Treatment Effect, Duration Outcomes and Parameters Identification

COST-EFFECTIVENESS OF STRUCTURED EDUCATION IN CHILDREN WITH TYPE-1 DIABETES MELLITUS

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462316000507 ◽

2016 ◽

Vol 32 (4) ◽

pp. 203-211 ◽

Cited By ~ 1

Author(s):

Hasan Basarir ◽

Alan Brennan ◽

Richard Jacques ◽

Daniel Pollard ◽

Katherine Stevens ◽

...

Keyword(s):

Type 1 Diabetes ◽

Cost Effectiveness ◽

Usual Care ◽

Treatment Effect ◽

Severe Hypoglycemia ◽

Structured Education ◽

Effect Duration ◽

The Cost

Objectives: Kids in Control OF Food (KICk-OFF) is a 5-day structured education program for 11- to 16-year-olds with type 1 diabetes mellitus (T1DM) who are using multiple daily insulin injections. This study evaluates the cost-effectiveness of the KICk-OFF education program compared with the usual care using data from the KICk-OFF trial.Methods: The short-term within-trial analysis covers the 2-year postintervention period. Data on glycated hemoglobin (HbA1c), severe hypoglycemia, and diabetic ketoacidosis (DKA) were collected over a 2-year follow-up period. Sub-group analyses have been defined on the basis of baseline HbA1c being below 7.5 percent (58.5 mmol/mol) (low group), between 7.5 percent and 9.5 percent (80.3 mmol/mol) (medium group), and over 9.5 percent (high group). The long-term cost-effectiveness evaluation has been conducted by using The Sheffield Type 1 Diabetes Policy Model, which is a patient-level simulation model on T1DM. It includes long-term microvascular (retinopathy, neuropathy, and nephropathy) and macrovascular (myocardial infarction, stroke, revascularization, and angina) diabetes-related complications and acute adverse events (severe hypoglycemia and DKA).Results: The most favorable within-trial scenario for the KICk-OFF arm led to an incremental cost-effectiveness ratio (ICER) of £23,688 (base year 2009) with a cost-effectiveness probability of 41.3 percent. Simulating the long-term complications using the full cohort data, the mean ICER for the base case was £28,813 (base year 2011) and the probability of the KICk-OFF intervention being cost-effective at £20,000/QALY threshold was 42.6 percent, with considerable variation due to treatment effect duration. For the high HbA1c sub-group, the KICk-OFF arm was “dominant” (meaning it provided better health gains at lower costs than usual care) over the usual care arm in each scenario considered.Conclusions: For the whole study population, the cost-effectiveness of KICk-OFF depends on the assumption for treatment effect duration. For the high baseline HbA1c sub-group, KICk-OFF arm was estimated to be dominant over the usual care arm regardless of the assumption on the treatment effect duration.

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The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽

10.1027/1614-2241/a000121 ◽

2017 ◽

Vol 13 (2) ◽

pp. 41-60

Author(s):

Shahab Jolani ◽

Maryam Safarkhani

Keyword(s):

Randomized Controlled Trials ◽

Discrete Time ◽

Treatment Effect ◽

Survival Data ◽

Controlled Trials ◽

Missing Covariates ◽

Indicator Method ◽

Imputation Methods ◽

Randomized Controlled ◽

Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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