Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

2021 ◽  
pp. 096228022110558
Author(s):  
Steven D Lauzon ◽  
Wenle Zhao ◽  
Paul J Nietert ◽  
Jody D Ciolino ◽  
Michael D Hill ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Logistic Regression ◽  
Selection Bias ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Simulation Studies ◽  
Binary Outcome ◽  
Treatment Allocation ◽  
Analysis Phase ◽  
The Impact

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

scholarly journals Estimating and reporting treatment effects in clinical trials for weight management: using estimands to interpret effects of intercurrent events and missing data

2021 ◽  
Author(s):  
Sean Wharton ◽  
Arne Astrup ◽  
Lars Endahl ◽  
Michael E. J. Lean ◽  
Altynai Satylganova ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Weight Management ◽  
Treatment Effect ◽  
Repeated Measures ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Weight Losses ◽  
The Difference ◽  
Treatment Of Obesity

AbstractIn the approval process for new weight management therapies, regulators typically require estimates of effect size. Usually, as with other drug evaluations, the placebo-adjusted treatment effect (i.e., the difference between weight losses with pharmacotherapy and placebo, when given as an adjunct to lifestyle intervention) is provided from data in randomized clinical trials (RCTs). At first glance, this may seem appropriate and straightforward. However, weight loss is not a simple direct drug effect, but is also mediated by other factors such as changes in diet and physical activity. Interpreting observed differences between treatment arms in weight management RCTs can be challenging; intercurrent events that occur after treatment initiation may affect the interpretation of results at the end of treatment. Utilizing estimands helps to address these uncertainties and improve transparency in clinical trial reporting by better matching the treatment-effect estimates to the scientific and/or clinical questions of interest. Estimands aim to provide an indication of trial outcomes that might be expected in the same patients under different conditions. This article reviews how intercurrent events during weight management trials can influence placebo-adjusted treatment effects, depending on how they are accounted for and how missing data are handled. The most appropriate method for statistical analysis is also discussed, including assessment of the last observation carried forward approach, and more recent methods, such as multiple imputation and mixed models for repeated measures. The use of each of these approaches, and that of estimands, is discussed in the context of the SCALE phase 3a and 3b RCTs evaluating the effect of liraglutide 3.0 mg for the treatment of obesity.

Bounding Treatment Effects with Contaminated and Censored Data: Assessing the Impact of Early Childbearing on Children

2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Charles H Mullin
Keyword(s):  
Censored Data ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Child Outcomes ◽  
Well Being ◽  
Teenage Mothers ◽  
Age At First Birth ◽  
First Birth ◽  
The Impact

AbstractEmpirical researchers commonly invoke instrumental variable (IV) assumptions to identify treatment effects. This paper considers what can be learned under two specific violations of those assumptions: contaminated and corrupted data. Either of these violations prevents point identification, but sharp bounds of the treatment effect remain feasible. In an applied example, random miscarriages are an IV for women’s age at first birth. However, the inability to separate random miscarriages from behaviorally induced miscarriages (those caused by smoking and drinking) results in a contaminated sample. Furthermore, censored child outcomes produce a corrupted sample. Despite these limitations, the bounds demonstrate that delaying the age at first birth for the current population of non-black teenage mothers reduces their first-born child’s well-being.

scholarly journals The impact of loss to follow-up on hypothesis tests of the treatment effect for several statistical methods in substance abuse clinical trials

2009 ◽  
Vol 37 (1) ◽  
pp. 54-63 ◽  
Author(s):  
Sarra L. Hedden ◽  
Robert F. Woolson ◽  
Rickey E. Carter ◽  
Yuko Palesch ◽  
Himanshu P. Upadhyaya ◽  
...  
Keyword(s):  
Substance Abuse ◽  
Clinical Trials ◽  
Statistical Methods ◽  
Treatment Effect ◽  
Hypothesis Tests ◽  
Loss To Follow Up ◽  
The Impact

scholarly journals Impact of a non-constant baseline hazard on detection of time-dependent treatment effects: a simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kim Jachno ◽  
Stephane Heritier ◽  
Rory Wolfe
Keyword(s):  
Clinical Trials ◽  
Simulation Study ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Proportional Hazards ◽  
Time Dependent ◽  
Time To Event ◽  
Baseline Hazard ◽  
Event Rates ◽  
Analytical Approaches

Abstract Background Non-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard. Methods A simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes. Results In the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels. Conclusions Our results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

scholarly journals Balancing Treatment Allocation over Continuous Covariates: A New Imbalance Measure for Minimization

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Yanqing Hu ◽  
Feifang Hu
Keyword(s):  
Clinical Trials ◽  
Efficient Algorithm ◽  
Allocation Rule ◽  
Simulation Studies ◽  
Treatment Allocation ◽  
Good Balance ◽  
Loss Of Information ◽  
Continuous Covariates ◽  
Continuous Covariate ◽  
Balance Treatment

In many clinical trials, it is important to balance treatment allocation over covariates. Although a great many papers have been published on balancing over discrete covariates, the procedures for continuous covariates have been less well studied. Traditionally, a continuous covariate usually needs to be transformed to a discrete one by splitting its range into several categories. Such practice may lead to loss of information and is susceptible to misspecification of covariate distribution. The more recent papers seek to define an imbalance measure that preserves the nature of continuous covariates and set the allocation rule in order to minimize that measure. We propose a new design, which defines the imbalance measure by the maximum assignment difference when all possible divisions of the covariate range are considered. This measure depends only on ranks of the covariate values and is therefore free of covariate distribution. In addition, we developed an efficient algorithm to implement the new procedure. By simulation studies we show that the new procedure is able to keep good balance properties in comparison with other popular designs.

Restricted mean survival time analysis in heart failure clinical trials

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C Perego ◽  
M Sbolli ◽  
C Specchia ◽  
C Oriecuia ◽  
G Peveri ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Trials ◽  
Survival Time ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Statistical Testing ◽  
Secondary Outcome ◽  
Time To Event ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Abstract Background The hazard ratio (HR) is the most common measure used to quantify treatment effects in heart failure (HF) clinical trials. However, the HR is only valid when the proportional hazards assumption is plausible, and the HR may be difficult to interpret for clinicians and laypeople. Restricted mean survival time (RMST), defined as the average time-to-event before a specific timepoint, is an intuitive summary of group-wise survival. The difference between two RMSTs measures treatment effects without model assumptions and may communicate more clinically interpretable results. Purpose To evaluate statistical and clinical properties of RMST-based statistics applied to clinical trial data for treatments of HF with reduced ejection fraction. Methods Patient time-to-event data was reconstructed from the published primary and secondary outcome Kaplan-Meier curves from landmark HF clinical trials. We estimated the RMST-differences between treatment groups as a measure of treatment effect with published data, and compared statistical testing results and effect size values to HR analysis results. Results We analyzed 7 HF clinical trials, including data from a total of 27,845 patients (Table 1). RMST should be interpreted as the average number of months that the outcome is avoided over the study period. As examples: On average, treatment with enalapril for 12 months extended each patient's life by 2.2 months compared to placebo, and treatment with spironolactone for 34 months extended each patient's life by 2.2 months compared to placebo. Conclusions RMST-difference test statistic has identical statistical conclusions as HRs but provided an intuitive estimate of each treatment effect. RMST-based data can potentially be used to better communicate treatment effects to patients, to assist in patient-preference discussions and shared decision-making Funding Acknowledgement Type of funding source: None

Age Significantly Influences Presentation and Patterns of Care in Patients with Newly Diagnosed Diffuse Large Cell Lymphoma (DLCL): Collective Data from 5 Centers Participating in the National Comprehensive Center Network (NCCN) NHL Outcomes Project.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1335-1335
Author(s):  
Maria Alma Rodriguez ◽  
Myron S. Czuczman ◽  
Jonathan W. Friedberg ◽  
Michelle E. Kho ◽  
Ann S. LaCasce ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Logistic Regression ◽  
Older Patients ◽  
International Prognostic Index ◽  
Newly Diagnosed ◽  
First Line ◽  
Independent Variables ◽  
Therapeutic Decisions ◽  
The Impact ◽  
To Receive

Abstract Background: Older patients with DLCL are underrepresented in clinical trials, and little is known about the impact of age on therapeutic decisions outside the clinical trial setting. Treatment for older individuals can be influenced by comorbidities, possibly limiting the use of proven aggressive therapies. We investigated the effects of age on patterns of presentation and treatment for patients with DLCL. Methods: Data from the 5 centers participating in the NCCN NHL Outcomes Project were used for analysis. Patients with newly diagnosed DLCL, receiving some or all of their care at the NCCN institutions, with first presention between 7/01/2000 to 6/30/2004 were included. Independent variables evaluated were stage, International Prognostic Index (IPI), Charlson comorbidity score, NCCN center, and age (≤ 60 and >60). Components of IPI were evaluated as separate independent variables. Fisher’s exact tests compared baseline characteristics between age groups. Univariate and multivariate logistic regression models were developed to identify factors associated with the receipt of anthracycline-based first-line therapy. Results: Of 417 patients presenting with newly diagnosed DLCL, 376 were eligible for analysis. Older patients (>60 years) accounted for 38% (142/376) of our cohort. The median age was 55.6 years; among older patients, over half were over 70. Older patients were significantly more likely to have HI/H IPI scores (52% vs. 27%, p=0.0001) and more than 2 major comorbidities (29% vs. 8%, p=0.0001). Overall, 18% (66/376) participated in first-line clinical trials, with no difference in participation by age (p=0.49). Among the 310 patients treated off protocol, 93% (289) received an anthracycline-containing first-line regimen. Older patients were less likely to receive anthracyclines [87% (105/120) vs. 97% (184/190)]. Among patients receiving anthracyclines, older patients were more likely to receive growth factor support [78% (82/105) vs.54% (100/184)]. In multivariable logistic regression adjusting for stage, comorbidity, and center, age was the only factor statistically significantly associated with treatment choice, with patients over 60 less likely to receive an anthracycline-based first-line regimen (OR=0.29; 95% CI=0.11 to 0.80 p=0.02). Conclusions: In a large cohort of patients with newly diagnosed DLCL, we found that older patients (>60 years) with DLCL had both higher risk disease, and more comorbidity at diagnosis. In our centers, there was no difference in clinical trail participation between older and younger patients; however, older patients were less likely to receive anthracyclines.

scholarly journals Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available

2016 ◽  
Vol 12 (1) ◽  
pp. 219-232 ◽  
Author(s):  
Ashkan Ertefaie ◽  
Dylan Small ◽  
James Flory ◽  
Sean Hennessy
Keyword(s):  
Selection Bias ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Health Improvement ◽  
Effect Estimate ◽  
Diabetic Patients ◽  
Data Set ◽  
The Health Improvement Network ◽  
Theoretical Evidence

Abstract Instrumental variable (IV) methods are widely used to adjust for the bias in estimating treatment effects caused by unmeasured confounders in observational studies. It is common that a comparison between two treatments is focused on and that only subjects receiving one of these two treatments are considered in the analysis even though more than two treatments are available. In this paper, we provide empirical and theoretical evidence that the IV methods may result in biased treatment effects if applied on a data set in which subjects are preselected based on their received treatments. We frame this as a selection bias problem and propose a procedure that identifies the treatment effect of interest as a function of a vector of sensitivity parameters. We also list assumptions under which analyzing the preselected data does not lead to a biased treatment effect estimate. The performance of the proposed method is examined using simulation studies. We applied our method on The Health Improvement Network (THIN) database to estimate the comparative effect of metformin and sulfonylureas on weight gain among diabetic patients.

scholarly journals Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness

2020 ◽  
Vol 8 (1) ◽  
pp. 182-208
Author(s):  
Nick Huntington-Klein
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Weighted Average ◽  
Small Sample ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Heterogeneous Effects ◽  
The Impact ◽  
Local Average

AbstractIn Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples.

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