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The estimated intervention effects from both trials depend on the sensitivity parameters used.
Our analyses relied heavily on the information on intervention effects from Cochrane systematic reviews [ 25- 30].
Second, we make the assumption of homogenous between-study correlations for the intervention effects from different outcomes.
Table 3 presents the results of intervention effects from D-in-D estimation with four outcome variables adjusting for some socio-demographic characteristics.
Finally, our fourth aim was to investigate the possibility of estimating intervention effects from observational data without knowing the underlying causal structure.
Of note, cohort studies are subject to some biases avoided by cross-sectional surveys, such as loss to follow-up and intervention effects from repeated contacts including multiple risk assessment surveys and HIV counseling and testing.
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The results from Table 5 show that, corrected for these both factors, the intervention effect further increased compared to the uncorrected intervention effect from 0.43 to 0.51 points within therapeutic commitment.
The pooled effect from the fixed-effect method is considered to be a typical intervention effect from the studies included in the meta-analysis; implying that observed differences among study results are caused exclusively by chance.
Table 4 reports the mortality rates between the 7th and 28th day in the intervention and control groups and the odds ratios (ORs) for the intervention effect from the logistic regression.
A small to medium intervention effect from pre to post is expected for these variables, and we want to be able to detect an effect of d=0.25 with power =0.80 (two-tailed test and significance level of 0.05).
Control group subjects also demonstrated improvements in several domains, possibly due to an intervention effect from enrollment in the study and associated interactions with study staff and various measurements.
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