Biomedical subjects

K K Gordon Lan

Publications and source records attributed to K K Gordon Lan.

4 recordsLinked to original sources

PubMed2004

Increasing the sample size when the unblinded interim result is promising.

Increasing the sample size based on unblinded interim result may inflate the type I error rate and appropriate statistical adjustments may be needed to control the type I error rate at the nominal level. We briefly review the existing approaches which allow early stopping due to futility, or change the test statistic by using different weights, or adjust the critical value for final test, or enforce rules for sample size recalculation. The implication of early stopping due to futility and a simple modification to the weighted Z-statistic approach are discussed. In this paper, we show that increasing the sample size when the unblinded interim result is promising will not inflate the type I error rate and therefore no statistical adjustment is necessary. The unblinded interim result is considered promising if the conditional power is greater than 50 per cent or equivalently, the sample size increment needed to achieve a desired power does not exceed an upper bound. The actual sample size increment may be determined by important factors such as budget, size of the eligible patient population and competition in the market. The 50 per cent-conditional-power approach is extended to a group sequential trial with one interim analysis where a decision may be made at the interim analysis to stop the trial early due to a convincing treatment benefit, or to increase the sample size if the interim result is not as good as expected. The type I error rate will not be inflated if the sample size may be increased only when the conditional power is greater than 50 per cent. If there are two or more interim analyses in a group sequential trial, our simulation study shows that the type I error rate is also well controlled.

Anti-HIV Agents↗

PubMed2004

Conditional bias of point estimates following a group sequential test.

Repeated significance testing in a sequential experiment not only increases the overall type I error rate of the false positive conclusion but also causes biases in estimating the unknown parameter. In general, the test statistics in a sequential trial can be properly approximated by a Brownian motion with a drift parameter at interim looks. The unadjusted maximum likelihood estimator can be potentially very biased due to the possible early stopping rule at any interim. In this paper, we investigate the conditional and marginal biases with focus on the conditional one upon the stopping time in estimating the Brownian motion drift parameter. It is found that the conditional bias may be very serious for existing point estimation methods, even if the unconditional bias is satisfactory. New conditional estimators are thus proposed, which can significantly reduce the conditional bias from unconditional estimators. The results of Monte-Carlo studies show that the proposed estimators can provide a much smaller conditional bias and MSE than the naive MLE and a Whitebead's bias reduced estimator.

Effect Modifier, Epidemiologic↗

PubMed2003

Over-ruling a group sequential boundary--a stopping rule versus a guideline.

We evaluate the properties of group sequential procedures where the trial is continued even though the boundary for statistical significance (stopping) to demonstrate effectiveness has been crossed. In this case, one may buy-back the previously spent alpha probability to be re-spent or re-distributed at future looks. We show that such plans using an O'Brien-Fleming-like spending function have a negligible effect on the final type I error probability and on the ultimate power of the study. With a Pocock-like bound, however, there is a small additional loss in power. We also show that this approach can be simplified by using a fixed-sample size Z critical value for future looks after buying-back previously spent alpha, such as using a critical Z value of 1.96 for alpha=0.025. We show that this procedure preserves the type I error probability while incurring a minimal loss in power. In this sense, one still has a stopping boundary rather than simply a guideline. This concept is discussed relative to monitoring procedures for inferiority or futility, and cases where both an upper and lower boundary are employed.

Biometry↗

PubMed2003

Monitoring mortality at interim analyses while testing a composite endpoint at the final analysis.

Mortality is often used as the clinical endpoint in clinical trials for acute diseases and takes precedence over any other outcome. A composite outcome such as death plus disease occurrence (or recurrence) or death plus hospitalization may also be considered, sometimes even as the primary outcome due to practical sample size issues. That is, a composite endpoint should have a higher event rate and thus a smaller sample size than for mortality alone to reach the same power. Two different scenarios are considered: in Scenario 1, the composite outcome is the primary endpoint and the mortality outcome is secondary; in Scenario 2, the mortality outcome is the primary endpoint and the composite outcome is secondary. In either scenario, the trial will be stopped if the simple mortality outcome shows an adverse effect or a significant benefit at an interim analysis, while the composite outcome will be tested at the final analysis if the mortality outcomes fails to show significance. These scenarios are typical in many trials sponsored by industry for regulatory approval. We refer to them as a switching the primary endpoint process. Two switching-endpoint procedures are proposed to calculate the efficacy boundary for the composite test statistic at the final analysis. The Bonferroni method is used in Method 1. In Method 2, the calculation is based upon the joint distribution of the test statistics for the simple mortality and the composite outcomes. A completed clinical trial, prospective randomized amlodipine survival evaluation (PRAISE-1), is used to illustrate the two switching-endpoint procedures. A simulation study shows that the two switching-endpoint procedures allow a trial to be stopped early due to a clinically relevant benefit in the mortality while preserving the overall alpha level.

Clinical Trials as Topic↗