scholarly journals Impact of informative censoring on the treatment effect estimate of disability worsening in multiple sclerosis clinical trials

2020 ◽  
Vol 39 ◽  
pp. 101865
Author(s):  
Katherine Riester ◽  
Ludwig Kappos ◽  
Krzysztof Selmaj ◽  
Stacy Lindborg ◽  
Ilya Lipkovich ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Treatment Effect ◽  
Informative Censoring ◽  
Effect Estimate ◽  
Treatment Effect Estimate

scholarly journals Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Nicole Bohme Carnegie ◽  
Rui Wang ◽  
Victor De Gruttola
Keyword(s):  
Infectious Disease ◽  
Disease Prevention ◽  
Social Interactions ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Effect Estimate ◽  
Treatment Effect Estimate ◽  
Partial Interference ◽  
The Difference ◽  
Cluster Randomized

AbstractAn issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of “partial interference” – interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the no-interference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none – referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference – contacts sufficient for transmission that are with treated clusters – is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

Underestimation of treatment effects in sequentially monitored clinical trials that did not stop early for benefit

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3027-3041 ◽  
Author(s):  
Ian C Marschner ◽  
I Manjula Schou
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Treatment Effect ◽  
Interim Analysis ◽  
Experimental Treatment ◽  
Final Analysis ◽  
Effect Estimate ◽  
Effectiveness Analysis ◽  
Simulation Results ◽  
Meta Analyses

In recent years, there has been a prominent discussion in the literature about the potential for overestimation of the treatment effect when a clinical trial stops at an interim analysis due to the experimental treatment showing a benefit over the control. However, there has been much less attention paid to the converse issue, namely, that sequentially monitored clinical trials which did not stop early for benefit tend to underestimate the treatment effect. In meta-analyses of many studies, these two sources of bias will tend to balance each other to produce an unbiased estimate of the treatment effect. However, for the interpretation of a single study in isolation, underestimation due to interim analysis may be an important consideration. In this paper, we discuss the nature of this underestimation, including theoretical and simulation results demonstrating that it can be substantial in some contexts. Furthermore, we show how a conditional approach to estimation, in which we condition on the study reaching its final analysis, may be used to validly inflate the observed treatment difference from a sequentially monitored clinical trial. Expressions for the conditional bias and information are derived, and these are used in supplied R code that computes the bias-adjusted estimate and an associated confidence interval. As well as simulation results demonstrating the validity of the methods, we present a data analysis example from a pivotal clinical trial in cardiovascular disease. The methods will be most useful when an unbiased treatment effect estimate is critical, such as in cost-effectiveness analysis or risk prediction.

The Mechanics of Treatment-effect Estimate Bias for Nonexperimental Data

2019 ◽  
pp. 004912411985237
Author(s):  
Roberto V. Penaloza ◽  
Mark Berends
Keyword(s):  
Treatment Effect ◽  
Latent Variable ◽  
Value Added ◽  
Effect Estimate ◽  
Education Production Function ◽  
Education Production ◽  
Group Assignment ◽  
Treatment Effect Estimate ◽  
Flexible Form ◽  
Variable Errors

To measure “treatment” effects, social science researchers typically rely on nonexperimental data. In education, school and teacher effects on students are often measured through value-added models (VAMs) that are not fully understood. We propose a framework that relates to the education production function in its most flexible form and connects with the basic VAMs without using untenable assumptions. We illustrate how, due to measurement error (ME), cross-group imbalances created by nonrandom group assignment cause correlations that drive the models’ treatment-effect estimate bias. We derive formulas to calculate bias and rank the models and show that no model is better in all situations. The framework and formulas’ workings are verified and illustrated via simulation. We also evaluate the performance of latent variable/errors-in-variables models that handle ME and study the role of extra covariates including lags of the outcome.

Estimating a treatment effect: Choosing between relative and absolute measures

2016 ◽  
Vol 23 (2) ◽  
pp. 197-200 ◽  
Author(s):  
Maria Pia Sormani ◽  
Paolo Bruzzi
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Treatment Effect ◽  
Numerical Examples ◽  
Relative Risks ◽  
Relative Treatment Effect ◽  
Hazard Ratios ◽  
Numbers Needed To Treat ◽  
Indirect Comparisons ◽  
Independent Trials

The size of a treatment effect in clinical trials can be expressed in relative or absolute terms. Commonly used relative treatment effect measures are relative risks, odds ratios, and hazard ratios, while absolute estimate of treatment effect are absolute differences and numbers needed to treat. When making indirect comparisons of treatment effects, which is common in multiple sclerosis (MS), having now many drugs tested in independent trials, we can have different figures if we use relative or absolute measures, and a frequently asked question by clinicians is which approach should be used. In this report, we will try to define these measures, to give numerical examples of their calculation and specify their meaning and their context of use.

scholarly journals Estimating the contribution of studies in network meta-analysis: paths, flows and streams

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 610
Author(s):  
Theodoros Papakonstantinou ◽  
Adriani Nikolakopoulou ◽  
Gerta Rücker ◽  
Anna Chaimani ◽  
Guido Schwarzer ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Direct Evidence ◽  
Meta Analysis ◽  
Effect Estimate ◽  
Analysis Model ◽  
Direct Treatment ◽  
Analysis Methodology ◽  
Treatment Effect Estimate ◽  
Quinolone Antibiotics ◽  
Hat Matrix

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to network treatment effects.

scholarly journals Estimation after blinded sample size reassessment

2016 ◽  
Vol 27 (6) ◽  
pp. 1830-1846 ◽  
Author(s):  
Martin Posch ◽  
Florian Klinglmueller ◽  
Franz König ◽  
Frank Miller
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Nuisance Parameters ◽  
Effect Estimate ◽  
Worst Case ◽  
Point Estimates ◽  
Treatment Effect Estimate ◽  
Parallel Group ◽  
The Mean ◽  
Superiority Trials

Blinded sample size reassessment is a popular means to control the power in clinical trials if no reliable information on nuisance parameters is available in the planning phase. We investigate how sample size reassessment based on blinded interim data affects the properties of point estimates and confidence intervals for parallel group superiority trials comparing the means of a normal endpoint. We evaluate the properties of two standard reassessment rules that are based on the sample size formula of the z-test, derive the worst case reassessment rule that maximizes the absolute mean bias and obtain an upper bound for the mean bias of the treatment effect estimate.

Composite end points to assess delay of disability progression by MS treatments

2014 ◽  
Vol 20 (11) ◽  
pp. 1494-1501 ◽  
Author(s):  
J Zhang ◽  
E Waubant ◽  
G Cutter ◽  
JS Wolinsky ◽  
D Leppert
Keyword(s):  
Treatment Effect ◽  
Effect Estimate ◽  
Walk Test ◽  
Disability Progression ◽  
End Points ◽  
Similar Treatment ◽  
Treatment Effect Estimate ◽  
Disability Status ◽  
Low Sensitivity ◽  
Estimate Versus

Background: The Expanded Disability Status Scale (EDSS) has low sensitivity and reliability for detecting sustained disability progression (SDP) in multiple sclerosis (MS) trials. Objective: This study evaluated composite disability end points as alternatives to EDSS alone. Methods: SDP rates were determined using 96-week data from the Olympus trial (rituximab in patients with primary progressive MS). SDP was analyzed using composite disability end points: SDP in EDSS, timed 25-foot walk test (T25FWT), or 9-hole peg test (9HPT) (composite A); SDP in T25FWT or 9HPT (composite B); SDP in EDSS and (T25FWT or 9HPT) (composite C); and SDP in any two (EDSS, T25FWT, and 9HPT) (composite D). Results: Overall agreements between EDSS and other disability measures in defining SDP were 66%−73%. Composite A showed similar treatment effect estimate versus EDSS alone with much higher SDP rates. Composite B, C, and D all showed larger treatment effect estimate with different or similar SDP rates versus EDSS alone. Using composite A (24-week confirmation only), B, C, or D could reduce sample sizes needed for MS trials. Conclusion: Composite end points including multiple accepted disability measures could be superior to EDSS alone in analyzing disability progression and should be considered in future MS trials.

scholarly journals Using predicted length of stay to define treatment and model costs in hospitalized adults with serious illness: an evaluation of palliative care

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter May ◽  
Charles Normand ◽  
Danielle Noreika ◽  
Nevena Skoro ◽  
J. Brian Cassel
Keyword(s):  
Palliative Care ◽  
Treatment Effect ◽  
Predictive Accuracy ◽  
Model Performance ◽  
The United States ◽  
Information Criteria ◽  
Economic Research ◽  
Effect Estimate ◽  
Treatment Variable ◽  
Treatment Effect Estimate

Abstract Background Economic research on hospital palliative care faces major challenges. Observational studies using routine data encounter difficulties because treatment timing is not under investigator control and unobserved patient complexity is endemic. An individual’s predicted LOS at admission offers potential advantages in this context. Methods We conducted a retrospective cohort study on adults admitted to a large cancer center in the United States between 2009 and 2015. We defined a derivation sample to estimate predicted LOS using baseline factors (N = 16,425) and an analytic sample for our primary analyses (N = 2674) based on diagnosis of a terminal illness and high risk of hospital mortality. We modelled our treatment variable according to the timing of first palliative care interaction as a function of predicted LOS, and we employed predicted LOS as an additional covariate in regression as a proxy for complexity alongside diagnosis and comorbidity index. We evaluated models based on predictive accuracy in and out of sample, on Akaike and Bayesian Information Criteria, and precision of treatment effect estimate. Results Our approach using an additional covariate yielded major improvement in model accuracy: R2 increased from 0.14 to 0.23, and model performance also improved on predictive accuracy and information criteria. Treatment effect estimates and conclusions were unaffected. Our approach with respect to treatment variable yielded no substantial improvements in model performance, but post hoc analyses show an association between treatment effect estimate and estimated LOS at baseline. Conclusion Allocation of scarce palliative care capacity and value-based reimbursement models should take into consideration when and for whom the intervention has the largest impact on treatment choices. An individual’s predicted LOS at baseline is useful in this context for accurately predicting costs, and potentially has further benefits in modelling treatment effects.

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