Generate Balancing Weights for Longitudinal Treatments

weightitMSM() allows for the easy generation of balancing weights for marginal structural models for time-varying treatments using a variety of available methods for binary, continuous, and multi-category treatments. Many of these methods exist in other packages, which weightit() calls; these packages must be installed to use the desired method.

Usage

weightitMSM(
  formula.list,
  data = NULL,
  method = "glm",
  stabilize = FALSE,
  by = NULL,
  s.weights = NULL,
  num.formula = NULL,
  moments = NULL,
  int = FALSE,
  missing = NULL,
  verbose = FALSE,
  include.obj = FALSE,
  keep.mparts = TRUE,
  is.MSM.method,
  weightit.force = FALSE,
  ...
)

Arguments

formula.list: a list of formulas corresponding to each time point with the time-specific treatment variable on the left hand side and pre-treatment covariates to be balanced on the right hand side. The formulas must be in temporal order, and must contain all covariates to be balanced at that time point (i.e., treatments and covariates featured in early formulas should appear in later ones). Interactions and functions of covariates are allowed.
data: an optional data set in the form of a data frame that contains the variables in the formulas in formula.list. This must be a wide data set with exactly one row per unit.
method: a string of length 1 containing the name of the method that will be used to estimate weights. See weightit() for allowable options. The default is "glm", which estimates the weights using generalized linear models.
stabilize: logical; whether or not to stabilize the weights. Stabilizing the weights involves fitting a model predicting treatment at each time point from treatment status at prior time points. If TRUE, a fully saturated model will be fit (i.e., all interactions between all treatments up to each time point), essentially using the observed treatment probabilities in the numerator (for binary and multi-category treatments). This may yield an error if some combinations are not observed. Default is FALSE. To manually specify stabilization model formulas, e.g., to specify non-saturated models, use num.formula. With many time points, saturated models may be time-consuming or impossible to fit.
by: a string containing the name of the variable in data for which weighting is to be done within categories or a one-sided formula with the stratifying variable on the right-hand side. For example, if by = "gender" or by = ~gender, a separate propensity score model or optimization will occur within each level of the variable "gender". Only one by variable is allowed; to stratify by multiply variables simultaneously, create a new variable that is a full cross of those variables using interaction().
s.weights: A vector of sampling weights or the name of a variable in data that contains sampling weights. These can also be matching weights if weighting is to be used on matched data. See the individual pages for each method for information on whether sampling weights can be supplied.
num.formula: optional; a one-sided formula with the stabilization factors (other than the previous treatments) on the right hand side, which adds, for each time point, the stabilization factors to a model saturated with previous treatments. See Cole & Hernán (2008) for a discussion of how to specify this model; including stabilization factors can change the estimand without proper adjustment, and should be done with caution. Can also be a list of one-sided formulas, one for each time point. Unless you know what you are doing, we recommend setting stabilize = TRUE and ignoring num.formula.
moments: numeric; for some methods, the greatest power of each covariate to be balanced. For example, if moments = 3, for each non-categorical covariate, the covariate, its square, and its cube will be balanced. This argument is ignored for other methods; to balance powers of the covariates, appropriate functions must be entered in formula. See the individual pages for each method for information on whether they accept moments.
int: logical; for some methods, whether first-order interactions of the covariates are to be balanced. This argument is ignored for other methods; to balance interactions between the variables, appropriate functions must be entered in formula. See the individual pages for each method for information on whether they accept int.
missing: character; how missing data should be handled. The options and defaults depend on the method used. Ignored if no missing data is present. It should be noted that multiple imputation outperforms all available missingness methods available in weightit() and should probably be used instead. Consider the MatchThem package for the use of weightit() with multiply imputed data.
verbose: logical; whether to print additional information output by the fitting function.
include.obj: whether to include in the output a list of the fit objects created in the process of estimating the weights at each time point. For example, with method = "glm", a list of the glm objects containing the propensity score models at each time point will be included. See the help pages for each method for information on what object will be included if TRUE.
keep.mparts: logical; whether to include in the output components necessary to estimate standard errors that account for estimation of the weights in glm_weightit(). Default is TRUE if such parts are present. See the individual pages for each method for whether these components are produced. Set to FALSE to keep the output object smaller, e.g., if standard errors will not be computed using glm_weightit().
is.MSM.method: whether the method estimates weights for multiple time points all at once (TRUE) or by estimating weights at each time point and then multiplying them together (FALSE). This is only relevant for user-specified functions.
weightit.force: several methods are not valid for estimating weights with longitudinal treatments, and will produce an error message if attempted. Set to TRUE to bypass this error message.
...: other arguments for functions called by weightit() that control aspects of fitting that are not covered by the above arguments. See Details at weightit().

Value

A weightitMSM object with the following elements:

weights: The estimated weights, one for each unit.
treat.list: A list of the values of the time-varying treatment variables.
covs.list: A list of the covariates used in the fitting at each time point. Only includes the raw covariates, which may have been altered in the fitting process.
data: The data.frame originally entered to weightitMSM().
estimand: "ATE", currently the only estimand for MSMs with binary or multi-category treatments.
method: The weight estimation method specified.
ps.list: A list of the estimated propensity scores (if any) at each time point.
s.weights: The provided sampling weights.
by: A data.frame containing the by variable when specified.
stabilization: The stabilization factors, if any.

When keep.mparts is TRUE (the default) and the chosen method is compatible with M-estimation, the components related to M-estimation for use in glm_weightit() are stored in the "Mparts.list" attribute. When by is specified, keep.mparts is set to FALSE.

Details

Currently only "wide" data sets, where each row corresponds to a unit's entire variable history, are supported. You can use reshape() or other functions to transform your data into this format; see example below.

In general, weightitMSM() works by separating the estimation of weights into separate procedures for each time period based on the formulas provided. For each formula, weightitMSM() simply calls weightit() to that formula, collects the weights for each time period, and multiplies them together to arrive at longitudinal balancing weights.

Each formula should contain all the covariates to be balanced on. For example, the formula corresponding to the second time period should contain all the baseline covariates, the treatment variable at the first time period, and the time-varying covariates that took on values after the first treatment and before the second. Currently, only wide data sets are supported, where each unit is represented by exactly one row that contains the covariate and treatment history encoded in separate variables.

The "cbps" method, which calls CBPS() in CBPS, will yield different results from CBMSM() in CBPS because CBMSM() takes a different approach to generating weights than simply estimating several time-specific models.

References

Cole, S. R., & Hernán, M. A. (2008). Constructing Inverse Probability Weights for Marginal Structural Models. American Journal of Epidemiology, 168(6), 656–664. doi:10.1093/aje/kwn164

Examples


library("cobalt")

data("msmdata")
(W1 <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
                        A_2 ~ X1_1 + X2_1 +
                          A_1 + X1_0 + X2_0,
                        A_3 ~ X1_2 + X2_2 +
                          A_2 + X1_1 + X2_1 +
                          A_1 + X1_0 + X2_0),
                   data = msmdata,
                   method = "glm"))
#> A weightitMSM object
#>  - method: "glm" (propensity score weighting with GLM)
#>  - number of obs.: 7500
#>  - sampling weights: none
#>  - number of time points: 3 (A_1, A_2, A_3)
#>  - treatment:
#>     + time 1: 2-category
#>     + time 2: 2-category
#>     + time 3: 2-category
#>  - covariates:
#>     + baseline: X1_0, X2_0
#>     + after time 1: X1_1, X2_1, A_1, X1_0, X2_0
#>     + after time 2: X1_2, X2_2, A_2, X1_1, X2_1, A_1, X1_0, X2_0
summary(W1)
#>                         Time 1                        
#>                   Summary of weights
#> 
#> - Weight ranges:
#> 
#>            Min                                    Max
#> treated 1.0791 |---------------------------| 403.4833
#> control 1.2761 |-------------------|         284.7636
#> 
#> - Units with the 5 most extreme weights by group:
#>                                                      
#>              5488     3440     3593     1286     5685
#>  treated  166.992 170.5549 196.4136 213.1934 403.4833
#>              2594     2932     5226     1875     2533
#>  control 155.6248  168.964 172.4195 245.8822 284.7636
#> 
#> - Weight statistics:
#> 
#>         Coef of Var   MAD Entropy # Zeros
#> treated       1.914 0.816   0.649       0
#> control       1.706 0.862   0.670       0
#> 
#> - Effective Sample Sizes:
#> 
#>            Control Treated
#> Unweighted 3306.    4194. 
#> Weighted    845.79   899.4
#> 
#>                         Time 2                        
#>                   Summary of weights
#> 
#> - Weight ranges:
#> 
#>            Min                                    Max
#> treated 1.0791 |---------------------------| 403.4833
#> control 1.2761 |----------------|            245.8822
#> 
#> - Units with the 5 most extreme weights by group:
#>                                                      
#>              2932     3440     3593     2533     5685
#>  treated  168.964 170.5549 196.4136 284.7636 403.4833
#>              2594     5488     5226     1286     1875
#>  control 155.6248  166.992 172.4195 213.1934 245.8822
#> 
#> - Weight statistics:
#> 
#>         Coef of Var   MAD Entropy # Zeros
#> treated       1.892 0.819   0.652       0
#> control       1.748 0.869   0.686       0
#> 
#> - Effective Sample Sizes:
#> 
#>            Control Treated
#> Unweighted 3701.   3799.  
#> Weighted    912.87  829.87
#> 
#>                         Time 3                        
#>                   Summary of weights
#> 
#> - Weight ranges:
#> 
#>            Min                                    Max
#> treated 1.0791 |---------------------------| 403.4833
#> control 1.2761 |---------|                   148.1547
#> 
#> - Units with the 5 most extreme weights by group:
#>                                                      
#>              3593     1286     1875     2533     5685
#>  treated 196.4136 213.1934 245.8822 284.7636 403.4833
#>              6862      168     3729     6158     3774
#>  control  88.0721  97.8273  104.623 121.8451 148.1547
#> 
#> - Weight statistics:
#> 
#>         Coef of Var   MAD Entropy # Zeros
#> treated       1.832 0.975   0.785       0
#> control       1.254 0.683   0.412       0
#> 
#> - Effective Sample Sizes:
#> 
#>            Control Treated
#> Unweighted 4886.   2614.  
#> Weighted   1900.26  600.12
#> 
bal.tab(W1)
#> Balance summary across all time points
#>        Times    Type Max.Diff.Adj
#> X1_0 1, 2, 3 Contin.       0.0342
#> X2_0 1, 2, 3  Binary       0.0299
#> X1_1    2, 3 Contin.       0.0657
#> X2_1    2, 3  Binary       0.0299
#> A_1     2, 3  Binary       0.0262
#> X1_2       3 Contin.       0.0643
#> X2_2       3  Binary       0.0096
#> A_2        3  Binary       0.0054
#> 
#> Effective sample sizes
#>  - Time 1
#>            Control Treated
#> Unadjusted 3306.    4194. 
#> Adjusted    845.79   899.4
#>  - Time 2
#>            Control Treated
#> Unadjusted 3701.   3799.  
#> Adjusted    912.87  829.87
#>  - Time 3
#>            Control Treated
#> Unadjusted 4886.   2614.  
#> Adjusted   1900.26  600.12

#Using stabilization factors
W2 <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
                        A_2 ~ X1_1 + X2_1 +
                          A_1 + X1_0 + X2_0,
                        A_3 ~ X1_2 + X2_2 +
                          A_2 + X1_1 + X2_1 +
                          A_1 + X1_0 + X2_0),
                   data = msmdata,
                   method = "glm",
                   stabilize = TRUE,
                   num.formula = list(~ 1,
                                      ~ A_1,
                                      ~ A_1 + A_2))

#Same as above but with fully saturated stabilization factors
#(i.e., making the last entry in 'num.formula' A_1*A_2)
W3 <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
                        A_2 ~ X1_1 + X2_1 +
                          A_1 + X1_0 + X2_0,
                        A_3 ~ X1_2 + X2_2 +
                          A_2 + X1_1 + X2_1 +
                          A_1 + X1_0 + X2_0),
                   data = msmdata,
                   method = "glm",
                   stabilize = TRUE)