Predict health utility from psychological and functional measures (PHQ-9 and SOFAS)
Source:vignettes/Prediction_With_Mdls.Rmd
Prediction_With_Mdls.RmdThis vignette outlines a workflow for:
- Searching, selecting and retrieving transfer to utility models;
- Preparing a prediction dataset for use with a selected transfer to utility model; and
- Applying the selected transfer to utility model to a prediction dataset to predict Quality Adjusted Life Years (QALYs).
The practical value of implementing such a workflow is discussed in the economic analysis vignette and a scientific manuscript. Note, this example uses fake data - it should should not be used to inform decision making.
Search, select and retrieve transfer to utility models
To identify datasets that contain transfer to utility models
compatible with youthu (ie those developped with the TTU package), you
can use the get_ttu_dv_dss function. The function searches
specified dataverses (in the below example, the TTU dataverse)
for datasets containing output from the TTU package.
ttu_dv_dss_tb <- get_ttu_dv_dss("TTU")The ttu_dv_dss_tb table summarises some pertinent
details about each dataset containing TTU models found by the preceding
command. These details include a link to any scientific summary (the
“Article” column) associated with a dataset.
| ID | Utility | Predictors | Article |
|---|---|---|---|
| 1 | aqol6dtotalw | BADS total score , GAD7 total score , K6 total score , OASIS total score , PHQ9 total score , SCARED total score, SOFAS total score | https://doi.org/10.1101/2021.07.07.21260129 |
To identify models that predict a specified type of health utility from one or more of a specified subset of predictors, use:
mdls_lup <- get_mdls_lup(ttu_dv_dss_tb = ttu_dv_dss_tb,
utility_type_chr = "AQoL-6D",
mdl_predrs_in_ds_chr = c("PHQ9 total score",
"SOFAS total score"))The preceding command will produce a lookup table with information that includes the catalogue names of models, the predictors used in each model and the analysis that generated each one.
| Catalogue reference | Predictors | Analysis |
|---|---|---|
| PHQ9_1_GLM_GSN_LOG | PHQ9 | Primary Analysis |
| PHQ9_1_OLS_CLL | PHQ9 | Primary Analysis |
| PHQ9_SOFAS_1_GLM_GSN_LOG | PHQ9 , SOFAS | Primary Analysis |
| PHQ9_SOFAS_1_OLS_CLL | PHQ9 , SOFAS | Primary Analysis |
| OASIS_SOFAS_1_GLM_GSN_LOG | OASIS, SOFAS | Primary Analysis |
| OASIS_SOFAS_1_OLS_CLL | OASIS, SOFAS | Primary Analysis |
| BADS_SOFAS_1_GLM_GSN_LOG | BADS , SOFAS | Primary Analysis |
| BADS_SOFAS_1_OLS_CLL | BADS , SOFAS | Primary Analysis |
| K6_SOFAS_1_GLM_GSN_LOG | K6 , SOFAS | Primary Analysis |
| K6_SOFAS_1_OLS_CLL | K6 , SOFAS | Primary Analysis |
| SCARED_SOFAS_1_GLM_GSN_LOG | SCARED, SOFAS | Primary Analysis |
| SCARED_SOFAS_1_OLS_CLL | SCARED, SOFAS | Primary Analysis |
| GAD7_SOFAS_1_GLM_GSN_LOG | GAD7 , SOFAS | Primary Analysis |
| GAD7_SOFAS_1_OLS_CLL | GAD7 , SOFAS | Primary Analysis |
| SOFAS_1_GLM_GSN_LOG | SOFAS | Secondary Analysis A |
| SOFAS_1_OLS_CLL | SOFAS | Secondary Analysis A |
| OASIS_PHQ9_1_GLM_GSN_LOG | OASIS, PHQ9 | Secondary Analysis B |
| OASIS_PHQ9_1_OLS_CLL | OASIS, PHQ9 | Secondary Analysis B |
| GAD7_PHQ9_1_GLM_GSN_LOG | GAD7, PHQ9 | Secondary Analysis B |
| GAD7_PHQ9_1_OLS_CLL | GAD7, PHQ9 | Secondary Analysis B |
| SCARED_PHQ9_1_GLM_GSN_LOG | SCARED, PHQ9 | Secondary Analysis B |
| SCARED_PHQ9_1_OLS_CLL | SCARED, PHQ9 | Secondary Analysis B |
To review the summary information about the predictive performance of a specific model, use:
get_dv_mdl_smrys(mdls_lup,
mdl_nms_chr = "PHQ9_SOFAS_1_OLS_CLL")## $PHQ9_SOFAS_1_OLS_CLL
## Parameter Estimate SE 95% CI
## 1 SD (Intercept) 0.348 0.017 0.312 , 0.382
## 2 Intercept 0.428 0.129 0.174 , 0.686
## 3 PHQ9 baseline -9.115 0.249 -9.601 , -8.618
## 4 PHQ9 change -7.331 0.339 -8.007 , -6.665
## 5 SOFAS baseline 0.960 0.172 0.616 , 1.292
## 6 SOFAS change 1.146 0.235 0.674 , 1.607
## 7 R2 0.767 0.012 0.743 , 0.788
## 8 RMSE 0.925 0.004 0.922 , 0.928
## 9 Sigma 0.406 0.012 0.384 , 0.429More information about a selected model can be found in the online model catalogue, the link to which can be obtained with the following command:
get_mdl_ctlg_url(mdls_lup,
mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")[1] “https://dataverse.harvard.edu/api/access/datafile/6484935”
Prepare a prediction dataset for use with a selected transfer to utility model
Import data
You can now import and inspect the dataset you plan on using for prediction. In the below example we use fake data.
data_tb <- make_fake_ds_one()| UID | Timepoint | Date | PHQ_total | SOFAS_total |
|---|---|---|---|---|
| Participant_1 | Baseline | 2022-12-20 | 7 | 69 |
| Participant_10 | Baseline | 2022-11-16 | 17 | 60 |
| Participant_10 | Follow-up | 2023-02-21 | 17 | 64 |
| Participant_100 | Baseline | 2023-01-31 | 0 | 76 |
| Participant_1000 | Baseline | 2023-02-05 | 0 | 71 |
| Participant_1000 | Follow-up | 2023-04-10 | 0 | 71 |
Confirm dataset can be used as a prediction dataset
The prediction dataset must contain variables that correspond to all
the predictors of the model you intend to apply. The allowable range and
required class of each predictor variable are described in the
min_val_dbl, max_val_dbl and
class_chr columns of the model predictors lookup table,
which can be accessed with a call to the get_predictors_lup
function.
predictors_lup <- get_predictors_lup(mdls_lup = mdls_lup,
mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")| short_name_chr | long_name_chr | min_val_dbl | max_val_dbl | class_chr | increment_dbl | class_fn_chr | mdl_scaling_dbl | covariate_lgl |
|---|---|---|---|---|---|---|---|---|
| PHQ9 | PHQ9 total score | 0 | 27 | integer | 1 | youthvars::youthvars_phq9 | 0.01 | FALSE |
| SOFAS | SOFAS total score | 0 | 100 | integer | 1 | youthvars::youthvars_sofas | 0.01 | TRUE |
The prediction dataset must also include both a unique client
identifier variable and a measurement time-point identifier variable
(which must be a factor with two
levels). The dataset also needs to be in long format (ie where measures
at different time-points for the same individual are stacked on top of
each other in separate rows). We can confirm these conditions hold by
creating a dataset metadata object using the
make_predn_metadata_ls function. In creating the metadata
object, the function checks that the dataset can be used in conjunction
with the model specified at the mdl_nm_1L_chr argument. If
the prediction dataset uses different variable names for the predictors
to those specified in the predictors_lup lookup table, a named vector
detailing the correspondence between the two sets of variable names
needs to be passed to the predr_vars_nms_chr argument.
Finally, if you wish to specify a preferred variable name to use for the
predicted utility values when applying the model, you can do this by
passing this name to the utl_var_nm_1L_chr argument.
predn_ds_ls <- make_predn_metadata_ls(data_tb,
id_var_nm_1L_chr = "UID",
msrmnt_date_var_nm_1L_chr = "Date",
predr_vars_nms_chr = c(PHQ9 = "PHQ_total",SOFAS = "SOFAS_total"),
round_var_nm_1L_chr = "Timepoint",
round_bl_val_1L_chr = "Baseline",
utl_var_nm_1L_chr = "AQoL6D_HU",
mdls_lup = mdls_lup,
mdl_nm_1L_chr = "PHQ9_SOFAS_1_OLS_CLL")Apply the selected transfer to utility model to a prediction dataset to predict Quality Adjusted Life Years (QALYs)
Predict health utility at baseline and follow-up timepoints
To generate utility predictions we use the add_utl_predn function. The function needs
to be supplied with the prediction dataset (the value passed to argument
data_tb) and the validated prediction metadata object we
created in the previous step.
data_tb <- add_utl_predn(data_tb,
predn_ds_ls = predn_ds_ls)## Joining with `by = join_by(UID, Timepoint)`By default the add_utl_predn
function samples model parameter values based on a table of model
coefficients when making predictions and constrains predictions to an
allowed range. You can override these defaults by adding additional
arguments new_data_is_1L_chr = "Predicted" (which uses mean
parameter values), force_min_max_1L_lgl = F (removes range
constraint) and (if the source dataset makes available downloadable
model objects) make_from_tbl_1L_lgl = F. These settings
will produce different predictions. It is strongly recommended that you
consult the model catalogue (see above) to understand how such decisions
may affect the validity of the predicted values that will be
generated.
| UID | Timepoint | Date | PHQ_total | SOFAS_total | AQoL6D_HU |
|---|---|---|---|---|---|
| Participant_1 | Baseline | 2022-12-20 | 7 | 69 | 0.6039160 |
| Participant_10 | Baseline | 2022-11-16 | 17 | 60 | 0.4661496 |
| Participant_10 | Follow-up | 2023-02-21 | 17 | 64 | 0.3535036 |
| Participant_100 | Baseline | 2023-01-31 | 0 | 76 | 0.9939090 |
| Participant_1000 | Baseline | 2023-02-05 | 0 | 71 | 0.9999991 |
| Participant_1000 | Follow-up | 2023-04-10 | 0 | 71 | 0.9774732 |
Our health utility predictions are now available for use and are summarised below.
summary(data_tb$AQoL6D_HU)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.04121 0.44149 0.64419 0.62758 0.83702 1.00000Calculate QALYs
The last step is to calculate Quality Adjusted Life Years, using a method assuming a linear rate of change between timepoints.
data_tb <- data_tb %>% add_qalys_to_ds(predn_ds_ls = predn_ds_ls,
include_predrs_1L_lgl = F,
reshape_1L_lgl = F)| UID | Timepoint | Date | PHQ_total | SOFAS_total | AQoL6D_HU | AQoL6D_HU_change_dbl | duration_prd | qalys_dbl |
|---|---|---|---|---|---|---|---|---|
| Participant_1 | Baseline | 2022-12-20 | 7 | 69 | 0.6039160 | 0.0000000 | 0S | 0.0000000 |
| Participant_10 | Baseline | 2022-11-16 | 17 | 60 | 0.4661496 | 0.0000000 | 0S | 0.0000000 |
| Participant_10 | Follow-up | 2023-02-21 | 17 | 64 | 0.3535036 | -0.1126460 | 97d 0H 0M 0S | 0.1088383 |
| Participant_100 | Baseline | 2023-01-31 | 0 | 76 | 0.9939090 | 0.0000000 | 0S | 0.0000000 |
| Participant_1000 | Baseline | 2023-02-05 | 0 | 71 | 0.9999991 | 0.0000000 | 0S | 0.0000000 |
| Participant_1000 | Follow-up | 2023-04-10 | 0 | 71 | 0.9774732 | -0.0225259 | 64d 0H 0M 0S | 0.1732488 |