Title: A 2-year follow-up to a randomized controlled trial on resistance training in
postmenopausal women: vasomotor symptoms, quality of life and cardiovascular risk markers
Version: 4 Date: 21 Jun 2024
Reviewer's report:
Review report A 2-year follow-up to a randomized controlled trial on resistance training in
postmenopausal women: vasomotor symptoms, quality of life and cardiovascular risk markers
Very interesting study with good methodological design but unknown statistical analysis model.
The model must be adjusted to the design, including the effect of exercise in the control and
intervention group as well as the effect of confounding variables.
Introduction
Indicate the plausibility of the hypotheses under study, which are: 1.-“ structured RTI reduces
MS-VMS frequency and HRQoL over time compared with a group that received a less structured
introduction to resistance training after the initial 15 weeks of unchanged low PA (herein referred
to as the control group, CG)” and 2 “RTI contributes to reduces cardiovascular risk markers over
time for postmenopausal women with VMS compared with a CG.
The above possibly has to do with the action of the exercise on neurotransmitters (for example
serotonin for hot flushes)(1) and control of the autonomic nervous system on cardiovascular
risk markers(2)
Material y methods
Good design but should be highlighted “non-blinding intervention”
Considering that statistical analysis is essential, it is necessary to make some indications:
1.-The bibliographic references for the statistical model used (Linear mixed models or
(3)“LMMs with diagonal covariance structure”) should be noted.
Since the reference of the model used is not specified; I suppose this is a subtype of regression
model on time (survival model) with repeated measures.
2.-The statistical model must consider the measurement scale of the outcome variables. In this
way, Hot Flushes are ordinal variables. An ordinal variable is not distributing normal (normality
is for continuous variables). Regarding this, there are survival models available; for example:
MERLIN Model (3) with repeated measurements for ordinal outcomes.
3.- On this survival model repeated measures should contemplate the times with exercise on
control and intervention groups as well as the effect of confounding variables. The model must
be adjusted to the nature of the design study, including the effect of exercise in the control and
intervention groups(4).
4.- it is necessary for the author to briefly indicate the evaluation for fulfillment of the general
assumptions: functional form, discrimination and calibration (5) for to the survival model (result
on time, without censure) used for repeated measures.
The functional form to preserve the quantitative nature of the covariates in a survival
regression model. The correct functional form of the quantitative variables for the
model should be checked assessing the association between the longitudinal response for
outcome variable with repeated samples of predictor on time.
The discrimination of a survival model reflects its ability to distinguish the outcomes
between subjects. The calibration aspect of the survival model refers to agreements between the
predicted outcome and observed outcome
5.- Respect to “A priori distribution evaluation of all outcome variables was performed to
confirm that the normality assumption criterion was fulfilled” It would be important to point
out what criteria was used.
Results
The use of a survival model for repeated measures, it consider the time effect of incomplete
observations and control for confounding variables; and this should show in results.
Point the results of the survival model used with verification of compliance with its assumptions.
Discussion
The limitations imposed by the non-blinding intervention must be discussed.
Results with a non-significant p value should not be explained. A "non-significant p value" is
interpreted as no change in knowledge(6). The explanation of the results does not depend on
the sample size issue. You performed a sample size calculation according to your hypothesis. If
things don't work out, it means that the hypothesis was not proven.
Do not limit the explanations to the original clinical trial; Well, what happens here is possibly a
consequence of intervention that produced changes in women's habits over time.
Conclusions
Efficacy is effect to which an intervention, under ideal and controlled circumstances – comparing
an intervention group with a placebo group. Effectiveness meanwhile refers to how well it
performs in the real world (7).
Blinding is a measure in randomized controlled trials (RCT) to reduce detection and performance
bias. There is evidence that lack of blinding leads to overestimated treatment effects (8). This
study was carried out (non-blinding intervention); the conclusion should that refers Effectiveness
on resistance training intervention on Vasomotor Symptoms.
The conclusion "The RTI did neither contribute to preserved improvements of cardiovascular
risk markers nor improved HRQoL after two years compared to a CG" is pitfall. A p-value non-
significant is not evidence of equivalence on effect. the   researcher hypothesis (H1) not was
the equivalence on risk markers.
1. Berendsen HH. The role of serotonin in hot flushes. Maturitas. 2000;36(3):155-64.
2. Hadaya J, Ardell JL. Autonomic Modulation for Cardiovascular Disease. Front Physiol.
2020;11:617459.
3. Crowther MJ. merlin—A unified modeling framework for data analysis and methods
development in Stata. The Stata Journal. 2020;20(4):763-84.
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5. van Houwelingen HC. Validation, calibration, revision and combination of prognostic
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6. O'Brien SF, Osmond L, Yi QL. How do I interpret a p value? Transfusion.
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7. Singal AG, Higgins PD, Waljee AK. A primer on effectiveness and efficacy trials. Clin Transl
Gastroenterol. 2014;5(1):e45.
8. Probst P, Grummich K, Heger P, Zaschke S, Knebel P, Ulrich A, et al. Blinding in randomized
controlled trials in general and abdominal surgery: protocol for a systematic review and
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