Mycobacterium avium Complex

Simulated Treatment Comparison (STC) is a modification of covariate adjustment,²¹ which fits an outcome model using the IPD in the $AB$ trial:
$g\left({\mu }_{t(AB)}\left(\mathit{X}\right)\right)={\beta }_0+{\mathit{\beta }}_1^T\mathit{X}+({\beta }_B+{\mathit{\beta }}_2^T{\mathit{X}}^{EM})I(t=B),$
where ${\beta }_0$ is an intercept term, ${\mathit{\beta }}_1$ is a vector of coefficients for prognostic variables, ${\beta }_B$ is the relative effect of treatment $B$ compared to $A$ at $\mathit{X}=0$ , ${\mathit{\beta }}_2$ is a vector of coefficients for effect modifiers ${\mathit{X}}^{EM}$ (a subvector of the full covariate vector $\mathit{X}$ ), and ${\mu }_{t(AB)}\left(\mathit{X}\right)$ is the expected outcome of an individual assigned treatment $t$ with covariate values $\mathit{X}$ , which is transformed onto a chosen linear predictor scale with link function $g(·)$ .
The model in equation (8) is a more general form of that given by Ishak et al.^{7 (link)}, which does not include any effect modifier terms. The STC literature advocates forming indirect comparisons directly on the natural outcome scale with $g(·)$ the identity link in equation (4) or (5); however, this leads to scale conflicts¹⁰ if the same link is not used in the outcome model (8) (see the following section on the importance of scale). ${\hat{Y}}_{A(AC)}$ and ${\hat{Y}}_{B(AC)}$ may be predicted from the outcome regression by substituting in mean covariate values to obtain ${\hat{Y}}_{A(AC)}=g^{-1}({\hat{\beta }}_0+{\hat{\mathit{\beta }}}_1^T{\overline{\mathit{X}}}_{(AC)})$ and ${\hat{Y}}_{B(AC)}=g^{-1}({\hat{\beta }}_0+{\hat{\mathit{\beta }}}_1^T{\overline{\mathit{X}}}_{(AC)}+{\hat{\mathit{\beta }}}_B+{\hat{\mathit{\beta }}}_2^T{\overline{\mathit{X}}}_{(AC)}^{EM})$ . These estimators are systematically biased whenever $g(·)$ is not the identity function, because the mean outcome depends on the full distribution of the covariates and not just their mean.^{7 (link)} Instead of substituting in mean covariate values in this case, Ishak et al. suggest that estimates are obtained by first drawing samples from the joint covariate distribution in the $AC$ trial and then averaging over the predicted outcomes based on the regression model. This simulation approach, however, inflates uncertainty of the relative effect estimates.
Standard tools for model checking (such as AIC/DIC, examining residuals, among others) may be used when constructing the outcome model in the $AB$ trial; however (as with MAIC), additional assumptions are required to predict absolute outcomes in the $AC$ population, which are difficult to test with the limited data available.

We conducted meta-analyses for groups of effect estimates that related similar WASH access or practices (e.g., latrine availability and/or use became “sanitation access”) to a common outcome. Potential outcomes included infection with a specific STH (i.e., A. lumbricoides, T. trichiura, hookworm, and S. stercoralis) or any STH generally. Note that “any STH” reflected infection with an individual species or co-infection with multiple species when authors reported aggregated STH infection results. Meta-analyses were performed for groups of independent effect estimates that numbered three or greater and shared a similar exposure and infection outcome. A study that measured several WASH components could contribute to multiple meta-analyses, but could only supply one effect estimate for any single meta-analysis.
We employed random-effects models to account for the expected heterogeneity between studies [36] (link). Only adjusted estimates were utilized to limit the impact of confounding on pooled effect measures [37] . When necessary, we inverted estimates to reflect the effect of WASH, rather than the absence of WASH. This inversion was necessary in order to ensure enough study estimates were available for meta-analysis, but could have resulted in additional heterogeneity. For example, the inverse of “no sanitation access” may be similar to, but distinct from, “sanitation access” when assessed by questionnaire due to bias associated with socially desirable responses. Further, the presence of WASH access or practices may not necessarily be the same as the inverse effect of their absence, especially if important confounders or effect modifiers remain unexplored. Estimates of effect not included in meta-analyses were summarized in the text. The meta-analysis package MAIS for Stata version 12 (StataCorp) was used to perform the random-effects meta-analyses with the DerSimonian and Laird method [38] (link). The natural log of reported ORs was the dependent variable. CIs use the 95% level unless otherwise noted.

Forty children (21 males, mean age  ±  standard deviation (SD): 5.09  ±  3.79
years old) with sensorineural hearing loss who received their first CI in our
hospital from September 2018 to June 2020 were included in this study. These
children were right-handed according to an assessment with the Edinburgh
Handedness Inventory (Oldfield, 1971 (link)). They started to use hearing aids at a mean age of
2.30  ±  1.21 years old, and had used hearing aids with a mean duration of
2.79  ±  3.26 years and for at least 4 h per day in their daily life. These
children had auditory responses to environmental sounds during the initial
period of hearing aid fitting. To confirm the effectiveness of hearing aid
fitting in the daily life, their auditory performance was reexamined by the
Meaningful Auditory Integration Scale (MAIS) and Categories of Auditory
Performance (CAP) at least every 8 months. The MAIS includes 10 questions
reflecting children's confidence in hearing devices, auditory sensitivity and
ability to connect sounds with meaning. The highest score is 40 and indicates
the best performance for meaningful sound use in everyday situations. The CAP is
an eight-score hierarchical scale that evaluates receptive auditory abilities
and ranges from no awareness of environmental sounds (1 score) to telephone use
with a familiar talker (8 scores). When hearing aid outcomes were poor and the
ABR thresholds estimated by the click and 500-Hz tone burst were above 90 dB
nHL, the hearing-impaired child received a CI. Before the CI surgery, the ABR,
40-Hz auditory evoked potential, multi-frequency steady state potential (MFSSP),
distortion product otoacoustic emission (DPOAE) and acoustic impedance had been
performed to confirm profound sensorineural hearing loss (hearing threshold ≥90
dB nHL). The 40-Hz auditory evoked potential (Lynn et al., 1984 (link)) and MFSSP (Johnson & Brown,
2005) tests were performed for hearing threshold estimation using the
500-Hz tone burst and sinusoidally amplitude modulated tones (1, 2 and 4 kHz),
respectively. Only 24 children finished the pure-tone audiometry and their
unaided pure tone averages (averaged over 0.25, 0.5, 1, 2, 4 and 8 kHz) were
above 90 dB HL. Participants who had a mental disability, intracranial lesions
or head trauma were excluded from this study. Of children in our study, 20 had
IEMs assessed by computerized tomography (CT) and magnetic resonance imaging
(MRI) according to previously published criteria (Sennaroglu & Bajin, 2017 (link)).
Detailed information for all children is provided in Table 1. All procedures performed in
this study involving human participants were in accordance with the ethical
standards of the institutional and/or national research committee and with the
1964 Helsinki declaration and its later amendments or comparable ethical
standards. The protocols and experimental procedures in the present study were
reviewed and approved by the Anhui Provincial Hospital Ethics Committee. Each
participant's guardians provided written informed consent.