2017 Baby weaning

Data analysis

Data extracted was tabulated in a google sheet. Then exported as csv file and imported in R (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.)

The package meta was used for the meta-analysis. The heterogenicity between studies was checked with Tau2. A funnel plot was used to detect publication bias. We grouped the comparison and outcomes to compare studies. A random effect meta-analysis using odds-ratio as outcome was performed with a DerSimonian and Lard method (add reference DerSimonian 1986) . A forest plot was used to visualize the association between the exposure to specific risk factors and the outcome.

Risk factors were grouped in reports focused to drink, food or breastfeeding and outcomes were severe early-childhood caries (s-ECC), white spot lesions (WSL) or caries measured in ICDAS>0.

Paquetes

Dataset

df <- read_csv("2017-weaning.csv")

Parsed with column specification:
cols(
  id = col_character(),
  `Risk factor` = col_character(),
  Comparison = col_character(),
  Outcome = col_character(),
  `Group A - Protector` = col_character(),
  `Group B - Risk factor` = col_character(),
  `Events in A` = col_integer(),
  `Total in A` = col_integer(),
  `Events in B` = col_integer(),
  `Total in B` = col_integer()
)

df <- mutate(df, Groups = paste(Comparison, Outcome))
df <- df %>% 
  filter(!str_detect(id, "Un Lam et al")) #avoid Un Lam papers

Data cleaning

ANALYSIS

Breastfeeding and s-ECC

Selection of papers

Bias

Heterogeneity

Baujat B, Mahé C, Pignon JP, Hill C (2002), A graphical method for exploring heterogeneity in meta-analyses: Application to a meta-analysis of 65 trials. Statistics in Medicine, 30, 2641–2652.

Meta-analysis

summary(meta1)

Number of studies combined: k = 2

                         OR           95%-CI     z  p-value
Random effects model 0.5046 [0.3693; 0.6895] -4.29 < 0.0001

Quantifying heterogeneity:
 tau^2 = 0; H = 1.00; I^2 = 0.0%

Test of heterogeneity:
    Q d.f.  p-value
 0.88    1   0.3480

Details on meta-analytical method:
- Mantel-Haenszel method
- DerSimonian-Laird estimator for tau^2

meta1

                                    OR           95%-CI %W(random)
Peltzer and Mongkolchati, 2015a 0.5690 [0.3812; 0.8492]       60.8
Peres et al 2017a               0.4190 [0.2545; 0.6897]       39.2

Number of studies combined: k = 2

                         OR           95%-CI     z  p-value
Random effects model 0.5046 [0.3693; 0.6895] -4.29 < 0.0001

Quantifying heterogeneity:
 tau^2 = 0; H = 1.00; I^2 = 0.0%

Test of heterogeneity:
    Q d.f.  p-value
 0.88    1   0.3480

Details on meta-analytical method:
- Mantel-Haenszel method
- DerSimonian-Laird estimator for tau^2

Forest plot

Sugary drinks and s-ECC

Selection of papers

Bias

Heterogeneity

Baujat B, Mahé C, Pignon JP, Hill C (2002), A graphical method for exploring heterogeneity in meta-analyses: Application to a meta-analysis of 65 trials. Statistics in Medicine, 30, 2641–2652.

Meta-analysis

summary(meta1)

Number of studies combined: k = 2

                         OR           95%-CI     z  p-value
Random effects model 0.6544 [0.5107; 0.8384] -3.35   0.0008

Quantifying heterogeneity:
 tau^2 = 0; H = 1.00; I^2 = 0.0%

Test of heterogeneity:
    Q d.f.  p-value
 0.25    1   0.6172

Details on meta-analytical method:
- Mantel-Haenszel method
- DerSimonian-Laird estimator for tau^2

meta1

                                    OR           95%-CI %W(random)
Peltzer and Mongkolchati, 2015b 0.6911 [0.4981; 0.9589]       57.3
Peltzer and Mongkolchati, 2015c 0.6082 [0.4163; 0.8885]       42.7

Number of studies combined: k = 2

                         OR           95%-CI     z  p-value
Random effects model 0.6544 [0.5107; 0.8384] -3.35   0.0008

Quantifying heterogeneity:
 tau^2 = 0; H = 1.00; I^2 = 0.0%

Test of heterogeneity:
    Q d.f.  p-value
 0.25    1   0.6172

Details on meta-analytical method:
- Mantel-Haenszel method
- DerSimonian-Laird estimator for tau^2

Forest plot

Food White and spot lesions

Selection of papers

Bias

Heterogeneity

Baujat B, Mahé C, Pignon JP, Hill C (2002), A graphical method for exploring heterogeneity in meta-analyses: Application to a meta-analysis of 65 trials. Statistics in Medicine, 30, 2641–2652.

Meta-analysis

summary(meta1)

Number of studies combined: k = 2

                         OR           95%-CI    z  p-value
Random effects model 1.0207 [0.2926; 3.5611] 0.03   0.9744

Quantifying heterogeneity:
 tau^2 = 0; H = 1.00; I^2 = 0.0%

Test of heterogeneity:
    Q d.f.  p-value
 0.02    1   0.8851

Details on meta-analytical method:
- Mantel-Haenszel method
- DerSimonian-Laird estimator for tau^2

meta1

                        OR            95%-CI %W(random)
Moimaz et al, 2014b 0.9750 [0.2415;  3.9358]       80.2
Moimaz et al, 2014c 1.2286 [0.0742; 20.3554]       19.8

Number of studies combined: k = 2

                         OR           95%-CI    z  p-value
Random effects model 1.0207 [0.2926; 3.5611] 0.03   0.9744

Quantifying heterogeneity:
 tau^2 = 0; H = 1.00; I^2 = 0.0%

Test of heterogeneity:
    Q d.f.  p-value
 0.02    1   0.8851

Details on meta-analytical method:
- Mantel-Haenszel method
- DerSimonian-Laird estimator for tau^2

Forest plot

References

citation(package = "meta")

To cite package 'meta' in publications use:

  Guido Schwarzer (2007), meta: An R package for meta-analysis, R News, 7(3),
  40-45.

A BibTeX entry for LaTeX users is

  @Article{,
    title = {meta: {A}n {R} package for meta-analysis},
    author = {Guido Schwarzer},
    journal = {R News},
    year = {2007},
    volume = {7},
    number = {3},
    pages = {40--45},
  }

URL https://cran.r-project.org/doc/Rnews/Rnews_2007-3.pdf