Hi Vasco, I’m afraid not, sorry. The diversity of outcome measures makes this all but impossible, e..g one study measures “servings of meat per week”, others it by the gram, others count how many meals are served in a given time period, etc.
Do you know about decent estimates for the standard deviation of meat consumption in kg over a given period (like the median delay of 2 weeks among the studies you reviewed) in a given country? One could multiply it by the meat consumption per capita to get the standard deviation in kg, and then multiply this by your effect size to get a rough estimate for the reduction in meat consumption in kg.
I don’t know this, sorry, and not every study reports enough location data to begin to estimate this (e.g. studies that recruit an online sample from multiple countries)
Thanks, Seth. I assume it is also difficult to know at which rate the effect size decays across time. A 3 % reduction in consumption over 1 year would be more impressive than a 3 % reduction over 1 week. Do you have a sense of whether the pooled effect size of 0.07 you estimate should be interpreted as referring more to 1 month than 1 week?
The median delay, in days, is 14, and the mean is 52 (we have a few studies with long delays, the longest is 3 years (Jalil et al. 2023).
So I’d say, think “about 2 weeks on average with some lengthy outliers”. Also there’s basically no relationship between delay and effect size.
to replicate in R (from the root directory of our project):
> source('./scripts/libraries.R')
> source('./scripts/load-data.R')
> summary(dat$delay)
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.00 11.50 14.00 52.05 60.00 1095.00
> source('./functions/sum-lm.R') # this is a little function we wrote that puts summary(lm()) into a dplyr-friendly pipe
> dat |> sum_lm(y = d, x = delay)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05312 0.02552 2.08181 0.03968
delay 0.00005 0.00019 0.23166 0.81723
Thanks, Seth! I thought you may not have that data easily available because I did not find it in your moderators’ analysis in Table 2. Do the effect sizes refer to the whole period of the delay (e.g. 14 days), or just to the last part of it (e.g. last day of the 14 days)? It does not seem clear from section 3.2. I would expect a greater decay if the effect sizes refer to the last part of the delay.
Delay indicates the number of days that have elapsed between the beginning of treatment and the final outcome measure. How outcomes are measured varies from study to study, so in some cases it’s a 24 hour food recall X number of days after treatment is administered (the last part of it), in others it’s a continuous outcome measurement in a cafeteria (the entire period of delay).
Hi Vasco, I’m afraid not, sorry. The diversity of outcome measures makes this all but impossible, e..g one study measures “servings of meat per week”, others it by the gram, others count how many meals are served in a given time period, etc.
Do you know about decent estimates for the standard deviation of meat consumption in kg over a given period (like the median delay of 2 weeks among the studies you reviewed) in a given country? One could multiply it by the meat consumption per capita to get the standard deviation in kg, and then multiply this by your effect size to get a rough estimate for the reduction in meat consumption in kg.
I don’t know this, sorry, and not every study reports enough location data to begin to estimate this (e.g. studies that recruit an online sample from multiple countries)
Thanks, Seth. I assume it is also difficult to know at which rate the effect size decays across time. A 3 % reduction in consumption over 1 year would be more impressive than a 3 % reduction over 1 week. Do you have a sense of whether the pooled effect size of 0.07 you estimate should be interpreted as referring more to 1 month than 1 week?
This I can say more about!
The median delay, in days, is 14, and the mean is 52 (we have a few studies with long delays, the longest is 3 years (Jalil et al. 2023).
So I’d say, think “about 2 weeks on average with some lengthy outliers”. Also there’s basically no relationship between delay and effect size.
to replicate in R (from the root directory of our project):
Thanks, Seth! I thought you may not have that data easily available because I did not find it in your moderators’ analysis in Table 2. Do the effect sizes refer to the whole period of the delay (e.g. 14 days), or just to the last part of it (e.g. last day of the 14 days)? It does not seem clear from section 3.2. I would expect a greater decay if the effect sizes refer to the last part of the delay.
Delay indicates the number of days that have elapsed between the beginning of treatment and the final outcome measure. How outcomes are measured varies from study to study, so in some cases it’s a 24 hour food recall X number of days after treatment is administered (the last part of it), in others it’s a continuous outcome measurement in a cafeteria (the entire period of delay).