Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART)

2020 ◽  
Author(s):  
Xiaoxi Yan ◽  
Palash Ghosh ◽  
Bibhas Chakraborty
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Multiple Assignment

scholarly journals Sequential Multiple Assignment Randomized Trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoxi Yan ◽  
David B. Matchar ◽  
Nirmali Sivapragasam ◽  
John P. Ansah ◽  
Aastha Goel ◽  
...  
Keyword(s):  
Insulin Therapy ◽  
Randomized Trial ◽  
Sample Size ◽  
Threshold Value ◽  
Primary Data ◽  
Base Case ◽  
Case Scenario ◽  
Base Case Scenario ◽  
Intermediate Point ◽  
Multiple Assignment

Abstract Background To examine the value of a Sequential Multiple Assignment Randomized Trial (SMART) design compared to a conventional randomized control trial (RCT) for telemedicine strategies to support titration of insulin therapy for Type 2 Diabetes Mellitus (T2DM) patients new to insulin. Methods Microsimulation models were created in R using a synthetic sample based on primary data from 63 subjects enrolled in a pilot study of a smartphone application (App), Diabetes Pal compared to a nurse-based telemedicine strategy (Nurse). For comparability, the SMART and an RCT design were constructed to allow comparison of four (embedded) adaptive interventions (AIs). Results In the base case scenario, the SMART has similar overall mean expected HbA1c and cost per subject compared with RCT, for sample size of n = 100 over 10,000 simulations. SMART has lower (better) standard deviations of the mean expected HbA1c per AI, and higher efficiency of choosing the correct AI across various sample sizes. The differences between SMART and RCT become apparent as sample size decreases. For both trial designs, the threshold value at which a subject was deemed to have been responsive at an intermediate point in the trial had an optimal choice (i.e., the sensitivity curve had a U-shape). SMART design dominates the RCT, in the overall mean HbA1c (lower value) when the threshold value is close to optimal. Conclusions SMART is suited to evaluating the efficacy of different sequences of treatment options, in addition to the advantage of providing information on optimal treatment sequences.

Sample size calculations for group randomized trials with unequal group sizes through Monte Carlo simulations

2016 ◽  
Vol 27 (9) ◽  
pp. 2569-2580 ◽  
Author(s):  
Yang Shi ◽  
Ji-Hyun Lee
Keyword(s):  
Monte Carlo ◽  
Randomized Trial ◽  
Sample Size ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Sample Size Calculation ◽  
Model Framework ◽  
Mixed Effect ◽  
Group Randomized Trials ◽  
Group Randomized Trial

Group randomized trial design is common in cancer prevention and health promotion research with intervention development. Several methods have been developed to handle the design and analytical issues for group randomized trial including the intraclass correlation coefficient. The widely used methods for the sample size calculation for the group randomized trial assume equal sizes across groups. In practice this assumption often fails and group randomized trial studies suffer from considerably lower statistical power than as planned. A few studies have developed sample size calculation methods for unequal group sizes, but most of them are limited to continuous outcomes. In this study, we develop a method for sample size calculation for group randomized trial studies with unequal group sizes based on Monte Carlo simulation in the mixed effect model framework. This approach incorporates the variation of group sizes and can be applied to group randomized trials with different types of outcomes. Further, it is easy to implement and can be applied to most commonly used group randomized trial designs such as pre-and-post cross-sectional and cohort study designs. We demonstrate the application of the proposed approach to two-arm group randomized trial studies with continuous and binary outcomes through simulations and analysis of a real group randomized trial dataset.

scholarly journals Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

2019 ◽  
Vol 29 (7) ◽  
pp. 1891-1912
Author(s):  
Nicholas J Seewald ◽  
Kelley M Kidwell ◽  
Inbal Nahum-Shani ◽  
Tianshuang Wu ◽  
James R McKay ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Treatment Regimen ◽  
Statistical Efficiency ◽  
Entire Study ◽  
Treatment Regimens ◽  
Longitudinal Outcome ◽  
Multiple Assignment ◽  
Dynamic Treatment ◽  
Inflation Factor

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

scholarly journals Sequential Multiple Assignment Randomized Trial (SMART) To Identify Optimal Sequences Of Telemedicine Interventions For Improving Initiation of Insulin Therapy: A Simulation Study

2021 ◽  
Author(s):  
Xiaoxi Yan ◽  
David B. Matchar ◽  
Aastha Goel ◽  
Nirmali Sivapragasam ◽  
John P. Ansah ◽  
...  
Keyword(s):  
Insulin Therapy ◽  
Randomized Trial ◽  
Sample Size ◽  
Threshold Value ◽  
Primary Data ◽  
Base Case ◽  
Case Scenario ◽  
Base Case Scenario ◽  
Intermediate Point ◽  
Multiple Assignment

Abstract Background: To examine the value of a Sequential Multiple Assignment Randomized Trial (SMART) design compared to a conventional randomized control trial (RCT) for telemedicine strategies to support titration of insulin therapy for Type 2 Diabetes Mellitus (T2DM) patients new to insulin. Methods: Microsimulation models were created in R using a synthetic sample based on primary data from 63 subjects enrolled in a pilot study of a smartphone application (App), Diabetes Pal compared to a nurse-based telemedicine strategy (Nurse). For comparability, the SMART and an RCT design were constructed to allow comparison of four (embedded) adaptive interventions (AIs). Results: In the base case scenario, the SMART has similar overall mean expected HbA1c and cost per subject compared with RCT, for sample size of n = 100 over 10000 simulations. SMART has lower (better) standard deviations of the mean expected HbA1c per AI, and higher efficiency of choosing the correct AI across various sample sizes. The differences between SMART and RCT become apparent as sample size decreases. For both trial designs, the threshold value at which a subject was deemed to have been responsive at an intermediate point in the trial had an optimal choice (i.e., the sensitivity curve had a U-shape). SMART design dominates the RCT, in the overall mean HbA1c (lower value) when the threshold value is close to optimal. Conclusions: SMART is suited to evaluating the efficacy of different sequences of treatment options, in addition to the advantage of providing information on optimal treatment sequences.

scholarly journals A Sequential Multiple Assignment Randomized Trial (SMART) study of medication and CBT sequencing in the treatment of pediatric anxiety disorders

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bradley S. Peterson ◽  
Amy E. West ◽  
John R. Weisz ◽  
Wendy J. Mack ◽  
Michele D. Kipke ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Anxiety Disorders ◽  
Randomized Trial ◽  
The Other ◽  
Smart Study ◽  
Main Effect ◽  
Multiple Assignment ◽  
Stage 1 ◽  
Main Effects ◽  
Treatment Sequences

Abstract Background Treatment of a child who has an anxiety disorder usually begins with the question of which treatment to start first, medication or psychotherapy. Both have strong empirical support, but few studies have compared their effectiveness head-to-head, and none has investigated what to do if the treatment tried first isn’t working well—whether to optimize the treatment already begun or to add the other treatment. Methods This is a single-blind Sequential Multiple Assignment Randomized Trial (SMART) of 24 weeks duration with two levels of randomization, one in each of two 12-week stages. In Stage 1, children will be randomized to fluoxetine or Coping Cat Cognitive Behavioral Therapy (CBT). In Stage 2, remitters will continue maintenance-level therapy with the single-modality treatment received in Stage 1. Non-remitters during the first 12 weeks of treatment will be randomized to either [1] optimization of their Stage 1 treatment, or [2] optimization of Stage 1 treatment and addition of the other intervention. After the 24-week trial, we will follow participants during open, naturalistic treatment to assess the durability of study treatment effects. Patients, 8–17 years of age who are diagnosed with an anxiety disorder, will be recruited and treated within 9 large clinical sites throughout greater Los Angeles. They will be predominantly underserved, ethnic minorities. The primary outcome measure will be the self-report score on the 41-item youth SCARED (Screen for Child Anxiety Related Disorders). An intent-to-treat analysis will compare youth randomized to fluoxetine first versus those randomized to CBT first (“Main Effect 1”). Then, among Stage 1 non-remitters, we will compare non-remitters randomized to optimization of their Stage 1 monotherapy versus non-remitters randomized to combination treatment (“Main Effect 2”). The interaction of these main effects will assess whether one of the 4 treatment sequences (CBT➔CBT; CBT➔med; med➔med; med➔CBT) in non-remitters is significantly better or worse than predicted from main effects alone. Discussion Findings from this SMART study will identify treatment sequences that optimize outcomes in ethnically diverse pediatric patients from underserved low- and middle-income households who have anxiety disorders. Trial registration This protocol, version 1.0, was registered in ClinicalTrials.gov on February 17, 2021 with Identifier: NCT04760275.

The most frequently asked question to a statistician: The sample size

2017 ◽  
Vol 23 (5) ◽  
pp. 644-646 ◽  
Author(s):  
Maria Pia Sormani
Keyword(s):  
Clinical Study ◽  
Sample Size ◽  
Study Design ◽  
Treatment Effect ◽  
Sample Size Calculation ◽  
Ethical Implications ◽  
Number Of Patients ◽  
Reducing Costs ◽  
Correct Size

The calculation of the sample size needed for a clinical study is the challenge most frequently put to statisticians, and it is one of the most relevant issues in the study design. The correct size of the study sample optimizes the number of patients needed to get the result, that is, to detect the minimum treatment effect that is clinically relevant. Minimizing the sample size of a study has the advantage of reducing costs, enhancing feasibility, and also has ethical implications. In this brief report, I will explore the main concepts on which the sample size calculation is based.

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