the initial or reduced model was
calculated for every drug treatment. For treatment
eff ects, according to Fisher’s least signifi cant diff erence
method for multiple comparisons,22 the global diff erence
between all the treatment groups was fi rst tested in the
model. Only when this test was signifi cant, all three
pairwise comparisons were made with the estimated
contrast from the initial or reduced model. As a
secondary analysis, change in pain during each
treatment period was calculated as the diff erence
between pain at treatment period baseline (mean of last
3 days of baseline before study start, or mean of last
3 days of washouts preceding periods B and C) and pain
on treatment (mean of last 3 days on maximum
tolerated dose). The percentage change in pain (change
in pain/treatment period baseline) was analysed as per
the above linear mixed model. Secondary continuous
outcome measures were analysed in the same way with
baseline scores included as an additional fi xed eff ect
in the model. Proportion data were analysed by
Fisher’s exact method.23 All reported p values are twosided.
All analyses were done with SAS software
(version 8.0)
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