Docs: Use utility mapping algorithms to help implement cost-utility analyses

Fri, 20 Oct 2023 00:00:00 +0000

This below section renders a vignette article from the youthu library. You can use the following links to:

view the vignette on the library website (adds useful hyperlinks to code blocks)
view the source file from that article, and;
edit its contents (requires a GitHub account).

library(youthu)
set.seed(1234)

This vignette illustrates the rationale for and practical decision-making utility of youthu’s QALYs prediction workflow. Note, this example is illustrated with fake data and should not be used to inform decision-making.

Motivation

The main motivation behind the youthu package is to extend the types of economic analysis that can be undertaken with both single group (e.g. pilot study, health service records) and matched groups (e.g. trial) longitudinal datasets that do not include measures of health utility. This article focuses on its application to matched group datasets.

Example dataset

First, we must first import our data. In this example we will use a fake dataset.

ds_tb <- make_fake_ds_two()
#> Joining with `by = join_by(fkClientID, study_arm_chr)`

Our dataset includes 268 matched comparisons, with each comparison containing baseline and follow-up records for one intervention arm participant and one control arm participant. The first few records are as follows.

First few records from input dataset
fkClientID	round	date_psx	duration_prd	PHQ9	SOFAS	costs_dbl	study_arm_chr	match_idx_int
Participant_20	Baseline	2023-03-15	0S	16	41	301.1868	Intervention	1
Participant_593	Baseline	2023-01-20	0S	19	43	259.3190	Control	1
Participant_593	Follow-up	2023-07-14	175d 0H 0M 0S	16	65	1290.4220	Control	1
Participant_20	Follow-up	2023-09-09	178d 0H 0M 0S	15	74	1787.4242	Intervention	1
Participant_259	Baseline	2023-05-10	0S	19	39	311.0018	Control	2
Participant_962	Baseline	2023-06-22	0S	10	45	276.2181	Intervention	2

This dataset contains features that make it possible to use in conjunction with youthu’s economic analysis functions. These requirements are described in the vignette about finding and using models compatible models to predict QALYs;

The dataset also contains a cost variable, which is a requirement for most, though not all, of the economic analyses that can be undertaken with youthu.

Limitations of datasets without measures of health utility

A notable omission from the dataset is any measure of utility. This omission means that, in the absence of using mapping algorithms such as those included with youthu, the most feasible types of economic evaluation to apply to this dataset would likely be cost-consequence analysis (where a synopsis of the differences in a range of measures are presented alongside cost differences) and cost-effectiveness analysis (where a summary statistic - the incremental cost-effectiveness ratio or ICER - is calculated by dividing differences in costs by differences in a single outcome measure).

These types of economic analyses can be relatively simple to interpret if either the intervention or control arm is simultaneously cheaper and more effective across all included outcome measures. However, these conditions don’t hold in our sample data.

summary((ds_tb %>% dplyr::filter(study_arm_chr == "Control" & round == "Baseline"))[5:6])
#>       PHQ9          SOFAS      
#>  Min.   : 0.0   Min.   :39.00  
#>  1st Qu.: 7.0   1st Qu.:60.00  
#>  Median :12.0   Median :66.00  
#>  Mean   :10.9   Mean   :66.13  
#>  3rd Qu.:15.0   3rd Qu.:72.00  
#>  Max.   :19.0   Max.   :89.00

summary((ds_tb %>% dplyr::filter(study_arm_chr == "Control" & round == "Follow-up"))[5:7])
#>       PHQ9            SOFAS         costs_dbl     
#>  Min.   : 0.000   Min.   :39.00   Min.   : 889.9  
#>  1st Qu.: 4.000   1st Qu.:64.00   1st Qu.:1321.1  
#>  Median : 8.000   Median :71.00   Median :1486.7  
#>  Mean   : 8.493   Mean   :70.65   Mean   :1489.0  
#>  3rd Qu.:13.000   3rd Qu.:77.00   3rd Qu.:1627.0  
#>  Max.   :27.000   Max.   :98.00   Max.   :2216.5

summary((ds_tb %>% dplyr::filter(study_arm_chr == "Intervention" & round == "Baseline"))[5:6])
#>       PHQ9           SOFAS      
#>  Min.   : 0.00   Min.   :36.00  
#>  1st Qu.: 7.00   1st Qu.:61.00  
#>  Median :11.00   Median :67.00  
#>  Mean   :10.81   Mean   :66.74  
#>  3rd Qu.:15.00   3rd Qu.:72.25  
#>  Max.   :19.00   Max.   :88.00

summary((ds_tb %>% dplyr::filter(study_arm_chr == "Intervention" & round == "Follow-up"))[5:7])
#>       PHQ9            SOFAS      costs_dbl     
#>  Min.   : 0.000   Min.   :40   Min.   : 923.4  
#>  1st Qu.: 2.000   1st Qu.:60   1st Qu.:1625.6  
#>  Median : 6.500   Median :68   Median :1777.3  
#>  Mean   : 6.851   Mean   :68   Mean   :1807.8  
#>  3rd Qu.:11.000   3rd Qu.:77   3rd Qu.:1996.0  
#>  Max.   :25.000   Max.   :93   Max.   :2872.7

The pattern of results summarised above create some significant barriers to meaningfully interpreting economic evaluations that are based on cost-consequence or cost-effectiveness analysis:

A cost-effectiveness analysis in which change in PHQ-9 was the benefit measure would be difficult to interpret as the Intervention arm is both more effective and more costly, which begs the question is it worth paying the extra dollars for this improvement? Also - would a judgment of cost-effectiveness remain the same if the study had measured a slightly different incremental benefit or recorded change over a longer or shorter time horizon? It is likely that there is no commonly used value for money benchmark for improvements measured in PHQ-9, nor is there any time weighting associated with the measure. Furthermore, if the potential funding for the intervention is from a budget that is allocated to non-depressive illnesses (e.g. physical health), results from a cost-effectiveness analysis using PHQ-9 as its benefit measure are not readily comparable with economic evaluations of interventions from other illness groups using different benefit measures that are potentially competing for the same scarce funding.
A cost consequence analyses that summarised the differences in costs with the differences in changes in PHQ-9 and SOFAS score would be difficult to interpret because while the intervention is more effective than control for improvements measured on PHQ-9 (where lower scores are better), the control group is superior if benefits are based on functioning improvements as measured by SOFAS scores (where higher scores are better). The lack of any formal weighting for how to trade off clinical symptoms and functioning means that interpretation of this analysis will be highly subjective and likely to change across potential decision makers.

These types of short-comings can be significantly addressed by undertaking cost-utility analyses (CUAs) as:

they use a measure of benefit - the Quality Adjusted Life Year (QALY) - that captures multiple domains of health, weighted by time and population preferences in a single index measure that can be applied across health conditions;
there are published benchmark willingness to pay values for QALYs that are routinely used by decision makers in many countries to make ICER statistics readily interpretable in the context of health budget allocation.

The rest of this article demonstrates how youthu functions can be used to undertake CUA based analyses on the type of data we have just profiled.

Using youthu in a cost-utility analysis workflow

Predict adolescent AQoL-6D health utility

Our first step is to identify which youthu models we will use to predict adolescent AQoL-6D and apply these models to our data. This step was explained in more detail in another vignette article about finding and using transfer to utility models, so will be dealt with briefly here.

First we make sure that our dataset can be used as a prediction dataset in conjunction with the model we intend using.

predn_ds_ls <- make_predn_metadata_ls(ds_tb,
                                      cmprsn_groups_chr = c("Intervention", "Control"),
                                      cmprsn_var_nm_1L_chr = "study_arm_chr",
                                      costs_var_nm_1L_chr = "costs_dbl",
                                      id_var_nm_1L_chr = "fkClientID",
                                      msrmnt_date_var_nm_1L_chr = "date_psx",
                                      round_var_nm_1L_chr = "round",
                                      round_bl_val_1L_chr = "Baseline",
                                      utl_var_nm_1L_chr = "AQoL6D_HU",
                                      mdls_lup = get_mdls_lup(utility_type_chr = "AQoL-6D",
                                                              mdl_predrs_in_ds_chr = c("PHQ9 total score",
                                                                                       "SOFAS total score"),
                                                              ttu_dv_nms_chr = "TTU"),
                                      mdl_nm_1L_chr =  "PHQ9_SOFAS_1_OLS_CLL")

We now use our preferred model to predict health utility from the measures in our dataset.

ds_tb <- add_utl_predn(ds_tb,
                       predn_ds_ls = predn_ds_ls) %>%
  dplyr::select(fkClientID, round, study_arm_chr, date_psx, duration_prd, dplyr::everything())
#> Joining with `by = join_by(fkClientID, round)`

Calculate QALYs

Next we combine the health utility data with the interval between measurement data to calculate QALYs and add them to the dataset.

ds_tb  <- ds_tb %>% add_qalys_to_ds(predn_ds_ls = predn_ds_ls,
                                    include_predrs_1L_lgl = T,
                                    reshape_1L_lgl = T)

First few records from updated dataset with QALYs
fkClientID	study_arm_chr	match_idx_int	date_psx_Baseline	date_psx_Follow-up	duration_prd_Baseline	duration_prd_Follow-up	costs_dbl_Baseline	costs_dbl_Follow-up	PHQ9_Baseline	PHQ9_Follow-up	SOFAS_Baseline	SOFAS_Follow-up	AQoL6D_HU_Baseline	AQoL6D_HU_Follow-up	PHQ9_change_dbl_Baseline	PHQ9_change_dbl_Follow-up	SOFAS_change_dbl_Baseline	SOFAS_change_dbl_Follow-up	AQoL6D_HU_change_dbl_Baseline	AQoL6D_HU_change_dbl_Follow-up	qalys_dbl_Baseline	qalys_dbl_Follow-up
Participant_10	Control	243	2022-12-29	2023-06-24	0S	177d 0H 0M 0S	647.9386	1696.235	8	10	61	64	0.7597988	0.6079774	0	2	0	3	0	-0.1518214	0	0.3314119
Participant_1000	Control	191	2023-02-24	2023-08-27	0S	184d 0H 0M 0S	428.9205	1619.037	4	2	63	82	0.8459579	0.7688131	0	-2	0	19	0	-0.0771448	0	0.4067322
Participant_1001	Intervention	230	2023-01-19	2023-07-17	0S	179d 0H 0M 0S	429.3703	1844.219	10	14	59	72	0.6138300	0.8607305	0	4	0	13	0	0.2469005	0	0.3613228
Participant_1003	Intervention	115	2023-02-17	2023-08-18	0S	182d 0H 0M 0S	395.1637	1537.365	9	0	71	81	0.5808015	0.9315788	0	-9	0	10	0	0.3507773	0	0.3768011
Participant_1005	Intervention	183	2023-05-21	2023-11-23	0S	186d 0H 0M 0S	402.9910	1826.511	17	0	78	88	0.5460607	0.9593811	0	-17	0	10	0	0.4133204	0	0.3833158
Participant_1006	Intervention	219	2023-06-16	2023-12-12	0S	179d 0H 0M 0S	534.2285	2401.478	9	14	75	73	0.7239490	0.5885972	0	5	0	-2	0	-0.1353518	0	0.3216232

Analyse results

Now we can run the main economic analysis. This is implemented by the make_hlth_ec_smry function, which first bootstraps the dataset (implemented by the boot function from the boot package) before passing the mean values for costs and QALYs from each bootstrap sample to with bcea function of the BCEA package to calculate a range of health economic statistics. For this example we pass a value of 50,000 for the willingness to pay parameter, as this is the dollar amount commonly used in Australia as a benchmark for the value of a QALY.

Note, for this illustrative example we only request 1000 bootstrap iterations - in practice this number may be higher.

he_smry_ls <- ds_tb %>% make_hlth_ec_smry(predn_ds_ls = predn_ds_ls,
                                                 wtp_dbl = 50000,
                                                 bootstrap_iters_1L_int = 1000L)
#> Warning: There was 1 warning in `dplyr::summarise()`.
#> ℹ In argument: `dplyr::across(.fns = mean)`.
#> Caused by warning:
#> ! Using `across()` without supplying `.cols` was deprecated in dplyr 1.1.0.
#> ℹ Please supply `.cols` instead.

As part of the output of the make_hlth_ec_smry function is a BCEA object, we can use the BCEA package to produce a number of graphical summaries of economic results. One of the most important is the production of a cost-effectiveness plane. This plot highlights that, with an ICER of $-98,145.56, less than half of the bootstrapped iteration incremental cost and QALY pairs fall within the zone of cost-effectiveness (green). In fact, at the cost-effectiveness threshold we supplied, the results suggest there is a 8% probability that the intervention is cost-effective.

library(ggplot2)
BCEA::ceplane.plot(he_smry_ls$ce_res_ls, wtp =50000,    
                   area_color = "green",
                    graph = "ggplot2",
          theme = ggplot2::theme_light())
#> Warning: Duplicated aesthetics after name standardisation: colour

ready4 – Use - cost-utility