br Material and methods br Results br Discussion We report
Material and methods
Discussion We report on fieldwork experience in the pilot phase of an esophageal squamous cell carcinoma case-control study in Western Kenya. The study proceeded to the main study which has now recruited over 400 cases and 400 controls (cumulative) and its first results are emerging . The pilot study called for protocol changes and considerations specific to this setting, which are discussed below and from which we make recommendations for fieldwork (R1-R9) in Box 1.
Funding This work was supported by the International Agency for Research on Cancer (IARC) and NIH/NCI (grant number R21CA191965).
Declarations of interest
Introduction Healthcare expenditures have increased importantly over the last decades, especially in oncology due to expensive novel targeted agents and personalized treatments based on molecular markers in order to provide patients with the best possible care [1,2]. Cost-effectiveness analysis of such novel medical technologies is becoming increasingly relevant, as it Okadaic acid may inform treatment, resource allocation, and research prioritization decisions. This is illustrated by the standardized approaches to value cancer treatment options in terms of efficacy and costs for clinicians [3,4] and guidance for performing cost-effectiveness analysis alongside clinical trials . High quality individual patient data (IPD) on health outcomes, resource use, and care procedures, e.g. obtained from randomized controlled trials (RCTs), are the preferred source of evidence for cost-effectiveness analysis. However, single individual patient datasets do not always provide all (or the only) evidence required for estimating the (long-term) cost-effectiveness of medical technologies [6,7], indicating the need for cost-effectiveness models to synthesize evidence from additional sources or to extrapolate beyond the time horizon of e.g. RCTs [5,8]. Such cost-effectiveness models should adequately represent clinical practice and, therefore, reflect the true nature of the evidence used to define them, including evidence obtained from RCTs and other sources of IPD. In other words, the model should match the evidence. The primary outcome of many clinical oncology studies is the time until an event of interest occurs, e.g. the patients’ overall survival or progression-free survival from the moment of randomization, which are typically recorded continuously over time. However, the most frequently applied cost-effectiveness modeling method, i.e. discrete-time state-transition modeling (DT-STM) , uses transition probabilities over discrete time cycles with a fixed length to represent the progression of time. For example, in an DT-STM with time cycles of three weeks patients can only progress to another health state after this predefined and rigid time length, even though in daily practice patients may progress at any time instead of only at a multiple of three weeks. The length of these time cycles needs to be chosen so that the complex dynamics of clinical practice are appropriately represented . For DT-STM to represent clinical practice better, shorter cycle lengths would be preferable . Although half-cycle corrections may be applied to avoid bias and to better approximate clinical practice , this still insufficiently allows complex clinical dynamics if the cycle length is too long . Using shorter cycles lengths can be disadvantageous, mainly because of increase in number of cycles that needs to be simulated. Besides increasing the computational burden of the simulation [9,12], the larger number of cycles makes it more challenging to represent the uncertainty in the transition probabilities, as the uncertainty in the numerous cycle-specific probabilities needs to be reflected while also maintaining the correlation between them. Furthermore, because the expected number of observations within a cycle decreases with decreasing cycle length, the likelihood of substantial irregularities in transition probabilities between successive cycles is expected to increase. These irregularities are likely to impact the simulation outcomes and do not correspond to clinical practice, as the probability of an event is commonly expected to be similar between successive moments, i.e. the transition-curves follow a smooth pattern over time.