Title: Impact of the Momentum pilot project on male involvement in maternal health and
newborn care in Kinshasa, Democratic Republic of the Congo: a quasi-experimental study
Version: 4 Date: 29 Sep 2022
Reviewer:  Michèle Dramaix
Reviewer's report:
I would like to thank the authors for their answers and the modifications made to the article.
Several things I didn’t fully understood are now clear now. I found the answers and changes
quite satisfactory except those related to sample size and tests used in Table 3.
The sample size estimate is based on the test comparing two independent proportions. Wang
& Kattan online sample size calculator gives as expected, the same results as STATA but the
“dropout rate” can be directly introduced in the calculation.
1. The specifications required to obtain an estimate of the sample size include the signication
level (two-sided). The authors speek of “99% confidence” does this mean that the significance
level has been set at 0.01?
2. In the previous version of the paper, a design effect of 2 was taken into account, but it no
longer appears in the new version. Why?
3. When I tried to compute the sample size (with Wang & Kattan calculator or STATA), using
the specifications of the article, I don’t get1213 in each group.
- Taking 6.5% as base percentage, an absolute difference of 10% (16.5% in the experimental
group), significance level = 0.05, power = 0.99 and a dropout rate of 25%, the required sample
size is 519 in each group (with continuity correction)
- taking the same specifications but a significance level = 0.01, the required sample size is 671
in each group (with continuity correction).
These estimates are therefore well below 1213 unless a design effect of 2 has been applied
applied.
It’s not a problem to have more subjects than estimated but I think important that a reader can
understand the sample size calculation, for another survey for example. In addition, the sample
size is ultimately smaller.
Table 3: the footnote mentions one-way analysis of variance as statistical method used to obtain
the p-values presented in the table. However, proportions are also compared and some p-values
relate to the comparison between baseline and end-line. For these comparisons, tests comparing
paired samples should be applied : McNemar’s Chi² and paired t-test. Which methods were used
to compare proportions or means at baseline and end-line?
Comment for response to comment 15. I actually misread a bad reading and took the % from
the comparison and intervention groups instead of those from the “lost-to-follow-up” and
“remaining” groups. . However, the last part of my question was : “For this variable “Always
lived in the locality”, I don’t understand the % in the whole sample (all male partners) because
they are closer to those in the lost to follow-up, the smallest group.” I will try to express my
comment in another way. The % for “All male Partners” are closer to those for “Remaining
Male Partners” because it is the larger of the two groups. But this is not the case for the variable
“Always lived in locality”. Based on the % presented in the 2nd to 5th columns and the sample
sizes  I have now seen in the last row, I found for all male partners in the comparison group, a
% of 30.2% and in the intervention group, a % of 38.6%.
Some details on the form.
-Line 388 : possibly write in full the abbreviation ATE (Adjusted Treatment Effect ?) the 1st
time it appears in the text even if it’s defined
-Table S1 : I may have misunderstood the title but it seems that it doesn’t mention the comparison
made. It could also be simplified and the definition of “not included” and “included” clarified
in a footnote to the table. It’s just a suggestion.