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J M Lachin

Publications and source records attributed to J M Lachin.

At least 19 recordsLinked to original sources
PubMed1992

Some large-sample distribution-free estimators and tests for multivariate partially incomplete data from two populations.

The most common instance of multivariate observations is the case of repeated measures over time. The two most widely used methods for the analysis of K repeated measures for two groups are the K degrees of freedom (d.f.) T2 MANOVA F-test and the within-subjects 1 degree of freedom ANOVA F-test. Both require complete samples from normally distributed populations. In this paper, I describe alternative K and 1 d.f. distribution-free procedures which allow for randomly missing observations. These include a large-sample analysis of means, the Wei and Lachin multivariate Wilcoxon test with estimates of the Mann-Whitney parameter, and a multivariate Hodges-Lehmann location shift estimator based on the multivariate U-statistic of Wei and Johnson. Each of these methods provides a distribution-free K-variate estimate of the magnitude of group differences which can be used as the basis for an overall test of group differences. These tests include the K d.f. omnibus T2-like test, 1 d.f. tests of restricted hypotheses, such as the Wei-Lachin multivariate one-sided test of stochastic ordering, and the test of general association based on a minimum variance generalized least squares (GLS) estimate of the average group difference. I then describe covariate stratified-adjusted GLS estimates and tests of group differences. This approach also provides tests of homogeneity (interaction) for within-subjects and between-subjects effects. I illustrate these analyses with an analysis of repeated cholesterol measurements in two groups of patients, stratified by sex. Such analyses provide an overall distribution-free summary estimate and test of the treatment effect obtained by combining the group differences over both time (repeated measures) and strata.

Chenodeoxycholic Acid↗
PubMed1992

Power and sample size evaluation for the McNemar test with application to matched case-control studies.

Various expressions have appeared for sample size calculation based on the power function of McNemar's test for paired or matched proportions, especially with reference to a matched case-control study. These differ principally with respect to the expression for the variance of the statistic under the alternative hypothesis. In addition to the conditional power function, I identify and compare four distinct unconditional expressions. I show that the unconditional calculation of Schlesselman for the matched case-control study can be expressed as a first-order unconditional calculation as described by Miettinen. Corrections to Schlesselman's unconditional expression presented by Fleiss and Levin and by Dupont, which use different models to describe exposure association among matched cases and controls, are also equivalent to a first-order unconditional calculation. I present a simplification of these corrections that directly provides the underlying table of cell probabilities, from which one can perform any of the alternative sample size calculations. Also, I compare the four unconditional sample size expressions relative to the exact power function. The conclusion is that Miettinen's first-order expression tends to underestimate sample size, while his second-order expression is usually fairly accurate, though possibly slightly anti-conservative. A multinomial-based expression presented by Connor, among others, is also fairly accurate and is usually slightly conservative. Finally, a local unconditional expression of Mitra, among others, tends to be excessively conservative.

Case-Control Studies↗
PubMed1992

A controlled trial of plasmapheresis therapy in severe lupus nephritis. The Lupus Nephritis Collaborative Study Group.

BACKGROUND: The prognosis of patients with systemic lupus erythematosus who have glomerulonephritis is poor, despite treatment with immunosuppressive therapy. Plasmapheresis therapy has been used, but there have been few controlled clinical observations of its efficacy. METHODS: We carried out a randomized, controlled trial comparing a standard-therapy regimen of prednisone and cyclophosphamide (standard therapy) with a regimen of standard therapy plus plasmapheresis in 86 patients with severe lupus nephritis in 14 medical centers. The patients underwent plasmapheresis three times weekly for four weeks. Drug therapy was standardized, with strict adherence to nine detailed medical-management protocols. RESULTS: Forty-six patients received standard therapy, and 40 patients received standard therapy plus plasmapheresis. The mean follow-up was 136 weeks. Six patients (13 percent) in the standard-therapy group and eight patients (20 percent) in the plasmapheresis group died. Renal failure developed in 8 patients (17 percent) in the standard-therapy group, as compared with 10 (25 percent) in the plasmapheresis group. Thirty patients (35 percent) reached stopping points--14 (30 percent) in the standard-therapy group and 16 (40 percent) in the plasmapheresis group. A similar number of patients in each group had a decrease in both the serum creatinine concentration and urinary protein excretion to approximately normal values. Patients treated with plasmapheresis had a significantly more rapid reduction of serum concentrations of antibodies against double-stranded DNA and cryoglobulins. CONCLUSIONS: Treatment with plasmapheresis plus a standard regimen of prednisone and cyclophosphamide therapy does not improve the clinical outcome in patients with systemic lupus erythematosus and severe nephritis, as compared with the standard regimen alone.

Adult↗
PubMed1992

Termination of a clinical trial with no treatment group difference: the Lupus Nephritis Collaborative Study.

The Lupus Nephritis Collaborative Study (LNCS) was a multicenter randomized clinical trial designed to assess the effects of standard drug therapy alone versus drug therapy plus plasmapheresis (plasma exchange) on the incidence of fatal or nonfatal renal failure associated with lupus nephritis. After 86 patients had been entered, with a mean of 97 weeks of follow-up, the trial was terminated partly due to lack of a beneficial effect of plasmapheresis. Although there are numerous methods for the statistical analysis of emerging results in a clinical trial, there have been relatively few descriptions of the application of these methods to the termination of a clinical trial when no favorable difference exists between groups. This report presents a review of the statistical methods employed for the pivotal interim analyses of the LNCS that were performed in order to help reach the decision to terminate the trial. These included the assessment of unconditional power post-hoc and the assessment of conditional power using an exact method appropriate for small sample sizes. Conditional power was used to assess the likelihood of detecting a significant treatment effect in the future given the data thus far observed and given reasonable hypotheses regarding the nature of the possible differences between the treatment groups. In addition, weighted-likelihood ratios (Bayes odds ratios) were computed to assess the likelihood of various alternative hypotheses given the present data. We show how such analyses can be useful in reaching a decision to terminate a trial that fails to show a treatment effect.

Combined Modality Therapy↗
PubMed1992

Group sequential distribution-free methods for the analysis of multivariate observations.

Many studies involve the collection of multivariate observations, such as repeated measures, on two groups of subjects who are recruited over time, i.e., with staggered entry of subjects. Various marginal distribution-free multivariate methods have been proposed for the analyses of such multivariate observations where some measures may be missing at random. Using the multivariate U statistic of Wei and Johnson (1985, Biometrika 72, 359-364), we describe the group sequential analysis of such a study where the multivariate observations are observed sequentially--both within and among subjects. We describe a multivariate generalization of the Hodges and Lehmann (1963, Annals of Mathematical Statistics 34, 598-611) estimator of a location shift that can be obtained via the multivariate U statistic with the Mann-Whitney-Wilcoxon kernel. We then describe large-sample group sequential interval estimators and tests based on an aggregate estimate of the location shift combined over all of the repeated measures. We also describe how the same steps could be employed to perform a group sequential analysis based on any one of the variety of marginal multivariate methods that have been proposed. These methods are applied to a real-life example.

Biometry↗
PubMed1990

Implementation of group sequential logrank tests in a maximum duration trial.

To control the Type I error probability in a group sequential procedure using the logrank test, it is important to know the information times (fractions) at the times of interim analyses conducted for purposes of data monitoring. For the logrank test, the information time at an interim analysis is the fraction of the total number of events to be accrued in the entire trial. In a maximum information trial design, the trial is concluded when a prespecified total number of events has been accrued. For such a design, therefore, the information time at each interim analysis is known. However, many trials are designed to accrue data over a fixed duration of follow-up on a specified number of patients. This is termed a maximum duration trial design. Under such a design, the total number of events to be accrued is unknown at the time of an interim analysis. For a maximum duration trial design, therefore, these information times need to be estimated. A common practice is to assume that a fixed fraction of information will be accrued between any two consecutive interim analyses, and then employ a Pocock or O'Brien-Fleming boundary. In this article, we describe an estimate of the information time based on the fraction of total patient exposure, which tends to be slightly negatively biased (i.e., conservative) if survival is exponentially distributed. We then present a numerical exploration of the robustness of this estimate when nonexponential survival applies. We also show that the Lan-DeMets (1983, Biometrika 70, 659-663) procedure for constructing group sequential boundaries with the desired level of Type I error control can be computed using the estimated information fraction, even though it may be biased. Finally, we discuss the implications of employing a biased estimate of study information for a group sequential procedure.

Biometry↗
PubMed1988

Statistical properties of randomization in clinical trials.

This is the first of five articles on the properties of different randomization procedures used in clinical trials. This paper presents definitions and discussions of the statistical properties of randomization procedures as they relate to both the design of a clinical trial and the statistical analysis of trial results. The subsequent papers consider, respectively, the properties of simple (complete), permuted-block (i.e., blocked), and urn (adaptive biased-coin) randomization. The properties described herein are the probabilities of treatment imbalances and the potential effects on the power of statistical tests; the permutational basis for statistical tests; and the potential for experimental biases in the assessment of treatment effects due either to the predictability of the random allocations (selection bias) or the susceptibility of the randomization procedure to covariate imbalances (accidental bias). For most randomization procedures, the probabilities of overall treatment imbalances are readily computed, even when a stratified randomization is used. This is important because treatment imbalance may affect statistical power. It is shown, however, that treatment imbalance must be substantial before power is more than trivially affected. The differences between a population versus a permutation model as a basis for a statistical test are reviewed. It is argued that a population model can only be invoked in clinical trials as an untestable assumption, rather than being formally based on sampling at random from a population. On the other hand, a permutational analysis based on the randomization actually employed requires no assumptions regarding the origin of the samples of patients studied. The large sample permutational distribution of the family of linear rank tests is described as a basis for easily conducting a variety of permutation tests. Subgroup (stratified) analyses, analyses when some data are missing, and regression model analyses are also discussed. The Blackwell-Hodges model for selection bias in the composition of the study groups is described. The expected selection bias associated with a randomization procedure is a function of the predictability of the treatment allocations and is readily evaluated for any sequence of treatment assignments. In an unmasked study, the potential for selection bias may be substantial with highly predictable sequences. Finally, the Efron model for accidental bias in the estimate of treatment effect in a linear model is described. This is important because the potential for accidental bias is equivalent to the potential for a covariate imbalance.(ABSTRACT TRUNCATED AT 400 WORDS)

Clinical Trials as Topic↗
PubMed1988

Properties of simple randomization in clinical trials.

This article presents the properties of complete randomization (e.g., coin toss) and of the random allocation rule (random permutation of n/2 of n elements). The latter is principally used in cases where the total sample size n is known exactly a priori. The likelihood of treatment imbalances is readily computed and is shown to be negligible for large trials (n greater than 200), regardless of whether a stratified randomization is used. It is shown that substantial treatment imbalances are extremely unlikely in large trials, and therefore there is likely to be no substantial effect on power. The large-sample permutational distribution of the family of linear rank tests is presented for complete randomization unconditionally and conditionally, and for the random allocation rule. Asymptotically the three are equivalent to the distribution of these tests under a sampling-based population model. Permutation tests are also presented for a stratified analysis within one or more subgroups of patients defined post hoc on the basis of a covariate. This provides a basis for analysis when some patients' responses are assumed to be missing-at-random. Using the Blackwell-Hodges model, it is shown that complete randomization eliminates the potential for selection bias, but that the random allocation rule yields a substantial potential for selection bias in an unmasked trial. Finally, the Efron model for accidental bias is used to assess the potential for bias in the estimate of treatment effect due to covariate imbalance. Asymptotically, this probability approaches zero for complete randomization and for the random allocation rule. However, for finite n, complete randomization minimizes the probability of accidental bias, whereas this probability is slightly higher with a random allocation rule. It is concluded that complete randomization has merit in large clinical trials.

Clinical Trials as Topic↗
PubMed1988

Properties of permuted-block randomization in clinical trials.

This article describes some of the important statistical properties of the commonly used permuted-block design, also known simply as blocked-randomization. Under a permutation model for statistical tests, proper analyses should employ tests that incorporate the blocking used in the randomization. These include the block-stratified Mantel-Haenszel chi-square test for binary data, the blocked analysis of variance F test, and the blocked nonparametric linear rank test. It is common, however, to ignore the blocking in the analysis. For these tests, it is shown that the size of a test obtained from an analysis incorporating the blocking (say T), versus an analysis ignoring the blocking (say TI), is related to the intrablock correlation coefficient (R) as TI = T(1-R). For blocks of common length 2m, the range of R is from -1/(2m-1) to 1. Thus, if there is a positive intrablock correlation, which is more likely than not for m greater than 1, an analysis ignoring blocking will be unduly conservative. Permutation tests are also presented for the case of stratified analyses within one or more subgroups of patients defined post hoc on the basis of a covariate. This provides a basis for the analysis when responses from some patients are assumed to be missing-at-random. An alternative strategy that requires no assumptions is to perform the analysis using only the subset of complete blocks in which no observations are missing. The Blackwell-Hodges model is used to assess the potential for selection bias induced by investigator attempts to guess which treatment is more likely to be assigned to each incoming patient. In an unmasked trial, the permuted-block design provides substantial potential for selection bias in the comparison of treatments due to the predictability of the assignments that is induced by the requirement of balance within blocks. Further, this bias is not eliminated by the use of random block sizes. We also modify the Blackwell-Hodges model to allow for selection bias only when the investigator is able to discern the next assignment with certainty. This type of bias is reduced by the use of random block sizes and is eliminated only if the possible block sizes are unknown to the investigators. Finally, the Efron model for accidental bias is used to assess the potential for bias in the estimation of treatment effects due to covariate imbalances. For the permuted-block design, the variance of this bias approaches that of complete randomization as the half-block length m----infinity.(ABSTRACT TRUNCATED AT 400 WORDS)

Analysis of Variance↗
PubMed1988

Properties of the urn randomization in clinical trials.

In this article we review the important statistical properties of the urn randomization (design) for assigning patients to treatment groups in a clinical trial. The urn design is the most widely studied member of the family of adaptive biased-coin designs. Such designs are a compromise between designs that yield perfect balance in treatment assignments and complete randomization which eliminates experimental bias. The urn design forces a small-sized trial to be balanced but approaches complete randomization as the size of the trial (n) increases. Thus, the urn design is not as vulnerable to experimental bias as are other restricted randomization procedures. In a clinical trial it may be difficult to postulate that the study subjects constitute a random sample from a well-defined homogeneous population. In this case, a randomization model provides a preferred basis for statistical inference. We describe the large-sample permutational null distributions of linear rank statistics for testing the equality of treatment groups based on the urn design. In general, these permutation tests may be different from those based on the population model, which is equivalent to assuming complete randomization. Poststratified subgroup analyses can also be performed on the basis of the urn design permutational distribution. This provides a basis for analyzing the subset of patients with observed responses when some patients' responses can be assumed to be missing-at-random. For multiple mutually exclusive strata, these tests are correlated. For this case, a combined covariate-adjusted test of treatment effect is described. Finally, we show how to generalize the urn design to a prospectively stratified trial with a fairly large number of strata.

Clinical Trials as Topic↗
PubMed1988

Randomization in clinical trials: conclusions and recommendations.

The statistical properties of simple (complete) randomization, permuted-block (or simply blocked) randomization, and the urn adaptive biased-coin randomization are summarized. These procedures are contrasted to covariate adaptive procedures such as minimization and to response adaptive procedures such as the play-the-winner rule. General recommendations are offered regarding the use of complete, permuted-block, or urn randomization. In a large double-masked trial, any of these procedures may be acceptable. For a given trial, the relative merits of each procedure should be carefully weighed in relation to the characteristics of the trial. Important considerations are the size of the trial, overall as well as within the smallest subgroup to be employed in a subgroup-specific analysis, whether or not the trial is to be masked, and the resources needed to perform the proper randomization-based permutational analysis.

Clinical Trials as Topic↗
PubMed1988

Estimators and tests in the analysis of multiple nonindependent 2 x 2 tables with partially missing observations.

We present methods for the analysis of a K-variate binary measure for two independent groups where some observations may be incomplete, as in the case of K repeated measures in a comparative trial. For the K 2 X 2 tables, let theta = (theta 1,..., theta K) be a vector of association parameters where theta k is a measure of association that is a continuous function of the probabilities pi ik in each group (i = 1, 2; k = 1,..., K), such as the log odds ratio or log relative risk. The asymptotic distribution of the estimates theta = (theta 1,..., theta K) is derived. Under the assumption that theta k = theta for all k, we describe the maximally efficient linear estimator theta of the common parameter theta. Tests of contrasts on the theta are presented which provide a test of homogeneity Ha: theta k = theta l for all k not equal to l. We then present maximally efficient tests of aggregate association Hb: theta = theta 0, where theta 0 is a given value. It is shown that the test of aggregate association Hb is asymptotically independent of the preliminary test of homogeneity Ha. These methods generalize the efficient estimators of Gart (1962, Biometrics 18, 601-610), and the Cochran (1954, Biometrics 10, 417-451), Mantel-Haenszel (1959, Journal of the National Cancer Institute 22, 719-748), and Radhakrishna (1965, Biometrics 21, 86-98) tests to nonindependent tables. The methods are illustrated with an analysis of repeated morphologic evaluations of liver biopsies obtained in the National Cooperative Gallstone Study.

Analysis of Variance↗
PubMed1987

External monitoring of a data coordinating center: experience of the National Cooperative Gallstone Study.

A Biostatistical Monitoring Committee was established to review periodically the procedures and performance of the data coordinating center of the National Cooperative Gallstone Study. The functions of this committee, the types of data coordinating center activities reviewed, the manner in which monitoring of these activities was carried out, and an assessment of the value of this committee to the study are discussed in this article.

Chenodeoxycholic Acid↗
PubMed1986

Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification.

When designing a clinical trial to test the equality of survival distributions for two treatment groups, the usual assumptions are exponential survival, uniform patient entry, full compliance, and censoring only administratively at the end of the trial. Various authors have presented methods for estimation of sample size or power under these assumptions, some of which allow for an R-year accrual period with T total years of study, T greater than R. The method of Lachin (1981, Controlled Clinical Trials 2, 93-113) is extended to allow for cases where patients enter the trial in a nonuniform manner over time, patients may exit from the trial due to loss to follow-up (other than administrative), other patients may continue follow-up although failing to comply with the treatment regimen, and a stratified analysis may be planned according to one or more prognostic covariates.

Analysis of Variance↗
PubMed1985

Mass spectrometry identification of biliary bile acids in bile from patients with gallstones before and during treatment with chenodeoxycholic acid. An ancillary study of the National Cooperative Gallstone Study.

The chemical structure of individual bile acids in 255 duodenal bile samples obtained from patients with radiolucent gallstones before and during treatment with chenodeoxycholic acid (375 or 750 mg/day) was determined by coupled gas chromatography/mass spectrometry. The two primary bile acids, cholic acid and chenodeoxycholic acid, and their metabolic products, deoxycholic acid, lithocholic acid, and ursodeoxycholic acid, were present in all bile samples and constituted greater than 97% of all bile acids. In pretreatment samples, the 12-oxo derivative of deoxycholic acid (3 alpha-hydroxy-12-oxo-cholanoic acid) was the next most abundant bile acid, being present in 62% of the samples; the average concentration was 1%, but three individuals had 6% to 8% of this bile acid. The 7-oxo derivative of chenodeoxycholic acid was also present in the majority of samples, but at a lower proportion (0.3%); five individuals had 2% to 3%. The 7-oxo derivative of cholic acid was present in a minority of samples (37%) in trace concentrations; isodeoxycholic acid and the 3-oxo derivatives of chenodeoxycholic acid and deoxycholic acid were also present in trace amounts. Four patients had 1% to 11% ursocholic acid in bile. During treatment with chenodeoxycholic acid, bile became enriched in it in direct relation to dosage; the concentration of its bacterial metabolites increased, and the proportion of cholic acid and its bacterial metabolites showed a reciprocal decrease. No unusual bile acids appeared, indicating that treatment with these doses of chenodeoxycholic acid does not result in the occurrence of additional uncommon bile acids in bile. It is suggested that the paucity of uncommon bile acids in bile, which contrasts strikingly with the great variety of uncommon bile acids known to be present in urine and feces, is the result of two factors: (1) the conversion of uncommon bile acids to common bile acids by reduction, hydroxylation, or epimerization during hepatic passage; and (2) efficient hepatic transport of common but not uncommon bile acids into bile.

Bile↗
PubMed1977

Feasibility of low-dose and intermittent chenodeoxycholic acid therapy of gallstones.

Chenodeoxycholic acid, by reducing the concentration of biliary cholesterol relative to that of bile acid and phospholipid, dissolves cholesterol gallstones. This bile acid, however, has potential dose-related hepatotoxicity and causes dose-related diarrhea. Therefore, the feasibility of low-dose and intermittent therapy was assessed by studying the induction and persistence of chenodeoxycholic acid-induced biliary lipid changes. Biliary lipid composition with each of 3 doses of chenodeoxycholic acid was determined in bile samples obtained by cholecystokinin-stimulated duodenal drainage before, after one week and one month of treatment, and up to 9 weeks after discontinuation of treatment. The lowest dose that significantly reduced the relative concentration of biliary cholesterol was 250 mg/day. A significant reduction occurred one week after initiation of treatment and was maintained for 9 weeks following discontinuation of treatment. Thus, clinical trials on low-dose and intermittent chenodeoxycholic acid therapy for gallstone prophylaxis or dissolution are warranted.

Adult↗