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Draw a league table with a CINeMA confidence profile in every cell

Source: R/cinema_league.R
cinema_league.Rd

Draws every pairwise estimate of a network meta-analysis as a grid laid out like ggleague() and netmeta::netleague(): each cell compares the treatment that comes first on the diagonal with the one that comes later, network estimates sit below the diagonal and direct estimates above it. Under each network estimate six small marks give its judgment in each CINeMA domain, left to right within-study bias, reporting bias, indirectness, imprecision, heterogeneity and incoherence, colored by no, some or major concerns. The marks are never added into a score.

Usage

cinema_league(x, ..., title = NULL, caption = NULL, family = "Lato")

Arguments

x

Judgments from cinema_judge(), or a network meta-analysis from netmeta::netmeta(), which is then judged with the arguments in ....

...

When x is a netmeta fit, arguments for cinema_judge(): the study judgments rob and indirectness, reporting, threshold, rule, your own judgments, small_values and order. Giving only judgments, such as a report exported from the CINeMA web application, draws your judgments as they are.

title, caption

Title above the table and note below it. The default caption says which estimates sit on each side of the diagonal and, domain by domain, which judgments were computed by which rule and which are yours. A caption you give is added above it.

family

Font family. The package ships Lato and registers it on load.

Value

An object of class cinema_league, which prints as an interactive widget. Use graph_widget(), graph_plot() or graph_save() for the widget, a static ggplot or a file. The field judgments holds the result of cinema_judge() the table was drawn from.

Details

Hovering over a cell lists its judgments; clicking it opens a panel under the table with every domain's judgment, the reason for it and whether it was computed by a rule or given by you, the estimate against the range of little difference, and a bar of the contribution of each study to the estimate, colored by its risk of bias. The cells can also be reached with the keyboard: Tab moves between them and Enter or Space opens one. Hovering over a treatment on the diagonal lights its row and column.

A mark drawn hollow is a domain not judged, such as reporting bias when you gave no judgment for it. A round mark in the incoherence place means there was no local test for that comparison, only the global test of the whole network, as CINeMA prescribes.

Rules

Every rule below is the one CINeMA implements, as published; where the papers leave a detail open the choice made here is stated.

  • Within-study bias and indirectness combine the study judgments with the percentage contribution of each study to each estimate (Papakonstantinou et al. 2018), from netmeta::netcontrib(). The majority rule takes the level with the largest total contribution, the more serious level on a tie; the average rule scores low 1, moderate 2 and high 3, averages the scores weighted by contribution and rounds, halves up; the highest rule takes the most serious level among the studies that contribute more than 0.0001 percent. Low, moderate and high become no, some and major concerns.

  • Reporting bias is your judgment; CINeMA suggests suspected or undetected, and suspected is shown as some concerns.

  • Imprecision compares the confidence interval with the range of little difference. There are no concerns when the interval lies wholly within the range, or wholly on the side of no effect that the point estimate is on; some concerns when it crosses no effect but not the limit on the other side; and major concerns when it passes that limit, so that it holds important effects in both directions.

  • Heterogeneity judges the prediction interval by the same rule. There are no concerns when it reaches the same step as the confidence interval, some concerns when it reaches one step further and major concerns when it reaches two (Table 4 of Papakonstantinou et al. 2020; this reproduces every scenario in Figure 3 of Nikolakopoulou et al. 2020). A common effect model has no prediction interval, so heterogeneity is then not judged.

  • Incoherence, for a comparison with direct and indirect evidence, uses the test of the difference between them from netmeta::netsplit() (SIDE). With p above 0.10 there are no concerns. Otherwise the areas below, within and above the range of little difference are compared: when both confidence intervals reach the same areas there are no concerns, when they differ in one area some concerns, and when they differ in two or three major concerns. A comparison with only direct or only indirect evidence cannot be tested locally, and takes its judgment from the global design by treatment interaction test of netmeta::decomp.design(): major concerns below 0.05, some from 0.05 to 0.10 and no concerns above; when the network has no closed loop, so the test cannot be computed, major concerns. Both tests have low power.

CINeMA's authors stress that these rules are a starting point: the reasons say what each rule saw, so a judgment can be revised by giving it in judgments. CINeMA also leaves any overall rating to the reviewers; this function gives none, though a rating you supply is kept and shown.

Sources

Nikolakopoulou A, Higgins JPT, Papakonstantinou T, et al. CINeMA: an approach for assessing confidence in the results of a network meta-analysis. PLoS Medicine 2020;17(4):e1003082. doi:10.1371/journal.pmed.1003082

Papakonstantinou T, Nikolakopoulou A, Higgins JPT, Egger M, Salanti G. CINeMA: software for semiautomated assessment of the confidence in the results of network meta-analysis. Campbell Systematic Reviews 2020;16:e1080. doi:10.1002/cl2.1080

Papakonstantinou T, Nikolakopoulou A, Rucker G, et al. Estimating the contribution of studies in network meta-analysis: paths, flows and streams. F1000Research 2018;7:610.

Examples

# \donttest{
if (requireNamespace("netmeta", quietly = TRUE) &&
    requireNamespace("meta", quietly = TRUE)) {
  pw <- meta::pairwise(treat = treatment, event = pasi75_r,
                       n = pasi75_n, studlab = study,
                       data = psoriasis_nma, sm = "OR")
  nma <- netmeta::netmeta(pw, common = FALSE)
  # Illustrative judgments, invented for this example.
  rob <- data.frame(
    study = c("CLEAR", "ERASURE", "FEATURE", "FIXTURE", "JUNCTURE"),
    judgment = c("high", "low", "some concerns", "low", "some concerns")
  )
  cinema_league(nma, rob = rob, threshold = 1.25,
                small_values = "undesirable",
                caption = "Illustrative judgments, not published assessments.")
}
# }