Efficient, Doubly Robust Estimation of the Effect of Dose Switching for Switchers in a Randomised Clinical Trial

Motivated by a clinical trial conducted by Janssen Pharmaceuticals in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose is unsatisfactory because the latter patients are usually in a better health condition. Because the available information in the considered trial is too scarce to enable a reliable adjustment, we will instead transport data from a fixed dosing trial that has been conducted concurrently on the same target, albeit not in an identical patient population. In particular, we will propose an estimator which relies on an outcome model and a propensity score model for the association between study and patient characteristics. The proposed estimator is asymptotically unbiased if at least one of both models is correctly specified, and efficient (under the model defined by the restrictions on the propensity score) when both models are correctly specified. We show that the proposed method for using results from an external study is generically applicable in studies where a classical confounding adjustment is not possible due to positivity violation (e.g., studies where switching takes place in a deterministic manner). Monte Carlo simulations and application to the motivating study demonstrate adequate performance

A novel estimand to adjust for rescue treatment in clinical trials

The interpretation of randomised clinical trial results is often complicated by intercurrent events. For instance, rescue medication is sometimes given to patients in response to worsening of their disease, either in addition to the randomised treatment or in its place. The use of such medication complicates the interpretation of the intention-to-treat analysis. In view of this, we propose a novel estimand defined as the intention-to-treat effect that would have been observed, had patients on the active arm been switched to rescue medication if and only if they would have been switched when randomised to control. This enables us to disentangle the treatment effect from the effect of rescue medication on a patient's outcome, while avoiding the strong extrapolations that are typically needed when inferring what the intention-to-treat effect would have been in the absence of rescue medication. We develop an inverse probability weighting method to estimate this estimand under specific untestable assumptions, in view of which we propose a sensitivity analysis. We use the method for the analysis of a clinical trial conducted by Janssen Pharmaceuticals, in which chronically ill patients can switch to rescue medication for ethical reasons. Monte Carlo simulations confirm that the proposed estimator is unbiased in moderate sample sizes

Evaluating futility of a binary clinical endpoint using early read-outs.

Interim analyses are routinely used to monitor accumulating data in clinical trials. When the objective of the interim analysis is to stop the trial if the trial is deemed futile, it must ideally be conducted as early as possible. In trials where the clinical endpoint of interest is only observed after a long follow-up, many enrolled patients may therefore have no information on the primary endpoint available at the time of the interim analysis. To facilitate earlier decision-making, one may incorporate early response data that are predictive for the primary endpoint (eg, an assessment of the primary endpoint at an earlier time) in the interim analysis. Most attention so far has been given to the development of interim test statistics that include such short-term endpoints, but not to decision procedures. Existing tests moreover perform poorly when the information is scarce, eg, due to rare events, when the cohort of patients with observed primary endpoint data is small, or when the short-term endpoint is a strong but imperfect predictor. In view of this, we develop an interim decision procedure based on the conditional power approach that utilizes the short-term and long-term binary endpoints in a framework that is expected to provide reliable inferences, even when the primary endpoint is only available for a few patients, and has the added advantage that it allows the use of historical information. The operational characteristics of the proposed procedure are evaluated for the phase III clinical trial that motivated this approach, using simulation studies

Методичні вказівки для проведення практичних занять і організації самостійної роботи з навчальної дисципліни «Управління нерухомим майном» (для студентів 4 курсу денної і заочної форм навчання напряму підготовки 6.080101 – Геодезія, картографія та землеустрій).

Table S2. Week-4 simeprevir pharmacokinetic parameters after administration in (a) Panels 1–3 and (b) Panel 4. (DOCX 15 kb

Estimation and interpretation of vaccine efficacy in COVID-19 randomized clinical trials

Abstract Objectives An exceptional effort by the scientific community has led to the development of multiple vaccines against COVID-19. Efficacy estimates for these vaccines have been widely communicated to the general public, but are nonetheless challenging to compare because they are based on phase 3 trials that differ in study design, definition of vaccine efficacy and the handling of cases arising shortly after vaccination. We investigate the impact of these choices on vaccine efficacy estimates, both theoretically and by re-analyzing the Janssen and Pfizer COVID-19 trial data under a uniform protocol. We moreover study the causal interpretation that can be assigned to per-protocol analyses typically performed in vaccine trials. Finally, we propose alternative estimands to measure the intrinsic vaccine efficacy in settings with delayed immune response. Methods The data of the Janssen COVID-19 trials were recreated, based on the published Kaplan-Meier curves. An estimator for the alternative causal estimand was developed using a Structural Distribution Model. Results In the data analyses, we observed rather large differences between intention-to-treat and per-protocol effect estimates. In contrast, the causal estimand and the different estimators used for per-protocol effects lead approximately to the same estimates. Conclusions In these COVID-10 vaccine trials, per-protocol effects can be interpreted as the number of cases that can be avoided by vaccination, if the vaccine would immediately induce an immune response. However, it is unclear whether this interpretation also holds in other settings. Objectives An exceptional effort by the scientific community has led to the development of multiple vaccines against COVID-19. Efficacy estimates for these vaccines have been widely communicated to the general public, but are nonetheless challenging to compare because they are based on phase 3 trials that differ in study design, definition of vaccine efficacy and the handling of cases arising shortly after vaccination. We investigate the impact of these choices on vaccine efficacy estimates, both theoretically and by re-analyzing the Janssen and Pfizer COVID-19 trial data under a uniform protocol. We moreover study the causal interpretation that can be assigned to per-protocol analyses typically performed in vaccine trials. Finally, we propose alternative estimands to measure the intrinsic vaccine efficacy in settings with delayed immune response. Methods The data of the Janssen COVID-19 trials were recreated, based on the published Kaplan-Meier curves. An estimator for the alternative causal estimand was developed using a Structural Distribution Model. Results In the data analyses, we observed rather large differences between intention-to-treat and per-protocol effect estimates. In contrast, the causal estimand and the different estimators used for per-protocol effects lead approximately to the same estimates. Conclusions In these COVID-10 vaccine trials, per-protocol effects can be interpreted as the number of cases that can be avoided by vaccination, if the vaccine would immediately induce an immune response. However, it is unclear whether this interpretation also holds in other setting