Results from 23 studies on the effectiveness of practice facilitation interventions within the primary care practice setting.

dat.baskerville2012

Format

The data frame contains the following columns:

authorcharacterstudy author(s)
yearnumericpublication year
scorenumericquality score (0 to 12 scale)
designcharacterstudy design (cct = controlled clinical trial, rct = randomized clinical trial, crct = cluster randomized clinical trial)
alloconcnumericallocation concealed (0 = no, 1 = yes)
blindnumericsingle- or double-blind study (0 = no, 1 = yes)
ittnumericintention to treat analysis (0 = no, 1 = yes)
fumonthsnumericfollow-up months
retentionnumericretention (in percent)
countrycharactercountry where study was conducted
outcomesnumericnumber of outcomes assessed
durationnumericduration of intervention
pperfnumericpractices per facilitator
meetingsnumeric(average) number of meetings
hoursnumeric(average) hours per meeting
tailornumericintervention tailored to the context and needs of the practice (0 = no, 1 = yes)
smdnumericstandardized mean difference
senumericcorresponding standard error

Details

Baskerville et al. (2012) describe outreach or practice facilitation as a "multifaceted approach that involves skilled individuals who enable others, through a range of intervention components and approaches, to address the challenges in implementing evidence-based care guidelines within the primary care setting". The studies included in this dataset examined the effectiveness of practice facilitation interventions for improving some relevant evidence-based practice behavior. The effect was quantified in terms of a standardized mean difference, comparing the change (from pre- to post-intervention) in the intervention versus the comparison group (or the difference from baseline in prospective cohort studies).

Source

Baskerville, N. B., Liddy, C., & Hogg, W. (2012). Systematic review and meta-analysis of practice facilitation within primary care settings. Annals of Family Medicine, 10(1), 63–74. https://doi.org/10.1370/afm.1312

Concepts

medicine, primary care, standardized mean differences, publication bias, meta-regression

Examples

### copy data into 'dat' and examine data
dat <- dat.baskerville2012
dat
#>              author year score design alloconc blind itt fumonths retention country outcomes duration
#> 1     Kottke et al. 1992     6    cct        0     1   1       19      83.0      US        2       18
#> 2    McBride et al. 2000     6    rct        0     0   0       18     100.0      US        4       12
#> 3     Stange et al. 2000     6    rct        0     0   0       24        NA      US       35       12
#> 4       Lobo et al. 2004     6    rct        1     0   0       21      57.0      NL       16       21
#> 5  Roetzhiem et al. 2005     6   crct        0     1   0       24     100.0      US        3       24
#> 6       Hogg et al. 2008     6    cct        0     0   1        6      87.0     Can       26       12
#> 7       Aspy et al. 2008     6    cct        0     1   0       18      89.0      US        4       18
#> 8       Jaen et al. 2010     6    rct        0     1   0       26      86.0      US       11       26
#> 9   Cockburn et al. 1992     7    rct        0     0   0        3      79.0     Aus        2        2
#> 10    Modell et al. 1998     7    rct        0     0   0       12     100.0      UK        1       12
#> 11    Engels et al. 2006     7    rct        1     0   1       12      92.0      NL        7        5
#> 12      Aspy et al. 2008     7    rct        0     1   0        9     100.0      US        1        9
#> 13  Deitrich et al. 1992    18    rct        0     1   0       12      96.0      NL       16       21
#> 14      Lobo et al. 2002     8    rct        1     0   1       21     100.0      US       10        3
#> 15     Bryce et al. 1995     9    rct        1     1   1       12      93.3      US        4       12
#> 16 Kinsinger et al. 1998     9    rct        1     1   0       18      94.0      US        5       12
#> 17   Solberg et al. 1998     9    rct        1     0   1       22     100.0      US       10       22
#> 18   Lemelin et al. 2001     9    rct        1     1   0       18      98.0     Can       13       18
#> 19  Frijling et al. 2002     9   crct        1     1   1       21      95.0      NL        7       21
#> 20  Frijling et al. 2003     9   crct        1     1   1       21      95.0      NL       12       21
#> 21  Margolis et al. 2004    10    rct        1     1   1       30     100.0      US        4       24
#> 22      Mold et al. 2008    10    rct        1     1   1        6     100.0      US        6        6
#> 23      Hogg et al. 2008    12    rct        1     1   1       13     100.0     Can       53       12
#>    pperf meetings hours tailor  smd   se
#> 1    5.5     30.0  1.00      1 1.01 0.52
#> 2     NA      5.0  1.00      1 0.82 0.46
#> 3   20.0      4.0  1.50      1 0.59 0.23
#> 4    5.0     15.0  1.00      1 0.44 0.18
#> 5    4.0      4.0  1.00      0 0.84 0.29
#> 6   11.0     12.0  1.50      1 0.73 0.29
#> 7    3.0      3.0  6.00      1 1.12 0.36
#> 8    6.0      4.5  6.00      1 0.04 0.37
#> 9   40.0      2.0  0.25      0 0.24 0.15
#> 10  13.0      3.0  1.00      0 0.32 0.40
#> 11    NA      5.0  1.00      1 1.04 0.32
#> 12   6.0     18.0  6.00      1 1.31 0.57
#> 13  20.0     15.0  1.00      1 0.59 0.29
#> 14   8.0      3.0  1.00      1 0.66 0.19
#> 15  12.0      1.0 15.00      0 0.62 0.31
#> 16  13.0     10.0  0.75      1 0.47 0.27
#> 17  11.0      4.0  3.00      1 1.08 0.32
#> 18   8.0     33.0  1.75      1 0.98 0.32
#> 19  20.0     15.0  1.00      0 0.26 0.18
#> 20  20.0     15.0  1.00      1 0.39 0.18
#> 21  11.0      9.0  1.00      1 0.60 0.31
#> 22   8.0     18.0  4.00      1 0.94 0.53
#> 23  14.0      9.0  0.75      1 0.11 0.27

# \dontrun{

### load metafor package
library(metafor)

### random-effects model
res <- rma(smd, sei=se, data=dat, method="DL")
print(res, digits=2)
#> 
#> Random-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of total heterogeneity): 0.02 (SE = 0.03)
#> tau (square root of estimated tau^2 value):      0.13
#> I^2 (total heterogeneity / total variability):   20.15%
#> H^2 (total variability / sampling variability):  1.25
#> 
#> Test for Heterogeneity:
#> Q(df = 22) = 27.55, p-val = 0.19
#> 
#> Model Results:
#> 
#> estimate    se  zval  pval  ci.lb  ci.ub      
#>     0.56  0.06  8.70  <.01   0.43   0.68  *** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

### funnel plot
funnel(res, xlab="Standardized Mean Difference", ylim=c(0,0.6))


### rank and regression tests for funnel plot asymmetry
ranktest(res)
#> Warning: Cannot compute exact p-value with ties
#> 
#> Rank Correlation Test for Funnel Plot Asymmetry
#> 
#> Kendall's tau = 0.4284, p = 0.0049
#> 
regtest(res)
#> 
#> Regression Test for Funnel Plot Asymmetry
#> 
#> Model:     mixed-effects meta-regression model
#> Predictor: standard error
#> 
#> Test for Funnel Plot Asymmetry: z = 3.1763, p = 0.0015
#> Limit Estimate (as sei -> 0):   b = 0.0519 (CI: -0.2615, 0.3653)
#> 

### meta-regression analyses examining various potential moderators
rma(smd, sei=se, mods = ~ score, data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0211 (SE = 0.0288)
#> tau (square root of estimated tau^2 value):             0.1453
#> I^2 (residual heterogeneity / unaccounted variability): 22.94%
#> H^2 (unaccounted variability / sampling variability):   1.30
#> R^2 (amount of heterogeneity accounted for):            0.00%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 27.2532, p-val = 0.1626
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 0.3454, p-val = 0.5567
#> 
#> Model Results:
#> 
#>          estimate      se     zval    pval    ci.lb   ci.ub     
#> intrcpt    0.6830  0.2183   3.1286  0.0018   0.2551  1.1109  ** 
#> score     -0.0149  0.0253  -0.5877  0.5567  -0.0646  0.0348     
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ alloconc, data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0227 (SE = 0.0299)
#> tau (square root of estimated tau^2 value):             0.1507
#> I^2 (residual heterogeneity / unaccounted variability): 23.78%
#> H^2 (unaccounted variability / sampling variability):   1.31
#> R^2 (amount of heterogeneity accounted for):            0.00%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 27.5502, p-val = 0.1534
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 0.0417, p-val = 0.8383
#> 
#> Model Results:
#> 
#>           estimate      se     zval    pval    ci.lb   ci.ub      
#> intrcpt     0.5786  0.1041   5.5588  <.0001   0.3746  0.7827  *** 
#> alloconc   -0.0275  0.1348  -0.2041  0.8383  -0.2917  0.2366      
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ blind,    data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0226 (SE = 0.0298)
#> tau (square root of estimated tau^2 value):             0.1502
#> I^2 (residual heterogeneity / unaccounted variability): 23.65%
#> H^2 (unaccounted variability / sampling variability):   1.31
#> R^2 (amount of heterogeneity accounted for):            0.00%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 27.5032, p-val = 0.1548
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 0.0695, p-val = 0.7921
#> 
#> Model Results:
#> 
#>          estimate      se     zval    pval    ci.lb   ci.ub      
#> intrcpt    0.5809  0.0972   5.9752  <.0001   0.3903  0.7714  *** 
#> blind     -0.0349  0.1325  -0.2635  0.7921  -0.2946  0.2248      
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ itt,      data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0224 (SE = 0.0298)
#> tau (square root of estimated tau^2 value):             0.1495
#> I^2 (residual heterogeneity / unaccounted variability): 23.49%
#> H^2 (unaccounted variability / sampling variability):   1.31
#> R^2 (amount of heterogeneity accounted for):            0.00%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 27.4459, p-val = 0.1566
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 0.0360, p-val = 0.8496
#> 
#> Model Results:
#> 
#>          estimate      se    zval    pval    ci.lb   ci.ub      
#> intrcpt    0.5496  0.0926  5.9355  <.0001   0.3681  0.7310  *** 
#> itt        0.0250  0.1319  0.1897  0.8496  -0.2336  0.2836      
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ duration, data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0230 (SE = 0.0304)
#> tau (square root of estimated tau^2 value):             0.1516
#> I^2 (residual heterogeneity / unaccounted variability): 23.62%
#> H^2 (unaccounted variability / sampling variability):   1.31
#> R^2 (amount of heterogeneity accounted for):            0.00%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 27.4927, p-val = 0.1551
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 0.0004, p-val = 0.9849
#> 
#> Model Results:
#> 
#>           estimate      se    zval    pval    ci.lb   ci.ub      
#> intrcpt     0.5601  0.1451  3.8605  0.0001   0.2757  0.8444  *** 
#> duration    0.0002  0.0088  0.0190  0.9849  -0.0171  0.0174      
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ tailor,   data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0074 (SE = 0.0252)
#> tau (square root of estimated tau^2 value):             0.0863
#> I^2 (residual heterogeneity / unaccounted variability): 9.14%
#> H^2 (unaccounted variability / sampling variability):   1.10
#> R^2 (amount of heterogeneity accounted for):            58.32%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 23.1124, p-val = 0.3380
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 3.5474, p-val = 0.0596
#> 
#> Model Results:
#> 
#>          estimate      se    zval    pval    ci.lb   ci.ub      
#> intrcpt    0.3717  0.1084  3.4281  0.0006   0.1592  0.5843  *** 
#> tailor     0.2434  0.1292  1.8835  0.0596  -0.0099  0.4967    . 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ pperf,    data=dat, method="DL")
#> Warning: Studies with NAs omitted from model fitting.
#> 
#> Mixed-Effects Model (k = 21; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0237)
#> tau (square root of estimated tau^2 value):             0
#> I^2 (residual heterogeneity / unaccounted variability): 0.00%
#> H^2 (unaccounted variability / sampling variability):   1.00
#> R^2 (amount of heterogeneity accounted for):            100.00%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 19) = 17.1739, p-val = 0.5781
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 7.3013, p-val = 0.0069
#> 
#> Model Results:
#> 
#>          estimate      se     zval    pval    ci.lb    ci.ub      
#> intrcpt    0.7318  0.0997   7.3429  <.0001   0.5365   0.9272  *** 
#> pperf     -0.0137  0.0051  -2.7021  0.0069  -0.0236  -0.0038   ** 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
rma(smd, sei=se, mods = ~ I(meetings * hours), data=dat, method="DL")
#> 
#> Mixed-Effects Model (k = 23; tau^2 estimator: DL)
#> 
#> tau^2 (estimated amount of residual heterogeneity):     0.0075 (SE = 0.0239)
#> tau (square root of estimated tau^2 value):             0.0867
#> I^2 (residual heterogeneity / unaccounted variability): 9.72%
#> H^2 (unaccounted variability / sampling variability):   1.11
#> R^2 (amount of heterogeneity accounted for):            57.90%
#> 
#> Test for Residual Heterogeneity:
#> QE(df = 21) = 23.2607, p-val = 0.3302
#> 
#> Test of Moderators (coefficient 2):
#> QM(df = 1) = 3.8574, p-val = 0.0495
#> 
#> Model Results:
#> 
#>                      estimate      se    zval    pval   ci.lb   ci.ub      
#> intrcpt                0.4453  0.0773  5.7644  <.0001  0.2939  0.5968  *** 
#> I(meetings * hours)    0.0073  0.0037  1.9640  0.0495  0.0000  0.0146    * 
#> 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# }