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Kung-Jong Lui

Publications and source records attributed to Kung-Jong Lui.

15 recordsLinked to original sources
PubMed2007

Interval estimation of the risk difference in non-compliance randomized trials with repeated binary measurements.

In a randomized clinical trial (RCT), we often encounter non-compliance with the treatment protocol for a subset of patients. The intention-to-treat (ITT) analysis is probably the most commonly used method in a RCT with non-compliance. However, the ITT analysis estimates 'the programmatic effectiveness' rather than 'the biological efficacy'. In this paper, we focus attention on the latter index and consider use of the risk difference (RD) to measure the effect of a treatment. Based on a simple additive risk model proposed elsewhere, we develop four asymptotic interval estimators of the RD for repeated binary measurements in a RCT with non-compliance. We apply Monte Carlo simulation to evaluate and compare the finite-sample performance of these interval estimators in a variety of situations. We find that all interval estimators considered here can perform well with respect to the coverage probability. We further find that the interval estimator using a tanh(-1)(x) transformation is probably more precise than the others, while the interval estimator derived from a randomization-based approach may cause a slight loss of precision. When the number of patients per treatment is large and the probability of compliance to an assigned treatment is high, we find that all interval estimators discussed here are essentially equivalent. Finally, we illustrate use of these interval estimators with data simulated from a trial of using macrophage colony-stimulating factor to reduce febrile neutropenia incidence in acute myeloid leukaemia patients.

Humans↗
PubMed2006

Interval estimation of risk ratio in the simple compliance randomized trial.

Consider the simple compliance randomized trial (SCRT), in which patients assigned to an experimental group may switch to receive a control treatment, but patients assigned to a control group are assumed to all receive their assigned treatment. We develop five asymptotic interval estimators for the relative risk (RR) of probabilities of response among patients who would comply with the experimental treatment under the SCRT. We employ Monte Carlo simulation to evaluate the performance of these interval estimators in a variety of situations. We note that the interval estimator using Wald's statistic and the interval estimator derived from a quadratic equation based on asymptotic properties of the maximum likelihood estimator (MLE) can lose accuracy, while the most commonly-used interval estimator using a logarithmic transformation of the MLE for the RR suggested elsewhere can lose efficiency. We further note that the probability of failure to apply the interval estimator derived from an idea used in Fieller's Theorem to produce a confidence interval can be non-negligible even when the number of patients in both comparison groups is not small. Finally, we find that an interval estimator using a simple ad hoc procedure of combining two interval estimators with and without a logarithmic transformation of the MLE can consistently perform well with respect to the coverage probability even when the number of patients per treatment is not large. In fact, this estimator uniformly outperforms all the other estimators considered here and thereby is recommended for general use. We include an example regarding the study of vitamin A supplementation to reduce the mortality among preschool children to illustrate the use of interval estimators discussed in this paper.

Algorithms↗
PubMed2006

Procedures for testing the homogeneity of relative difference in sparse data.

To quantify the excess effect of an experimental treatment over a placebo group in clinical trials, we often consider use of the relative difference, defined as the proportion of patients who would respond to the experimental treatment among those who would not otherwise if they were assigned to the placebo group. To control the effects due to confounders on the response of interest, we frequently employ stratified analysis in practice. Before obtaining a summary estimate of the relative difference, it is desirable to assess whether this measure is constant across strata. Based on the beta-binomial model, we develop simple procedures for testing the homogeneity of relative difference for sparse data in which we have many strata but few patients per stratum. Using Monte Carlo simulations, we demonstrate that the proposed test procedures can generally perform well with respect to type I error in a variety of situations. We further evaluate and study the power of these test procedures. Finally, we note some robustness in using the test procedures proposed here.

Antineoplastic Agents↗
PubMed2006

A Monte Carlo evaluation of five interval estimators for the relative risk in sparse data.

The relative risk (RR) is one of the most frequently used indices to measure the strength of association between a disease and a risk factor in etiological studies or the efficacy of an experimental treatment in clinical trials. In this paper, we concentrate attention on interval estimation of RR for sparse data, in which we have only a few patients per stratum, but a moderate or large number of strata. We consider five asymptotic interval estimators for RR, including a weighted least-squares (WLS) interval estimator with an ad hoc adjustment procedure for sparse data, an interval estimator proposed elsewhere for rare events, an interval estimator based on the Mantel-Haenszel (MH) estimator with a logarithmic transformation, an interval estimator calculated from a quadratic equation, and an interval estimator derived from the ratio estimator with a logarithmic transformation. On the basis of Monte Carlo simulations, we evaluate and compare the performance of these five interval estimators in a variety of situations. We note that, except for the cases in which the underlying common RR across strata is around 1, using the WLS interval estimator with the adjustment procedure for sparse data can be misleading. We note further that using the interval estimator suggested elsewhere for rare events tends to be conservative and hence leads to loss of efficiency. We find that the other three interval estimators can consistently perform well even when the mean number of patients for a given treatment is approximately 3 patients per stratum and the number of strata is as small as 20. Finally, we use a mortality data set comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the estimators discussed in this paper.

Algorithms↗
PubMed2005

Estimation of attributable risk for case-control studies with multiple matching.

Kuritz and Landis considered case-control studies with multiple matching and proposed an asymptotic interval estimator of the attributable risk based on Wald's statistic. Using Monte Carlo simulation, Kuritz and Landis demonstrated that their interval estimator could perform well when the number of matched sets was large (>or=100). However, the number of matched sets may often be moderate or small in practice. In this paper, we evaluate the performance of Kuritz and Landis' interval estimator in small or moderate number of matched sets and compare it with four other interval estimators. We note that the coverage probability of Kuritz and Landis' interval estimator tends to be less than the desired confidence level when the probability of exposure among cases is large. In these cases, the interval estimator using the logarithmic transformation and the two interval estimators derived from the quadratic equations developed here can generally improve the coverage probability of Kuritz and Landis' interval estimator, especially for the case of a small number of matched sets. Furthermore, we find that an interval estimator derived from a quadratic equation is consistently more efficient than Kuritz and Landis' interval estimator. The interval estimator using the logit transformation, although which performs poorly when the underlying odds ratio (OR) is close to 1, can be useful when both the probability of exposure among cases and the underlying OR are moderate or large.

Case-Control Studies↗
PubMed2005

Interval estimation of the difference in proportions under m-to-one matching.

When the number of potential controls is large relative to the number of available cases, or when little effort needs to be expended in collecting the relevant information on the controls, we often apply multiple matching to attain the validity or increase the efficiency of our inference in epidemiological studies. In this paper, we focus interval estimation on the difference in proportions for m-to-one matching. We consider four asymptotic interval estimators, including the estimator directly using the Mantel-Haenszel (MH) point estimator, the estimator using the tanh(-1)(x) transformation, the estimator derived from the Cochran-Mantel-Haenszel (CMH) test statistic, and the estimator derived from the quadratic inequality developed in this paper. To evaluate and compare the performance of these estimators, we employ Monte Carlo simulation. We find that the estimator directly using the MH estimator can have the coverage probability less than the desired confidence level when the number of matched sets is small. We note that the estimator derived from the quadratic inequality can perform well when the underlying difference is close to 0 even for a small number of matched sets. However, this estimator tends to have the coverage probability less than the desired confidence level as well when the underlying difference in proportions is large. By contrast, the estimator using the CMH statistic tends to have the coverage probability larger than the desired confidence level when the underlying difference is small. We also find that the estimator using the tanh(-1)(x) transformation consistently outperforms the interval estimator directly using the MH estimator. We use the data regarding the association between induced abortions and ectopic pregnancy to illustrate the use of these estimators.

Abortion, Induced↗
PubMed2005

Interval estimation of the proportion ratio under multiple matching.

The discussions on interval estimation of the proportion ratio (PR) of responses or the relative risk (RR) of a disease for multiple matching have been generally focused on the odds ratio (OR) based on the assumption that the latter can approximate the former well. When the underlying proportion of outcomes is not rare, however, the results for the OR would be inadequate for use if the PR or RR was the parameter of our interest. In this paper, we develop five asymptotic interval estimators of the common PR (or RR) for multiple matching. To evaluate and compare the finite sample performance of these estimators, we apply Monte Carlo simulation to calculate the coverage probability and the average length of the resulting confidence intervals in a variety of situations. We note that when we have a constant number of matching, the interval estimator using the logarithmic transformation of the Mantel-Haenszel estimator, the interval estimator derived from the quadratic inequality given in this paper, and the interval estimator using the logarithmic transformation of the ratio estimator can consistently perform well. When the number of matching varies between matched sets, we find that the interval estimator using the logarithmic transformation of the ratio estimator is probably the best among the five interval estimators considered here in the case of a small number (=20) of matched sets. To illustrate the use of these interval estimators, we employ the data studying the supplemental ascorbate in the supportive treatment of terminal cancer patients.

Ascorbic Acid↗
PubMed2005

A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study.

In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.

Computer Simulation↗
PubMed2004

Eight interval estimators of a common rate ratio under stratified Poisson sampling.

Under the assumption that the rate ratio (RR) is constant across strata, we consider eight interval estimators of RR under stratified Poisson sampling: the weighted least-squares (WLS) interval estimator with the logarithmic transformation, the interval estimator using the principle analogous to that of Fieller's Theorem, the interval estimators using Wald's statistic with and without the logarithmic transformation, the interval estimators using the Mantel-Haenszel statistic with and without the logarithmic transformation, the score test-based interval estimator, and the asymptotic likelihood ratio test-based interval estimator. We apply Monte Carlo simulation to evaluate and compare the performance of these estimators with respect to the coverage probability and the average length in a variety of situations. We find that the coverage probability of the commonly used WLS interval estimator tends to be smaller than the desired confidence level, especially when we have a large number of strata with a small expected total number of cases (ETNC) per stratum and the underlying RR is far away from 1 (i.e. RR18 or RR8). We further find that the two estimators with the logarithmic transformation, as well as the two test-based estimators can consistently perform well in a variety of situations. When RR1 with a given reasonable size of ETNC per stratum, we note that the interval estimators without the logarithmic transformation can be preferable to the corresponding ones with the logarithmic transformation in the situations considered here. However, when evaluating the non-coverage probability in the two tails, we find that the former tends to shift the left, while the latter is generally not subject to this concern. We also note that interval estimator using the Mantel-Haenszel (MH) statistic with the logarithmic transformation is likely less efficient than the two test-based interval estimators using the score and the likelihood ratio tests. Finally, we use the data taken from a study of the postmenopausal hormone use on the risk of breast cancer in women as an example to illustrate the use of these interval estimators considered here.

Adult↗
PubMed2004

Testing non-inferiority (and equivalence) between two diagnostic procedures in paired-sample ordinal data.

Before adopting a new diagnostic procedure, which is more convenient and less expensive than the standard existing procedure, it is essentially important to assess whether the diagnostic accuracy of the new procedure is non-inferior (or equivalent) to that of the standard procedure. In this paper, we consider the situation where test responses are on an ordinal scale with more than two categories. We give two definitions of non-inferiority, one in terms of the probability of correctly identifying the case for a randomly selected pair of a case and a non-case over all possible cut-off points, and the other in terms of both the sensitivity and specificity directly. On the basis of large sample theory, we develop two simple test procedures for detecting non-inferiority. We further conduct Monte Carlo simulation to evaluate the finite sample performance of these test procedures. We note that the two asymptotic test procedures proposed here can actually perform reasonably well in a variety of situations even when the numbers of studied subjects from the diseased and non-diseased populations are not large. To illustrate the use of the proposed test procedures, we include an example of determining whether the diagnostic accuracy of using a digitized film is non-inferior to that of using a plain film for screening breast cancer. Finally, we note that the extension of these results to accommodate the case of detecting (two-sided) equivalence is simply straightforward.

Breast Neoplasms↗
PubMed2004

A simple logical solution to eliminate the limitations of using the number needed to treat.

Because it can be easily understood by clinicians, the "number needed to treat" (NNT) has recently received considerable attention in the clinical sciences. When the NNT is applied to summarize findings, it is important to understand the limitations of its use. First, the important confusion and criticisms found in the past on using the confidence interval of the NNT are summarized. To eliminate the confusion and criticisms, the NNT should be used only when there is some prior knowledge to rank the order of the response rates under comparison. An example is included to illustrate the use of the proposed approach and show how a number of the criticisms, limitations, and undesirable properties of the interval estimator for the NNT can be avoided. To test the equality of two response rates, the use of test statistics based on other well-established indices is recommended in lieu of the NNT.

Antihypertensive Agents↗
PubMed2003

Interval estimation of the attributable risk for multiple exposure levels in case-control studies with confounders.

The attributable risk (AR) is one of the most important and commonly-used epidemiological indices to assess the public health importance of an association between a risk factor and a disease. When the underlying risk factor has multiple exposure levels in the presence of confounders, we consider the case-control studies using random sampling to collect the cases and controls here. We develop four asymptotic interval estimators for AR, including the interval estimator using Wald's statistic, the interval estimator using the logarithmic transformation, the interval estimator using the logit transformation, and the interval estimator derived from a quadratic equation. We apply Monte Carlo simulation to evaluate the finite-sample performance of these interval estimators in a variety of situations. We demonstrate that given an adequately large sample size, all the estimators developed here can actually perform reasonably well. We note that the interval estimator using the logit transformation may be of limited use when the number of studied subjects is not large. We also note that the interval estimator using the logarithmic transformation can lose efficiency compared to the interval estimator using Wald's statistic or the interval estimator derived from a quadratic equation developed in this paper. Finally, we use the data taken from a case control study of the oral contraceptive use in myocardial infarction patients with various smoking levels to illustrate he practical usefulness of these estimators.

Adult↗
PubMed2002

Interval estimation of generalized odds ratio in data with repeated measurements.

When the underlying responses are on an ordinal scale, the generalized odds ratio (GOR), defined as the ratio of the proportions of concordant and discordant pairs, is a useful index to summarize the difference between two stochastically ordered distributions of an ordinal categorical variable. We discuss interval estimation of the GOR for ordinal data with repeated measurements. On the basis of the Dirichlet-multinomial model, we develop three asymptotic interval estimators of the GOR using Wald's test statistic, a logarithmic transformation, and a method analogous to Fieller's theorem, respectively. To evaluate and compare the finite-sample performance of these estimators, we apply Monte Carlo simulation. We find that when the number of subjects per group is not large, the coverage probability of interval estimator using Wald's test statistic is likely to be less than the desired confidence level. By contrast, the coverage probability of the other two estimators are approximately equal to or larger than the desired confidence level. When the number of subjects per group is small and the intraclass correlation between repeated measurements within subjects is large, we note that applying the interval estimator derived from a method analogous to Fieller's theorem can lose efficiency. We also note that the interval estimator using the logarithmic transformation is generally preferable to the other two estimators with respect to both the coverage probability and the average length. Finally, on the basis of a few preliminary simulations, we do find some robustness for all the estimators developed here. We include an example comparing the inflammation grade after lung transplant between surgeries to illustrate the use of the proposed interval estimators.

Animals↗