Sage Journals: Discover world-class research

Abstract

Introduction. Patient-level simulation models facilitate extrapolation of clinical trial data while allowing for heterogeneity, prior history, and nonlinearity. However, combining different types of uncertainty around within-trial and extrapolated results remains challenging. Methods. We tested 4 methods to combine parameter uncertainty (around the regression coefficients used to predict future events) with sampling uncertainty (uncertainty around mean risk factors within the finite sample whose outcomes are being predicted and the effect of treatment on these risk factors). We compared these 4 methods using a simulation study based on an economic evaluation extrapolating the AFORRD randomized controlled trial using the UK Prospective Diabetes Study Outcomes Model version 2. This established type 2 diabetes model predicts patient-level health outcomes and costs. Results. The 95% confidence intervals around life years gained gave 25% coverage when sampling uncertainty was excluded (i.e., 25% of 95% confidence intervals contained the “true” value). Allowing for sampling uncertainty as well as parameter uncertainty widened confidence intervals by 6.3-fold and gave 96.3% coverage. Methods adjusting for baseline risk factors that combine sampling and parameter uncertainty overcame the bias that can result from between-group baseline imbalance and gave confidence intervals around 50% wider than those just considering parameter uncertainty, with 99.8% coverage. Conclusions. Analyses extrapolating data for individual trial participants should include both sampling uncertainty and parameter uncertainty and should adjust for any imbalance in baseline covariates.

Keywords

diabetes patient-level simulation models randomized controlled trial decision-analytical modeling

Decision-analytical models are increasingly used to extrapolate individual-participant data (IPD) from randomized controlled trials (RCTs). For chronic diseases, extrapolation is almost unavoidable, as interventions can take many years before they have a measurable impact on clinical outcomes. Extrapolation may also be needed when the primary outcome of a trial is a proxy or intermediate outcome, such as a change in a known risk factor. It is particularly relevant for economic evaluations, which generally need to use a lifetime time horizon and estimate outcomes such as life expectancy or quality-adjusted life years (QALYs).¹ Patient-level simulation (PLS) models are now in routine use. For example, in the United Kingdom Prospective Diabetes Study Outcomes Model version 2 (UKPDS-OM2), patient characteristics and prior events affect the risk or consequences of subsequent events in type 2 diabetes.^2,3 Such dependencies are more difficult to capture in aggregate “cohort” models. Furthermore, when PLS models are used to extrapolate IPD, it becomes easier to capture heterogeneity and to account for the effect of patient history on the risk of subsequent events. By modeling the entire distribution of risk factors rather than the “average” patient, such models can also allow for nonlinear effects of patient characteristics on model outcomes and allow for treatment changes that are based on individual rather than aggregate outcomes.^4,5

The increased flexibility offered by PLS models comes at the expense of greater computational burden and additional complexity concerning how to handle uncertainty in the resulting estimates of cost-effectiveness or life expectancy. The reporting of uncertainty is often far from transparent. For example, although uncertainty in economic evaluations arises from several different sources, few published analyses attempt to systematically decompose the various contributions to overall uncertainty. Several publications have attempted to define taxonomies for either trial- or model-based economic evaluations,^6–8 but these existing taxonomies do not explicitly discuss uncertainty arising from missing data and do not make specific recommendations on how uncertainty in PLS models should be handled.

Studies that combine IPD and model-based extrapolations are subject to the uncertainties inherent within both types of analyses. Extrapolating RCT data using a model requires combining data from 2 distinct time periods: observed IPD over the within-trial period in which specific individuals are followed up on assigned interventions and simulated IPD during the modeled posttrial period in which outcomes depend not only on patient characteristics but also on model parameters. However, there has been little research on methods for combining uncertainty in this context, in contrast to the well-established methods for combining different types of uncertainty within multiple imputation.^9,10

Our study aims to offer suggestions for improving analytic methods and reporting standards for PLS-based extrapolations of trial data by 1) developing and comparing methods for combining sampling uncertainty and parameter uncertainty and 2) assessing the impact of including stochastic uncertainty. After setting out general principles, we compare different methods in a simulation study based on a case study in which the UKPDS-OM2^2,3 was used to extrapolate results of an RCT (the Atorvastatin in a Factorial design with Omega-3 fatty acids on cardiovascular Risk Reduction in patients with Type 2 Diabetes study, AFORRD¹¹).

Uncertainty for Studies Extrapolating Trial Data Using PLS

We combined published taxonomies categorizing the types of uncertainty affecting model-based^6,7 and trial-based^7,8 economic evaluations (Table 1). In this article, we focus on methods to combine sampling uncertainty and parameter uncertainty. Parameter uncertainty comprises uncertainty around model inputs (e.g., utilities, costs, and probabilities) and is often handled using probabilistic sensitivity analysis (PSA).^1,6,12

Table 1

Taxonomy of the Types of Uncertainty Affecting Trial-Based Economic Evaluations and Other Studies Extrapolating Randomized Controlled Trial Data Using Patient-Level Simulation Models

Type of Uncertainty	Subtypes	Methods to Represent Uncertainty	Example within AFORRD Extrapolation Using UKPDS-OM2
1) Methodological uncertainty	Perspective, time horizon, discount rate, choice of comparators, outcome^6,7	Sensitivity analysis Reference case improves comparability between studies¹	Reference case Sensitivity analyses will be reported separately
2) Sampling variation/heterogeneity/sampling uncertainty	Variability in patients’ costs and outcomes during the randomized trial period	Statistical analysis: we develop methods for combining sampling uncertainty with parameter uncertainty and adjusting for baseline characteristics Subgroup analyses	No variability in life years (no patients died); individual patients’ within-trial life years were added to those in the extrapolated period
	Variability between patients in the sample		Extrapolated outcomes for each individual in the trial population
	Sampling uncertainty around mean within-trial treatment effects (if individual trial participants are extrapolated, as randomized)^a		Extrapolated outcomes for each individual separately, as randomized
3) Parameter uncertainty (second-order uncertainty)^1,6,12	Uncertainty around treatment effects (if these are explicitly modeled)^a	PSA	Not applicable
	Uncertainty around costs and utilities associated with health states/events	PSA or bootstrapping	Not applicable (focus on life expectancy)
	Uncertainty around values or parametric equations predicting probabilities of events/transitions	PSA or bootstrapping	Used 800 sets of bootstrapped risk equation parameters
	Uncertainty around trajectories of risk factors beyond the trial period (if parameterized)^a	PSA or bootstrapping	Not applicable (assumed constant risk factors)
4) Stochastic uncertainty (MCE)		Report MCE and/or impact of MCE on resultsRun sufficient loops that MCE is negligible^5,17,18O’Hagan et al.¹⁷ present ANOVA-based methods for eliminating bias	First set of analyses used sufficient loops and bootstraps to minimize MCE.Second set of analyses assesses the effect of including stochastic uncertainty in SEs.
5) Structural/model uncertainty³⁴	Uncertainty around the assumptions about risk factor trajectories beyond the trial period^a	Sensitivity analysis	Not captured (sensitivity analysis reported separately)
	Uncertainty concerning the duration of treatment effect beyond the trial period	Sensitivity analysis	Not captured
	Selection of the specification of extrapolation models: functional form, which variables to include	Scenario analysis, model selection or averaging, parameterization, and estimating discrepancies between model predictions and reality^34,35	Not captured
	Uncertainty around appropriate health states	Sensitivity analysis where practical	Not captured
	Modeling process	Independent teams develop separate models	Not captured
6) Generalizability/transferability uncertainty³⁶	Multinational/multicenter trials	Sensitivity analysis	Not captured
	Applicability to different countries	Sensitivity analysis	Not captured
	External validity of the trial	Sensitivity analysis	Not captured
7) Imputation uncertainty	Uncertainty around the true value for missing data that should have been collected within the trial period (before censoring)	Multiple imputation and simulation of multiple imputationsSensitivity analysis on imputation methods^13,14	Not captured (complete case analysis)
7) Imputation uncertainty	Uncertainty around the true value for patients after they were administratively or informatively censored before the maximum trial follow-up	Kaplan-Meier sample averaging, inverse probability weighting, imputation, or model-based extrapolationSensitivity analysis on methods	Not relevant to the 16-week trial

AFORRD, Atorvastatin in a Factorial design with Omega-3 fatty acids on cardiovascular Risk Reduction in patients with Type 2 Diabetes study; ANOVA, analysis of variance; MCE, Monte Carlo error; PSA, probabilistic sensitivity analysis; SE, standard error; UKPDS-OM2, United Kingdom Prospective Diabetes Study Outcomes Model version 2.

These subtypes of uncertainty appear in the table twice as they could fall into 1 of 2 categories depending on the methods used.

Both sampling variation and patient heterogeneity arise in studies using IPD or in model-based economic evaluations that extrapolate outcomes for a finite sample of nonidentical individuals. Trial outcomes will vary between individual participants by chance: for example, some patients will have events and others with identical risk factors will not. Unlike stochastic uncertainty within a PLS model, we cannot eliminate this chance variation by running millions of model runs. Some of the variability may be explained by patients’ baseline characteristics; this explainable variation is termed heterogeneity and could be dealt with using subgroup analysis and/or regression analyses. However, sampling uncertainty (i.e., nonzero standard errors [SEs]) around mean outcomes will remain even after controlling for all observed baseline variables, since RCTs use finite samples and do not provide perfect information. For example, there may be sampling uncertainty around the mean baseline and posttreatment characteristics of the sample of patients recruited to the trial and the absolute or incremental costs, life years, and QALYs accrued during the trial period; variations between patients at the start of the simulation will also propagate sampling uncertainty into estimates of mean costs or life expectancy in the period extrapolated using the PLS. When calculating SEs around mean outcomes for a group of patients, we need to use statistical methods to allow for the fact that the sample is finite and was sampled from a larger population, particularly when estimating differences between treatments.

Methods for Quantifying and Combining Parameter and Sampling Uncertainty

When analyzing results, it is necessary to combine different forms of uncertainty to produce an overall SE or 95% confidence interval (CI) around estimates of mean lifetime outcomes.ⁱ However, methods to do so are currently underdeveloped. Several methods could be used to combine parameter and sampling uncertainty.

Rubin’s rule^9,10 is widely used following multiple imputation of missing data to combine uncertainty around imputed values with sampling variation from a trial.^13,14 This method involves adding the variance between imputed data sets to the variance within each imputation.^9,10 PLS models, such as the UKPDS-OM2, can be viewed as a form of multiple imputation, in which the model is used to predict or impute missing values for all participants after the end of the trial, with the model predictions using M sets of model parameters (sampled using PSA or bootstrapping) representing M imputations. On that basis, the original application of UKPDS-OM version 1 (UKPDS-OM1) adjusted 95% CIs to take account of the variance within and between different bootstraps using Rubin’s rule,^3,15 although to our knowledge, no such adjustment has been made in any subsequent application of UKPDS-OM1 or UKPDS-OM2.

In multiple imputation, it is recommended that imputed values include random components that take into account the SEs around the regression coefficients predicting the missing values (i.e., parameter uncertainty), the residuals from the regression (i.e., stochastic uncertainty), and the patients’ characteristics (i.e., heterogeneity).^13,16 In PLS models, stochastic (or first-order) uncertainty arises because the occurrence of an event depends on both its probability and chance, via Monte Carlo methods,⁶ and is measured using Monte Carlo error (MCE). If we view the PLS as a “multiple imputation” model, it could be argued that MCE should be included to mirror the lifetime trial data that are “missing.” However, in general, it is recommended that PLS models eliminate MCE by running large numbers of Monte Carlo trials and averaging the results to produce consistent outcomes^5,6,17,18: otherwise, MCE will bias and inflate SEs.¹⁷ This approach views stochastic uncertainty as random noise resulting from the computer simulation, rather than genuine uncertainty around the evidence or methods.

Alternative approaches can also be used to combine parameter and sampling uncertainty. In the variance sum law, uncertainty around 2 random variables is combined by summing the variances (minus any covariance) to calculate the variance around the sum or difference between the variables. Similarly, analysis of variance (ANOVA) partitions variances into different factors that can be summed together.¹⁹ Nonparametric techniques, such as bootstrapping, can also be used to combine uncertainty around correlated endpoints.^3,8 Parametric methods such as PSA⁸ or Markov chain Monte Carlo²⁰ may also be used to synthesize data from different sources and quantify uncertainty, although these are outside the scope of the current study since they cannot easily be applied to the way that we extrapolated trial data using the UKPDS-OM2.

There may also be chance differences in baseline characteristics between randomized groups. It is well established that baseline imbalance in EQ-5D utility biases estimates of incremental QALYs, since baseline utility is directly included in the estimates of QALYs for each patient.²¹ However, to our knowledge, it has not previously been recognized that the same is true for baseline imbalance in the risk factors used to extrapolate IPD within PLS models like the UKPDS-OM2. For example, if the patients in the control arm of an RCT are older than in the treatment group, this chance difference will increase event rates and decrease life expectancy and QALYs within the control arm as event rates and mortality increase with age in the model; unless we adjusted for this baseline imbalance, we would therefore overestimate the benefits of treatment. Appendix 1 gives a worked example illustrating this bias. In practice, it is unlikely that any RCT will be perfectly balanced for all 17 risk factors in UKPDS-OM2, and the combined effect of multiple interacting risk factors may be difficult to predict. Baseline imbalance could also introduce similar biases for within-trial outcomes that are correlated with baseline characteristics,²² although the problem may be greater for models where the structure imposes/formalizes relationships between end-of-trial characteristics and outcome. Baseline imbalance will decrease with increasing sample size,²³ and its impact will depend on the correlation between baseline variables and outcome.²² It may have a greater effect on posttreatment endpoints²² or absolute differences in event rates, life years, QALYs, or costs compared with relative effects (e.g., hazard ratios) or changes in intermediate endpoints (e.g., change in blood pressure).

Adjusting for baseline covariates can also increase precision.^22,24–26 However, it is essential to ensure that analyses are adjusted only for data observed before randomization, rather than risk factors that may have been affected by an intervention.

Given the matrix of predicted outcomes for each parameter set with each patient, it is possible to estimate regression models that adjust for baseline covariates using several convenient Stata commands (e.g., mim, micombine, and mi estimate) that apply Rubin’s rule to regression functions.

Methods

Case Study

AFORRD (ISRCTN: 76737502) was a 1-year factorial double-blind RCT in which 800 patients with type 2 diabetes and no history of cardiovascular events were randomized to 20 mg atorvastatin or placebo and, simultaneously, to omega-3 EE90 or placebo.^11,27 General practices were also cluster-randomized to receive a paper-based behavioral intervention intended to increase compliance with study medication or to standard care.²⁷ Since high-risk patients in both arms were given additional atorvastatin after week 16,¹¹ we extrapolated 16-week clinical outcomes for 30 years and focus here on a simple comparison of total undiscounted life expectancy between atorvastatin and no atorvastatin, with no adjustment for clustering. A 30-year time horizon was chosen to cover the life expectancy of the average trial participant (mean age, 64 years) while minimizing simulation time. We assumed that all risk factors other than age and event history would remain constant at the values observed at the end of the 16-week trial period.ⁱⁱ For simplicity, we used a complete-case analysis, including only the 732 patients with complete 16-week clinical data. Mean imputation was used for the 36 patients with missing baseline EQ-5D utility. Smoking status was assumed to be the same as at baseline for 3 patients with missing data on smoking status at week 16. All other 16-week data were either fully observed (if the patient attended the 16-week visit) or completely missing. A full economic evaluation reporting sensitivity analyses will be reported separately.

We predicted life expectancy for each individual participating in AFORRD based on their 16-week risk factor data using an adapted version of the UKPDS-OM2 that saved the outputs required for our regression analyses.

The UKPDS-OM2 is a second-generation lifetime individual-patient state transition model for people with type 2 diabetes mellitus.^2,28 It was constructed using IPD from up to 89,760 patient years of follow-up in the UKPDS. UKPDS-OM2 simulates the occurrence of death and 8 diabetes-related complications (myocardial infarction, ischemic heart disease, stroke, heart failure, amputation, renal failure, diabetic foot ulcer, and blindness in 1 eye) to estimate life expectancy, QALYs, and costs. It is based on an integrated system of parametric equations that predict the annual probability of death and complications; all model parameters have been published and internally validated previously.²

Parameter uncertainty around these risk equations is captured using 5000 sets of correlated risk equation coefficients: the 15 regression equations predicting diabetic events and mortality were jointly estimated on each bootstrap sample drawn, with replacement, from UKPDS trial population to allow for correlations between coefficients across all 15 regression models. Monte Carlo methods are used to randomly simulate a possible medical history for each patient in each “loop” of the model.² The likelihood of events for each patient is based on the following risk factors: patient demographics, smoking, diabetes duration, body mass index, high- and low-density lipoprotein (HDL, LDL), systolic blood pressure, glycated hemoglobin (HbA_1c), heart rate, white blood cell count, hemoglobin, estimated glomerular filtration rate, albuminuria, and event history. Different interventions can be evaluated through their impact on the time paths of these risk factors (e.g., HbA_1c) for a population of real or hypothetical individuals, which in turn will alter the probability of clinical events occurring.

The model therefore has a 3-level simulation, predicting L loops comprising lifetime outcomes for each of P patients for each of B sets of bootstrapped model parameters. Each loop comprises a single lifetime disease trajectory, showing when cardiovascular events and death occur for 1 specific individual in the sample. This is estimated by comparing random numbers against the annual probability of events and death. These annual probabilities are predicted by applying risk factor values for patient P to a set of risk equations. Due to the random or Monte Carlo element in their calculation, each loop will return a different value, and so the results of multiple loops are averaged to derive the expected value. Uncertainty concerning the risk equation parameters is dealt with by reestimating L loops for each patient using B sets of risk equation parameters bootstrapped from the UKPDS population.

In the case study, we extrapolated end-of-trial data for each of the AFORRD trial participants and compared the extrapolated outcomes between the 2 groups as randomized.¹⁵ Extrapolating data for individual patients is especially useful for evaluations based on a single study as it makes few assumptions, preserves the correlations between all clinical variables, and takes account of uncertainty around treatment effects and the variability between trial participants at baseline and over the randomized trial period. However, this approach means that any imbalance in baseline characteristics between randomized groups is propagated into the extrapolated estimates. Using this approach, the treatment effect observed in the trial is not parameterized, and uncertainty around treatment effects must be captured statistically based on the variability between patients in each group.

IPD from RCTs can also be extrapolated using UKPDS-OM2 in several alternative ways. First, some researchers have parametrically simulated data for hypothetical patients based on the mean and standard deviation (SD) for each clinical parameter.^29,30 This approach may be the only option when IPD are not available but requires distributional assumptions and makes it difficult to allow for correlations between different patient characteristics (e.g., LDL and blood pressure), particularly between continuous and dichotomous variables. Second, other studies have extrapolated IPD for 1 arm of the trial, with and without the addition of a treatment effect equal to the difference between the 2 arms of the study.³¹ Providing treatment effects are additive, this approach will not be biased by baseline imbalance and may be useful when it is necessary to compare against comparators not included in the trial but means that additional methods are required to allow for uncertainty around treatment effects and account for correlations between the treatment effects for different risk factors. Using these approaches, parameter uncertainty around treatment effects must be considered separately (e.g., using PSA).

Analytical Methods for the Simulation Study

We conducted a simulation study to do the following:

Compare methods for combining sampling and parameter uncertainty with respect to point estimates, SEs, and coverage. We hypothesized that 95% CIs that include only parameter uncertainty will be too narrow and have <95% coverage and that methods combining parameter uncertainty and sampling uncertainty would have wider 95% CIs and 95% coverage.

Evaluate the impact of adjusting for baseline imbalance in baseline risk factors in terms of point estimates, SEs, and coverage. We hypothesized that this would reduce SEs and alter point estimates by removing the bias resulting from baseline imbalance.

Evaluate the impact of varying the number of loops and assess whether the standard practice of running large numbers of inner loops to minimize MCE is appropriate when IPD from an RCT are extrapolated using PLS in a way analogous to multiple imputation.

The protocol is given in Appendix 2. We used UKPDS-OM2 to extrapolate the AFORRD trial for 30 years using 1, 2000, and 1 million loops and estimated point estimates and 95% CIs using 6 methods (Tables 2 and 3).

Table 2

Summary of the Analyses Conducted^a

Analysis	Description	Types of Uncertainty Included in 95% CI
1) Percentiles across bootstraps	95% CIs were calculated as the 2.5th and 97.5th percentiles across the mean difference in life expectancy estimated for the 800 sets of UKPDS-OM2 risk equation parameters. This mirrors the default method for calculating 95% CI within the UKPDS-OM2.²⁸	Parameter uncertainty only (excludes sampling uncertainty and heterogeneity). Does not adjust for baseline imbalance.
2) Analytical formulas for Rubin’s rule	Formulas adapted from Rubin’s rule were used to combine parameter uncertainty and sampling uncertainty in a way that can easily be implemented without specialist statistical software but which cannot adjust for covariates (see Appendix 1).	Parameter uncertainty, sampling uncertainty, and heterogeneity. Does not adjust for baseline imbalance.
3) Summing within- and between-bootstrap variance	The within-bootstrap variance (reflecting sampling uncertainty) and the between-bootstrap variance (reflecting parameter uncertainty) were added together to give an overall estimate of variance.	Parameter uncertainty, sampling uncertainty, and heterogeneity. Does not adjust for baseline imbalance.
4a) Rubin’s rule regression with no covariates	The mim Stata command³⁷ was used to implement Rubin’s rule in a regression framework. This is broadly equivalent to analysis 2 but makes the standard assumptions of linear regression, including assuming homoscedasticity, whereas analysis 2 allowed variances to differ between the atorvastatin and placebo groups. This analysis included only 1 explanatory variable (treatment allocation).	Parameter uncertainty, sampling uncertainty, and heterogeneity. Does not adjust for baseline imbalance.
4b) Rubin’s rule regression adjusting for prerandomization values of all UKPDS-OM2 inputs	Variant on analysis 4a that adjusts for nonwhite ethnicity, gender, age, duration of diabetes, BMI, history of atrial fibrillation, smoking, HDL cholesterol, LDL cholesterol, blood pressure, and HbA_1c^b within a linear regression model implementing Rubin’s rule using the mim command.	Parameter uncertainty and sampling uncertainty. Adjusts for baseline imbalance (thereby excluding most heterogeneity): controls for different baseline risk factors in an attempt to eliminate the bias associated with baseline imbalance and estimate 95% CIs that include parameter uncertainty and sampling uncertainty that is not explained by the observed covariates.
5) SD across bootstraps from UKPDS and trial	Combines parameter uncertainty and sampling uncertainty nonparametrically by bootstrapping from the AFORRD sample 1000 times within each of the 800 UKPDS bootstraps and calculating 95% CI as the 2.5th and 97.5th percentiles across the resulting 800,000 estimates.	Parameter uncertainty, sampling uncertainty and heterogeneity. Does not adjust for baseline imbalance.

AFORRD, Atorvastatin in a Factorial design with Omega-3 fatty acids on cardiovascular Risk Reduction in patients with Type 2 Diabetes study; BMI, body mass index; CI, confidence interval; HbA_1c, glycated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard deviation; UKPDS-OM2, United Kingdom Prospective Diabetes Study Outcomes Model version 2.

See Appendix 1 for full details of the assumptions, methods, and formulas in each analysis.

We controlled for prerandomization values of all risk factors that were considered in the UKPDS-OM2 and varied between AFORRD participants. Inclusion criteria meant that no trial participants had a history of cardiovascular events. We assumed that no patients had albuminuria or peripheral vascular disease and set heart rate, white blood cell count, hemoglobin, and glomerular filtration rate to the mean values observed in the Lipids in Diabetes study² for all patients, since these data were not collected in AFORRD.

Table 3

Summary of the Model Runs Conducted^a

Analyses	1 Million Loops, 800 UKPDS Bootstraps	2000 Loops, 800 UKPDS Bootstraps	1 Loop, 800 UKPDS Bootstraps	Long-Run Average: 20,000 Loops, 5000 UKPDS Bootstraps
Monte Carlo error	Negligible	Very low	Large	Negligible
Number times analysis was replicated	1	1000	1000	1
Analyses conducted	1,^b 2,^b 3,^b 4a,^b 4b,^b 5^b	1, 2, 3, 4a, 4b, 5^b	1, 2, 3, 4a, 4b, 5^b	Regression with/without covariates^c

UKPDS, United Kingdom Prospective Diabetes Study.

(1) Percentiles across bootstraps. (2) Analytical formulas for Rubin’s rule. (3) Summing within-and between-bootstrap variance. (4) Rubin’s rule regression including (a) no covariates and (b) adjusting for baseline values of all UKPDS-OM2 (United Kingdom Prospective Diabetes Study Outcomes Model version 2) parameters. (5) Standard deviation across bootstraps from UKPDS and trial.

Coverage not calculated.

The long-run average was used to calculate coverage by estimating the proportion of the 1000 runs in which the 95% confidence interval contained the long-run average controlling for the same set of baseline variables. The long-run average without adjusting for baseline covariates was used to estimate coverage for those analyses that did not adjust for baseline imbalance (i.e., analyses 1, 2, 3, and 4a). The long-run average adjusting for all baseline UKPDS-OM parameters was used to estimate coverage for analysis 4a.

The long-run average against which other results were compared comprised the mean difference in life expectancy from a model run extrapolating the original AFORRD sample (without bootstrapping) using 20,000 loops and all 5000 sets of bootstrapped model parameters, which was more than sufficient to give stable results with a feasible simulation time. To calculate coverage, we repeated analyses 1000 times and calculated the proportion of 95% CIs that contained the long-run average. The long-run average was adjusted for the same covariates as the analyses against which it was compared. We also calculated the empirical SE as the SD across the 1000 estimates of mean difference in life expectancy.

The analyses were intended to reflect the way that researchers would extrapolate and analyze data from a 16-week RCT. Each run used a different set of 800 bootstraps for UKPDS-OM2 parameters, and each model run using 1 loop or 2000 loops also extrapolated outcomes for a different bootstrap sample of patients drawn with replacement from the AFORRD sample. The runs therefore differed from another with respect to parameter uncertainty (bootstraps of model parameters), sampling uncertainty (bootstrap samples of AFORRD participants), and MCE (loops). All model analyses used 800 bootstraps, which is sufficient to estimate SEs to ±10% accuracy based on the methods of O’Hagan et al.¹⁷ and was found to give stable results (Appendix 3, Figure A1). Each run therefore used 16% of the available 5000 bootstraps. Although methods have been developed to calculate the optimal number of loops and eliminate the systematic overestimation of SEs due to MCE in some PLS models,¹⁷ these cannot be applied easily in this setting since we have a 3-level simulation (with L loops for each of B bootstraps of P patients). We therefore compared SEs between model runs using different numbers of loops and found that SEs differ very little between 1000 loops and 1 million loops; 1000 runs of 2000 loops and 1 run of 1 million loops were therefore chosen to keep the computation time feasible (Table 3).

Results of the model-based analyses were analyzed using 6 methods (Table 2), which are described in more detail in Appendix 1 with code supplied in Appendix 4. Coverage was not calculated for the run of 1 million loops, since a single UKPDS-OM2 run took 4 weeks to simulate, or for analysis 5, which took 2.5 hours to analyze per run. The mean and SE around the difference in life expectancy were averaged across all replications. All analyses were conducted in Stata version 14 (StataCorp LLC, College Station, TX).

Financial support for the AFORRD trial was provided by a grant from Pfizer, although this methodological work received no funding.

Results

Taking percentiles across the bootstraps (analysis 1, the standard UKPDS-OM2 output) for the analysis using 2000 loops suggested that the atorvastatin arm of AFORRD accrued 0.55 (95% CI, 0.42–0.69; P < 0.001) more life years than the placebo arm (Figure 1, Table 4). However, this 95% CI includes only parameter uncertainty around risk equations and ignores sampling uncertainty of patient characteristics and treatment effects within the AFORRD trial sample. The SEs from analysis 1 were only 16% of the empirical SE. Consequently, the coverage of the 95% CI for analysis 1 was 25%.

Figure 1

Results of each analysis with 2000 loops.

Table 4

Results for Each Analysis

	Difference in Life Expectancy between Atorvastatin and No Atorvastatin
Analysis	Mean	SE^a	Lower 95% CI	Upper 95% CI	P Value^b	Coverage, %	Empirical SE^c
2000 loops
1) Percentiles across bootstraps^d	0.54807	0.06921	0.41610	0.68741	<0.0001	24.8	0.42032
2) Analytical formulas for Rubin’s rule^d	0.54807	0.43602	−0.30652	1.40266	0.2092	96.3	0.42032
3) Summing within- and between-bootstrap variance^d	0.54807	0.43602	−0.30651	1.40265	0.2092	96.3	0.42032
4a) Rubin’s rule regression: no covariates^d	0.54807	0.43587	−0.30621	1.40235	0.2090	96.3	0.42032
4b) Rubin’s rule regression: with covariates^d	0.51975	0.10270	0.31846	0.72103	<0.0001	99.8	0.07526
5) SD across bootstraps from UKPDS and trial^e	0.57912	0.43514	−0.27413	1.43314	0.1836	NA	NA
1 loop
1) Percentiles across bootstraps^d	0.57385	0.79592	−0.98772	2.13222	0.4711	99.7	0.43367
2) Analytical formulas for Rubin’s rule^d	0.57385	1.06023	−1.50416	2.65186	0.5885	100.0	0.43367
3) Summing within- and between-bootstrap variance^d	0.57385	1.05986	−1.50343	2.65113	0.5884	100.0	0.43367
4a) Rubin’s rule regression: no covariates^d	0.57385	1.06021	−1.50411	2.65181	0.5885	100.0	0.43367
4b) Rubin’s rule regression: with all covariates^d	0.52090	0.97871	−1.39733	2.43913	0.5947	100.0	0.08323
5) SD across bootstraps from UKPDS and trial^e	0.60098	0.90957	−1.18484	2.35165	0.5090	NA	NA

CI, confidence interval; NA, not applicable; SD, standard deviation; SE, standard error; UKPDS, United Kingdom Prospective Diabetes Study.

Estimated SE, calculated from the outcomes for each run using the methods for that analysis (averaged across all 1000 runs).

Based on a 2-tailed t test calculated in Excel as = T.DIST.2T(mean/SE, 732-2).

Empirical SE calculated as the SD across the means from the 1000 runs.

Average across 1000 runs.

Based on 1 run.

By contrast, the analyses combining parameter and sampling uncertainty without adjusting for covariates (analyses 2, 3, 4a, 5) estimated much wider 95% CIs (mean difference: 0.55; 95% CI, –0.31 to 1.40). These analyses did not find statins to significantly increase life expectancy (P = 0.21) because of uncertainty around UKPDS-OM model parameters and the noise introduced by variability in life expectancy unrelated to statins. Sampling uncertainty between patients in the AFORRD sample (including heterogeneity and uncertainty around treatment effects) accounted for 97.5% of the variance, and only 2.5% of the variance was due to uncertainty around UKPDS-OM2 model parameters. (Formulae used to estimate these percentages are given in the “Analysis 2: Analytical Formulae for Rubin’s Rule” section of Appendix 1.) Differences between SEs calculated in analyses 2, 3, 4a, and 5 were negligible, confirming the equivalence of Rubin’s rule analytically and summing variances (Appendix 1) and suggesting that the parametric assumptions within analyses 2 to 4 have a minimal impact on the results. Across the 1000 runs, there was only a weak correlation between the between-bootstrap variance and the within-bootstrap variance (correlation coefficient: –0.11).

Adjusting for baseline levels of all UKPDS-OM2 input parameters within Rubin’s rule regression (analysis 4b) slightly reduced the mean difference in life expectancy by eliminating the bias introduced by imbalance between groups. Minimizing the impact of heterogeneity by adjusting for baseline covariates also reduced the SE by 77% and suggested that statin significantly increased life expectancy (P < 0.0001). However, the coefficient for statin treatment varied little across the 1000 runs compared with analyses 2 to 4a (empirical SE: 0.075), such that coverage increased to 99.8%, despite the reduction in estimated SE to 0.103.

Further analyses suggested that reducing the number of baseline variables included in the regression gave SEs and coverage that were in between those of analyses 4a and 4b (Appendix 3, Table A5 and Figure A2). Adjusting for age (the risk factor most strongly correlated with life expectancy (Appendix 3, Table A6) gave low SE, high coverage, and a point estimate markedly smaller than with all covariates, while controlling for LDL (the factor that was most imbalanced between treatment arms, P = 0.069) had 96% coverage but a high SE similar to that with no covariates.

Results with 1 million loops were very similar to those with 2000 loops for analyses 2 to 4a (Appendix 3, Table A5), confirming that 2000 loops were sufficient to minimize MCE. However, SEs with 2000 loops were 15% larger than with 1 million loops for analysis 1, 4.9% larger for analysis 4b, but only 0.1% larger for analyses 2 to 4a.

SEs were markedly larger when only 1 loop was run per bootstrap and no analysis observed a statistically significant difference between groups (Table 4). With 1 loop, MCE accounted for 99% of the variance in analyses 1 and 4b and 83% of the variance in analyses 2 to 4a, and coverage was ≥99.7% for all analyses.

Discussion

We built on existing taxonomies of uncertainty^6–8 to elucidate the specific issues that can arise when combining observed IPD from an RCT with posttrial outcomes extrapolated using PLS modeling. It is hoped that subdividing “uncertainty” into distinct categories will help researchers, readers, and reviewers to consider explicitly the different sources of uncertainty within their analyses and how each type of uncertainty should be represented and combined.

Our simulation study demonstrated the importance of including sampling uncertainty as well as parameter uncertainty when calculating SEs around statistics estimated on groups of individual trial participants extrapolated using a PLS, where the uncertainty around the treatment effect is captured in the patient sample. Ignoring sampling uncertainty produces SEs that are too small and have low coverage, particularly in a small/medium-sized trial like AFORRD. Underestimating SEs could lead to incorrect conclusions that differences are statistically significant, misrepresentation of the uncertainty around treatment adoption decisions, and/or underestimation of the value of conducting further research. However, parameter uncertainty would be sufficient when estimating outcomes for a single real or hypothetical individual with known risk factors (since sampling uncertainty is zero in this case, where the specified individual represents the whole population of interest).

We also highlighted the bias introduced by baseline imbalance between randomized groups in the variables that are used to predict outcomes beyond the end of the trial, even when there were no statistically significant differences in baseline risk factors between groups (Appendix 3, Table A4).¹¹ To minimize this bias, it is necessary to adjust for prerandomization characteristics when calculating differences between randomized groups extrapolated using PLS. This changed point estimates in this small/medium-sized trial with a marked imbalance in LDL. Similar biases could arise in any analysis estimating absolute differences in posttreatment outcomes,²² event rates, life expectancy or lifetime costs, or QALYs for cohorts of trial participants (even if results are not extrapolated beyond trial end). By contrast, other endpoints (e.g., costs, relative risks, or change in LDL) may be only weakly correlated with baseline data, such that baseline imbalance introduces noise but little bias. This suggests that researchers should consider adjusting for more prerandomization variables than is currently standard practice in within-trial economic evaluations. It has been suggested that trial analyses should adjust for all variables where the correlation with outcome is >0.5, regardless of imbalance or sample size.²⁶

Controlling for baseline characteristics also substantially reduced SEs by accounting for heterogeneity in baseline UKPDS-OM risk factors that explain much of the variability in life expectancy between patients. The extent to which SEs are reduced by covariate adjustment depends on the strength of the correlation between the covariate and outcome.²⁶ In AFORRD, all covariates have some prognostic value (because they are built into the UKPDS-OM), and the correlation between life expectancy and age is −0.968 (Appendix 3, Table A6); SEs therefore reduce substantially when we adjust for age or all covariates. By contrast, the correlation between baseline LDL and life expectancy is 0.026, and adjusting for LDL therefore has minimal impact on SEs despite the marked imbalance.

However, when we controlled for all UKPDS-OM2 risk factors, coverage was markedly higher than 95%, and SEs were 36% larger than the empirical SE, suggesting that this analysis may be inefficient. We are unclear why this should be the case, although it may be because the UKPDS-OM determines the strong link between risk factors and life expectancy, and all of the determinants of posttrial outcomes are known and accounted for in a way that would not happen in a within-trial analysis. The apparent stability in the means may be an artifact of the simulation, as the UKPDS-OM only has 100 seed values and 5000 sets of UKPDS-OM model parameters; reusing seeds and parameter sets may have reduced variability between runs and increased coverage. Consequently, the empirical SE may not be the “true” SE in this case. Despite the high coverage, it may be appropriate to control for all model risk factors to ensure that analyses can be prespecified in analysis plans and minimize the risk of baseline imbalance affecting the point estimate, even if this means that the 95% CI may be inappropriately wide.

The simulation study confirmed that MCE should be minimized even when we extrapolate IPD in a way that is analogous to multiple imputation: analyses using only 1 loop gave 95% CIs that were too wide and had 100% coverage. The simulation time was 1 hour for 2000 loops and 26 days for 1 million loops (Appendix 3, Table A6), although the difference in SEs was small. There is a need for more research to identify the optimal number of bootstraps and loops in models using a 3-level simulation (e.g., with B bootstraps and L loops for P patients).

There may be fundamental differences between the types of uncertainty affecting trial- and model-based economic evaluation. In particular, the outcomes for a 30-year RCT may be subject to stochastic uncertainty and sampling uncertainty, whereas our model-based analysis 4a included sampling uncertainty and parameter uncertainty and was subject to (but excluded) structural uncertainty. In our experience, trial-based economic evaluations tend to have wider 95% CIs and a lower probability of a treatment being cost-effective than model-based economic evaluations, which may reflect these fundamental differences between trials and models and the fact that stochastic uncertainty within a trial may often be larger than parameter uncertainty within a model. However, that trend could also reflect types of uncertainty that are not fully captured within models at present or differences between the types of evidence informing model-based (meta-analyses or explanatory trials) v. trial-based (pragmatic trials) economic evaluations.

The 4 methods that we developed for combining sampling and parameter uncertainty could be used to analyze clinical and economic endpoints in other settings; Stata code is provided in Appendix 4. If it is not necessary to adjust for covariates, any of the methods could be used and give virtually identical results. Rubin’s rule regression naturally facilitates adjustment for covariates. The bootstrapping approach could also be adapted to adjust for covariates by estimating treatment effects using regression rather than sample means. Although it takes longer to run the bootstrapping analysis, this approach avoids parametric assumptions, facilitates construction of scatter-graphs and cost-effectiveness acceptability curves, and could accommodate more complex analyses (e.g., Kaplan-Meier sample averaging). A separate study will illustrate how the bootstrapping approach can be readily extended to economic evaluations considering both costs and QALYs, allowing for correlations between costs and QALYs. Although our analysis did not adjust for clustering by general practitioner practice to avoid overcomplicating the analysis, this could be accounted for by extending analysis 5 to use a 2-stage bootstrap^32,33 or applying Rubin’s rule to the results of a mixed model.

In our simulation study, we did not consider structural uncertainty or uncertainty around missing data; future work could explore whether the methods identified here can be adapted to incorporate these types of uncertainty (e.g., by summing 3 variances together). Sensitivity analyses would still be required to evaluate generalizability, methodological uncertainty, and aspects of structural uncertainty that cannot easily be parameterized. We extrapolated 1 trial using UKPDS-OM2, although we anticipate that the results would also apply in other settings. Although the trial was small and lasted only 16 weeks, these limitations do not affect the proof of methodological concepts.

Our results suggest that sampling uncertainty and adjustment for covariates should be incorporated into analyses using models such as UKPDS-OM2 to extrapolate data for individual participants in each study arm. Future research should explore methods for combining uncertainty when using simulated patients or adding sample treatment effects to 1 patient cohort.

Supplemental Material

Appendix_1_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_1_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_2_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_2_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_3_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_3_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_4a_-_details_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_4a_-_details_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_4b_-_code_for_readers_to_apply_analyses_1-4f_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_4b_-_code_for_readers_to_apply_analyses_1-4f_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_4c_-_Analysis_5_code_for_paper_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_4c_-_Analysis_5_code_for_paper_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_4d_-_UKPDS_Outcomes_Model_v2.0_Patient_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_4d_-_UKPDS_Outcomes_Model_v2.0_Patient_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_4e_-_Risk_factor_data_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_4e_-_Risk_factor_data_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Supplemental Material

Appendix_4f_-_bootstrap_numbers_online_supp – Supplemental material for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data

Supplemental material, Appendix_4f_-_bootstrap_numbers_online_supp for Accurately Reflecting Uncertainty When Using Patient-Level Simulation Models to Extrapolate Clinical Trial Data by Helen A. Dakin, José Leal, Andrew Briggs, Philip Clarke, Rury R. Holman and Alastair Gray in Medical Decision Making

Footnotes

Acknowledgements

We thank the AFORRD participants and the AFORRD Study Group for their work in designing the trials and collecting and cleaning data. Rosa Legood conducted preliminary work on the costing and analysis for the AFORRD economic evaluation, while Frauke Becker helped run simulations and Olorunsola Agbaje provided feedback on a draft of the manuscript. We also thank Ian Kennedy and Vanessa Gregory for adapting the UKPDS-OM2 to save the outputs required for our regression-based analyses. We also received advice on Rubin’s rule from Ian White and helpful feedback on this project from those attending presentations held at the January 2017 Health Economists’ Study Group, the 2016 Mount Hood Challenge meeting, Oxford Health Economics Research Centre, Sheffield School of Health and Related Research, and University of Bristol. RRH is an emeritus UK National Institute for Health Research Senior Investigator.

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: All authors have been involved in the development of the UKPDS-OM, which is licensed by the University of Oxford.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The AFORRD trial was registered as ISRCTN 76737502. The protocol was approved by local and national ethics committees and regulatory agencies, and the study was carried out in accordance with the Declaration of Helsinki and good clinical practice guidelines.Financial support for the AFORRD trial was provided by a grant from Pfizer. Pfizer provided atorvastatin study medication and matching placebo, and Pronova Biopharma Norge AS provided omega-3 EE90 study medication and matching olive oil capsules. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. This methodological work received no funding, although Rury Holman and Alastair Gray are (or were at the time the research began) National Institute for Health Research (NIHR) senior investigators.

Rury Holman, Philip Clarke, and Alastair Gray are supported by the NIHR Oxford Biomedical Research Centre (BRC). José Leal acknowledges support from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 115881 (RHAPSODY). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

Authors’ Note

Preliminary results of this study were presented at the Health Economists’ Study Group meeting in Birmingham, UK, January 4–6, 2017, and the Mount Hood 2016 Challenge: Economics modelling and diabetes, St Gallen, Switzerland, September 16–18, 2016.

ORCID iDs

Helen A. Dakin

Rury R. Holman

Research Data

The code used to conduct analyses described in this article is available in Appendix 4. UKPDS-OM2 is available at .

Supplemental Material

Supplementary material for this article is available on the Medical Decision Making Web site at .

Notes

References

National Institute for Health and Care Excellence. Guide to the methods of technology appraisal 2013. Available from: https://www.nice.org.uk/process/pmg9/resources/guide-to-the-methods-of-technologyappraisal-2013-pdf-2007975843781 (accessed 19 November 2019).

Hayes

Leal

Gray

Holman

Clarke

PM.

UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925–33.

Clarke

Gray

Briggs

, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004;47(10):1747–59.

Davis

Stevenson

Tappenden

Wailoo

NICE DSU Technical Support Document 15: cost-effectiveness modelling using patient-level simulation. Available from: http://www.nicedsu.org.uk, https://www.ncbi.nlm.nih.gov/books/NBK310370/pdf/Bookshelf_NBK310370.pdf (accessed 29 May 2019).

Groot Koerkamp

Stijnen

Weinstein

Hunink

MG.

The combined analysis of uncertainty and patient heterogeneity in medical decision models. Med Decis Making. 2011;31(4):650–61.

Briggs

Weinstein

Fenwick

, et al. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–6. Value Health. 2012;15(6):835–42.

Briggs

Handling uncertainty in economic evaluation and presenting the results. In: Drummond

McGuire

, eds. Economic Evaluation in Health Care: Merging Theory with Practice. Oxford, UK: Oxford University Press; 2001.

Gray

Clarke

Wolstenholme

Wordsworth

Applied Methods of Cost-Effectiveness Analysis in Health Care. Oxford, UK: Oxford University Press; 2011.

Schafer

JL.

Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15.

10.

Rubin

DB.

Multiple Imputation for Nonresponse in Surveys. New York: John Wiley; 1987.

11.

Holman

Paul

Farmer

Tucker

Stratton

Neil

HA.

Atorvastatin in Factorial with Omega-3 EE90 Risk Reduction in Diabetes (AFORRD): a randomised controlled trial. Diabetologia. 2009;52(1):50–9.

12.

Claxton

Sculpher

McCabe

, et al. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ. 2005;14(4):339–47.

13.

White

Royston

Wood

AM.

Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99.

14.

Faria

Gomes

Epstein

White

IR.

A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials. Pharmacoeconomics. 2014;32(12):1157–70.

15.

Clarke

Gray

Briggs

, et al. Cost-utility analyses of intensive blood glucose and tight blood pressure control in type 2 diabetes (UKPDS 72). Diabetologia. 2005;48(5):868–77.

16.

Briggs

Clark

Wolstenholme

Clarke

Missing . . . presumed at random: cost-analysis of incomplete data. Health Econ. 2003;12(5):377–92.

17.

O’Hagan

Stevenson

Madan

Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA. Health Econ. 2007;16(10):1009–23.

18.

Koehler

Brown

Haneuse

SJ.

On the assessment of Monte Carlo error in simulation-based statistical analyses. Am Stat. 2009;63(2):155–62.

19.

Armitage

Berry

Mathews

JNS.

Comparison of several groups. In: Statistical Methods in Medical Research. 4th ed. Malden, MA: Blackwell Science; 2002. p 208–35.

20.

Cooper

Sutton

Abrams

KR.

Decision analytical economic modelling within a Bayesian framework: application to prophylactic antibiotics use for caesarean section. Stat Methods Med Res. 2002;11(6):491–512.

21.

Manca

Hawkins

Sculpher

MJ.

Estimating mean QALYs in trial-based cost-effectiveness analysis: the importance of controlling for baseline utility. Health Econ. 2005;14(5):487–96.

22.

Vickers

Altman

DG.

Statistics notes: analysing controlled trials with baseline and follow up measurements. BMJ. 2001;323(7321):1123–4.

23.

Altman

DG.

Comparability of randomized groups. J R Stat Soc D Sta. 1985;34(1):125–36.

24.

Nixon

Thompson

SG.

Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations. Health Econ. 2005;14(12):1217–29.

25.

Willan

Briggs

AH.

Statistical Analysis of Cost-Effectiveness Data. Chichester, UK: John Wiley; 2006.

26.

Pocock

Assmann

Enos

Kasten

LE.

Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med. 2002;21(19):2917–30.

27.

Farmer

Oke

Hardeman

, et al. The effect of a brief action planning intervention on adherence to double-blind study medication, compared to a standard trial protocol, in the Atorvastatin in Factorial with Omega EE90 Risk Reduction in Diabetes (AFORRD) clinical trial: a cluster randomised sub-study. Diabetes Res Clin Pract. 2016;120:56–64.

28.

University of Oxford Diabetes Trials Unit (DTU) and Health Economics Research Centre (HERC). UKPDS outcomes model user manual: version 2.0. Available from: https://www.dtu.ox.ac.uk/outcomesmodel/OM2Manual.pdf. (accessed 16 November 2016).

29.

Palmer

, Mount Hood 5 Modeling Group, Clarke

, et al. Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting. Value Health. 2013;16(4):670–85.

30.

Mount Hood 4 Modeling Group. Computer modeling of diabetes and its complications: a report on the Fourth Mount Hood Challenge Meeting. Diabetes Care. 2007;30(6):1638–46.

31.

McKinsey and Company. Evaluation report of the Diabetes Care Project. Available from: https://www1.health.gov.au/internet/main/publishing.nsf/Content/eval-rep-dcp. (accessed 20 November 2019).

32.

Gomes

Grieve

Nixon

Carpenter

Thompson

SG.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials. Med Decis Making. 2012;32(2):350–61.

33.

Gomes

Grieve

Nixon

Carpenter

Thompson

SG.

Methods for covariate adjustment in cost-effectiveness analysis that use cluster randomised trials. Health Econ. 2012;21(9):1101–18.

34.

Afzali

Karnon

Exploring structural uncertainty in model-based economic evaluations. Pharmacoeconomics. 2015;33(5):435–43.

35.

Eddy

Hollingworth

Caro

, et al. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making. 2012;32(5):733–43.

36.

Drummond

Barbieri

Cook

, et al. Transferability of economic evaluations across jurisdictions: ISPOR Good Research Practices Task Force report. Value Health. 2009;12(4):409–18.

37.

Royston

Carlin

White

IR.

Multiple imputation of missing values: new features for mim. Stata J. 2009;9(2):252–64.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.30 MB

0.05 MB

0.22 MB

0.03 MB

0.01 MB

0.00 MB

1.31 MB

0.02 MB

0.01 MB