Design and analysis of stratified clinical trials in the presence of bias

Ralf-Dieter Hilgers, Martin Manolov, Nicole Heussen, William F Rosenberger, Ralf-Dieter Hilgers, Martin Manolov, Nicole Heussen, William F Rosenberger

Abstract

Background: Among various design aspects, the choice of randomization procedure have to be agreed on, when planning a clinical trial stratified by center. The aim of the paper is to present a methodological approach to evaluate whether a randomization procedure mitigates the impact of bias on the test decision in clinical trial stratified by center.

Methods: We use the weighted t test to analyze the data from a clinical trial stratified by center with a two-arm parallel group design, an intended 1:1 allocation ratio, aiming to prove a superiority hypothesis with a continuous normal endpoint without interim analysis and no adaptation in the randomization process. The derivation is based on the weighted t test under misclassification, i.e. ignoring bias. An additive bias model combing selection bias and time-trend bias is linked to different stratified randomization procedures.

Results: Various aspects to formulate stratified versions of randomization procedures are discussed. A formula for sample size calculation of the weighted t test is derived and used to specify the tolerated imbalance allowed by some randomization procedures. The distribution of the weighted t test under misclassification is deduced, taking the sequence of patient allocation to treatment, i.e. the randomization sequence into account. An additive bias model combining selection bias and time-trend bias at strata level linked to the applied randomization sequence is proposed. With these before mentioned components, the potential impact of bias on the type one error probability depending on the selected randomization sequence and thus the randomization procedure is formally derived and exemplarily calculated within a numerical evaluation study.

Conclusion: The proposed biasing policy and test distribution are necessary to conduct an evaluation of the comparative performance of (stratified) randomization procedure in multi-center clinical trials with a two-arm parallel group design. It enables the choice of the best practice procedure. The evaluation stimulates the discussion about the level of evidence resulting in those kind of clinical trials.

Keywords: Multi-center clinical trial; sample size; selection bias; stratified randomization; time-trend bias; type I error probability; weighted t test.

References

    1. Fleiss JL. Analysis of data from multiclinic trials. Control Clin Trials 1986; 7: 267–275.
    1. Rosenberger WF, Lachin J. Randomization in clinical trials: theory and practice, New York, NY: Wiley, 2016.
    1. Proschan M. Influence of selection bias on type I error rate under random permuted block designs. Stat Sin 1994; 4: 219–231.
    1. Kennes LN, Cramer E, Hilgers RD, et al. The impact of selection bias on test decisions in randomized clinical trials. Stat Med 2011; 30: 2573–2581.
    1. Tamm M, Cramer E, Kennes LN, et al. Influence of selection bias on the test decision – a simulation study. Methods Inf Med 2012; 51: 138–143.
    1. Hilgers RD, Uschner D, Rosenberger WF, et al. ERDO – a framework to select an appropriate randomization procedure for clinical trials. BMC Med Res Methodol 2017; 17(1): 159.
    1. Efron B. Forcing a sequential experiment to be balanced. Biometrika 1971; 58: 403–417.
    1. Soares JF, Wu CFJ. Some restricted randomization rules in sequential designs. Commun Stat Theory Methods 1982; 12: 2017–2034.
    1. Mantel N. Random numbers and experimental design. Ann Stat 1969; 23: 32–34.
    1. Zelen M. The randomization and stratification of patients to clinical trials. J Chronic Dis 1974; 27: 365–375.
    1. Berger VW, Ivanova A, Knoll DM. Minimizing predictability while retaining balance through the use of less restrictive randomization procedures. Stat Med 2003; 22(19): 3017–3028.
    1. ICH E9. Statistical principles for clinical trials. (accessed 24 April 2019).
    1. Johnson NL, Kotz S. Continuous univariate distributions – 2, New York, NY: Wiley, 1970.
    1. Searle SR. Linear models. New York, NY: Wiley, 1971.
    1. Lin Z. An issue of statistical analysis in controlled multi centre studies: how shall we weight the centres?. Stat Med 1999; 18: 365–373.
    1. Tamm M, Hilgers RD. Chronological bias in randomized clinical trials arising from different types of unobserved time trends. Methods Inf Med 2014; 53: 501–510.
    1. Berger VW. Selection bias and covariate imbalances in randomized clinical trials. Chichester: Wiley, 2005.
    1. Kraemer H, Fendt KH. Random assignment in clinical trials: issues in planning (infant health and development program). J Clin Epidemiol 1990; 43: 1157–1167.
    1. Ganju J, Zhou K. The benefit of stratification in clinical trials revisited. Stat Med 2011; 30: 2881–2889.
    1. Pickering RM, Weatherall M. The analysis of continuous outcomes in multi-centre trials with small centre sizes. Stat Med 2007; 26: 5445–5456.
    1. Chu R, Thabane L, Ma J, et al. Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: a simulation study. BMC Med Res Methodol 2011; 11: 21.
    1. Feaster DJ, Mikulich-Gilbertson S, Brinks AM. Modeling site effects in the design and analysis of multisite trials. Am J Drug Alcohol Abuse 1998; 37: 383–391.
    1. Zheng L, Zelen M. Multi-center clinical trials: randomization and ancillary statistics. Ann Appl Stat 2008; 2(2): 582–600.
    1. Ruvuna F. Unequal center sizes, sample size, and power in multicenter clinical trials. Drug Inf J 2004; 38: 387–394.
    1. Vierron E, Giraudeau B. Sample size calculation for multicenter randomized trial: Taking the center effect into account. Control Clin Trials 2007; 28: 451–458.
    1. Ganju J, Mehrotra DV. Stratified experiments reexamined with emphasis on multicenter trials. Control Clin Trials 2003; 24: 167–181.

Source: PubMed

3
Abonnere