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John M Lachin

Publications and source records attributed to John M Lachin.

At least 19 recordsLinked to original sources
PubMed2006

Glycemic durability of rosiglitazone, metformin, or glyburide monotherapy.

BACKGROUND: The efficacy of thiazolidinediones, as compared with other oral glucose-lowering medications, in maintaining long-term glycemic control in type 2 diabetes is not known. METHODS: We evaluated rosiglitazone, metformin, and glyburide as initial treatment for recently diagnosed type 2 diabetes in a double-blind, randomized, controlled clinical trial involving 4360 patients. The patients were treated for a median of 4.0 years. The primary outcome was the time to monotherapy failure, which was defined as a confirmed level of fasting plasma glucose of more than 180 mg per deciliter (10.0 mmol per liter), for rosiglitazone, as compared with metformin or glyburide. Prespecified secondary outcomes were levels of fasting plasma glucose and glycated hemoglobin, insulin sensitivity, and beta-cell function. RESULTS: Kaplan-Meier analysis showed a cumulative incidence of monotherapy failure at 5 years of 15% with rosiglitazone, 21% with metformin, and 34% with glyburide. This represents a risk reduction of 32% for rosiglitazone, as compared with metformin, and 63%, as compared with glyburide (P<0.001 for both comparisons). The difference in the durability of the treatment effect was greater between rosiglitazone and glyburide than between rosiglitazone and metformin. Glyburide was associated with a lower risk of cardiovascular events (including congestive heart failure) than was rosiglitazone (P<0.05), and the risk associated with metformin was similar to that with rosiglitazone. Rosiglitazone was associated with more weight gain and edema than either metformin or glyburide but with fewer gastrointestinal events than metformin and with less hypoglycemia than glyburide (P<0.001 for all comparisons). CONCLUSIONS: The potential risks and benefits, the profile of adverse events, and the costs of these three drugs should all be considered to help inform the choice of pharmacotherapy for patients with type 2 diabetes. (ClinicalTrials.gov number, NCT00279045 [ClinicalTrials.gov].).

Cardiovascular Diseases↗
PubMed2006

Operating characteristics of sample size re-estimation with futility stopping based on conditional power.

Various methods have been described for re-estimating the final sample size in a clinical trial based on an interim assessment of the treatment effect. Many re-weight the observations after re-sizing so as to control the pursuant inflation in the type I error probability alpha. Lan and Trost (Estimation of parameters and sample size re-estimation. Proceedings of the American Statistical Association Biopharmaceutical Section 1997; 48-51) proposed a simple procedure based on conditional power calculated under the current trend in the data (CPT). The study is terminated for futility if CPT < or = CL, continued unchanged if CPT > or = CU, or re-sized by a factor m to yield CPT = CU if CL < CPT < CU, where CL and CU are pre-specified probability levels. The overall level alpha can be preserved since the reduction due to stopping for futility can balance the inflation due to sample size re-estimation, thus permitting any form of final analysis with no re-weighting. Herein the statistical properties of this approach are described including an evaluation of the probabilities of stopping for futility or re-sizing, the distribution of the re-sizing factor m, and the unconditional type I and II error probabilities alpha and beta. Since futility stopping does not allow a type I error but commits a type II error, then as the probability of stopping for futility increases, alpha decreases and beta increases. An iterative procedure is described for choice of the critical test value and the futility stopping boundary so as to ensure that specified alpha and beta are obtained. However, inflation in beta is controlled by reducing the probability of futility stopping, that in turn dramatically increases the possible re-sizing factor m. The procedure is also generalized to limit the maximum sample size inflation factor, such as at m max = 4. However, doing so then allows for a non-trivial fraction of studies to be re-sized at this level that still have low conditional power. These properties also apply to other methods for sample size re-estimation with a provision for stopping for futility. Sample size re-estimation procedures should be used with caution and the impact on the overall type II error probability should be assessed.

Clinical Trials as Topic↗
PubMed2006

Parametric survival models for interval-censored data with time-dependent covariates.

We present a parametric family of regression models for interval-censored event-time (survival) data that accomodates both fixed (e.g. baseline) and time-dependent covariates. The model employs a three-parameter family of survival distributions that includes the Weibull, negative binomial, and log-logistic distributions as special cases, and can be applied to data with left, right, interval, or non-censored event times. Standard methods, such as Newton-Raphson, can be employed to estimate the model and the resulting estimates have an asymptotically normal distribution about the true values with a covariance matrix that is consistently estimated by the information function. The deviance function is described to assess model fit and a robust sandwich estimate of the covariance may also be employed to provide asymptotically robust inferences when the model assumptions do not apply. Spline functions may also be employed to allow for non-linear covariates. The model is applied to data from a long-term study of type 1 diabetes to describe the effects of longitudinal measures of glycemia (HbA1c) over time (the time-dependent covariate) on the risk of progression of diabetic retinopathy (eye disease), an interval-censored event-time outcome.

Biometry↗
PubMed2006

Obesity is a major determinant of the association of C-reactive protein levels and the metabolic syndrome in type 2 diabetes.

The inflammatory factor C-reactive protein (CRP) and the fibrinolytic variables fibrinogen and plasminogen activator-1 (PAI-1) are associated with long-term cardiovascular morbidity. To determine the contribution of body adiposity (BMI), insulin sensitivity (homeostasis model assessment of insulin resistance [HOMA-IR], and glycemia (HbA(1c) [A1C]) to the levels of these inflammatory and fibrinolytic variables in recently diagnosed (<or=3 years), drug-naive, type 2 diabetic subjects (fasting plasma glucose <or=10 mmol/l), we examined a representative subgroup (n = 921) of the U.S. cohort in ADOPT (A Diabetes Outcome Progression Trial). The relationship between levels of CRP, fibrinogen, PAI-1 antigen and PAI-1 activity, and baseline variables including National Cholesterol Education Program Adult Treatment Panel III metabolic syndrome phenotype were explored. All four factors increased significantly with increasing numbers of metabolic syndrome components (P = 0.0136 to P < 0.0001). BMI (P < 0.0001) and HOMA-IR (P < 0.0001) but not A1C (P = 0.65) increased with increasing numbers of metabolic syndrome components. Adjustment of CRP levels for BMI eliminated the association between CRP and the number of metabolic syndrome components, while adjusting for HOMA-IR did not (P = 0.0028). The relationships of PAI-1 antigen and PAI-1 activity with the number of metabolic syndrome components were maintained after adjusting for BMI (P = 0.0002 and P = <0.0001, respectively) or HOMA-IR (P = 0.0008 and P = <0.0001, respectively), whereas that with fibrinogen was eliminated after adjusting for BMI but not after adjusting for HOMA-IR (P = 0.013). Adjustment for A1C had no effect on any of the relationships between the inflammatory and fibrinolytic factors and the metabolic syndrome. We conclude that in recently diagnosed, drug-naive type 2 diabetic subjects, markers of inflammation and fibrinolysis are strongly related to the number of metabolic syndrome components. Further, for CRP and fibrinogen this relationship is determined by body adiposity and not by insulin sensitivity or glucose control.

Adiposity↗
PubMed2006

The effect of intensive glycemic treatment on coronary artery calcification in type 1 diabetic participants of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study.

The Epidemiology of Diabetes Interventions and Complications (EDIC) study, an observational follow-up of the Diabetes Control and Complications Trial (DCCT) type 1 diabetes cohort, measured coronary artery calcification (CAC), an index of atherosclerosis, with computed tomography (CT) in 1,205 EDIC patients at approximately 7-9 years after the end of the DCCT. We examined the influence of the 6.5 years of prior conventional versus intensive diabetes treatment during the DCCT, as well as the effects of cardiovascular disease risk factors, on CAC. The prevalences of CAC >0 and >200 Agatston units were 31.0 and 8.5%, respectively. Compared with the conventional treatment group, the intensive group had significantly lower geometric mean CAC scores and a lower prevalence of CAC >0 in the primary retinopathy prevention cohort, but not in the secondary intervention cohort, and a lower prevalence of CAC >200 in the combined cohorts. Waist-to-hip ratio, smoking, hypertension, and hypercholesterolemia, before or at the time of CT, were significantly associated with CAC in univariate and multivariate analyses. CAC was associated with mean HbA(1c) (A1C) levels before enrollment, during the DCCT, and during the EDIC study. Prior intensive diabetes treatment during the DCCT was associated with less atherosclerosis, largely because of reduced levels of A1C during the DCCT.

Adult↗
PubMed2006

Effect of weight loss with lifestyle intervention on risk of diabetes.

OBJECTIVE: Diabetes Prevention Program (DPP) participants randomized to the intensive lifestyle intervention (ILS) had significantly reduced risk of diabetes compared with placebo participants. We explored the contribution of changes in weight, diet, and physical activity on the risk of developing diabetes among ILS participants. RESEARCH DESIGN AND METHODS: For this study, we analyzed one arm of a randomized trial using Cox proportional hazards regression over 3.2 years of follow-up. RESULTS: A total of 1,079 participants were aged 25-84 years (mean 50.6 years, BMI 33.9 kg/m(2)). Weight loss was the dominant predictor of reduced diabetes incidence (hazard ratio per 5-kg weight loss 0.42 [95% CI 0.35-0.51]; P < 0.0001). For every kilogram of weight loss, there was a 16% reduction in risk, adjusted for changes in diet and activity. Lower percent of calories from fat and increased physical activity predicted weight loss. Increased physical activity was important to help sustain weight loss. Among 495 participants not meeting the weight loss goal at year 1, those who achieved the physical activity goal had 44% lower diabetes incidence. CONCLUSIONS: Interventions to reduce diabetes risk should primarily target weight reduction.

Adult↗
PubMed2005

Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes.

BACKGROUND: Intensive diabetes therapy aimed at achieving near normoglycemia reduces the risk of microvascular and neurologic complications of type 1 diabetes. We studied whether the use of intensive therapy as compared with conventional therapy during the Diabetes Control and Complications Trial (DCCT) affected the long-term incidence of cardiovascular disease. METHODS: The DCCT randomly assigned 1441 patients with type 1 diabetes to intensive or conventional therapy, treating them for a mean of 6.5 years between 1983 and 1993. Ninety-three percent were subsequently followed until February 1, 2005, during the observational Epidemiology of Diabetes Interventions and Complications study. Cardiovascular disease (defined as nonfatal myocardial infarction, stroke, death from cardiovascular disease, confirmed angina, or the need for coronary-artery revascularization) was assessed with standardized measures and classified by an independent committee. RESULTS: During the mean 17 years of follow-up, 46 cardiovascular disease events occurred in 31 patients who had received intensive treatment in the DCCT, as compared with 98 events in 52 patients who had received conventional treatment. Intensive treatment reduced the risk of any cardiovascular disease event by 42 percent (95 percent confidence interval, 9 to 63 percent; P=0.02) and the risk of nonfatal myocardial infarction, stroke, or death from cardiovascular disease by 57 percent (95 percent confidence interval, 12 to 79 percent; P=0.02). The decrease in glycosylated hemoglobin values during the DCCT was significantly associated with most of the positive effects of intensive treatment on the risk of cardiovascular disease. Microalbuminuria and albuminuria were associated with a significant increase in the risk of cardiovascular disease, but differences between treatment groups remained significant (P< or =0.05) after adjusting for these factors. CONCLUSIONS: Intensive diabetes therapy has long-term beneficial effects on the risk of cardiovascular disease in patients with type 1 diabetes.

Adolescent↗
PubMed2005

A review of methods for futility stopping based on conditional power.

Conditional power (CP) is the probability that the final study result will be statistically significant, given the data observed thus far and a specific assumption about the pattern of the data to be observed in the remainder of the study, such as assuming the original design effect, or the effect estimated from the current data, or under the null hypothesis. In many clinical trials, a CP computation at a pre-specified point in the study, such as mid-way, is used as the basis for early termination for futility when there is little evidence of a beneficial effect. Brownian motion can be used to describe the distribution of the interim Z-test value, the corresponding B-value, and the CP values under a specific assumption about the future data. A stopping boundary on the CP value specifies an equivalent boundary on the B-value from which the probability of stopping for futility can then be computed based on the planned study design (sample size and duration) and the assumed true effect size. This yields expressions for the total type I and II error probabilities. As the probability of stopping increases, the probability of a type I error alpha decreases from the nominal desired level (e.g. 0.05) while the probability of a type II error beta increases from the level specified in the study design. Thus a stopping boundary on the B-value can be determined such that the inflation in type II error probability is controlled at a desired level. An iterative procedure is also described that determines a stopping boundary on the B-value and a final test critical Z-value with specified type I and II error probabilities. The implementation in conjunction with a group sequential analysis for effectiveness is also described.

Biometry↗
PubMed2005

Maximum information designs.

BACKGROUND: Expressions to determine the sample size N needed to provide power 1-beta to detect a difference between groups, say delta, involve other nuisance parameters, such as the variance of the observations for a test or means, or the control group probability for a test of proportions or the control group hazard rate for a logrank test of event-times. Designs where N is fixed are called maximum N or duration designs because the sample size and required duration of the study can be fixed, or estimated. However, such designs are expected, but not guaranteed, to provide the desired level of power to detect the specified difference delta at the study end because the true or estimated values of the nuisance parameters are unknown. Thus, the actual information to be accrued and the associated level of power are random variables with sample variation. METHODS: Expressions are developed to determine the amount of information needed to provide the desired level of power, regardless of the values of the nuisance parameters. RESULTS: The amount of information (I) in the observed data is readily quantified and can often be expressed as the inverse of the variance of the test statistic. Also, the total amount of information required to provide the desired level of power 1-beta to detect a difference delta with a test at level alpha is readily determined, designated as Ialpha,beta,delta. Under a maximum information design, the study is continued until the required total amount of information is accrued, or I = Ialpha,beta,delta. In this case, the sample size or duration are random variables, but each can be estimated under various projections about the underlying parameters (variance, probability, hazard) in advance. The implementation of a maximum information design for two and multiple group trials is described for a test of means, proportions and event-times using the logrank test. Application to other methods of analysis is described. CONCLUSIONS: A maximum information design provides greater assurance that the desired level of power will be attained. However, the exact study duration is unknown. Issues related to the implementation of such a design are discussed.

Clinical Trials as Topic↗
PubMed2004

Conflicts of interest in data monitoring of industry versus publicly financed clinical trials.

The FDA Guidance, while highly appropriate for industry sponsored trials, need not be imposed on publicly (e.g. NIH) financed clinical trials. While the potential for conflicts of interest exist in the latter, they are in general manageable and pose an acceptable low risk of threatening the integrity of a study. However, the Guidance should heighten the awareness of all investigators in public trials to the potential for the appearance of conflicts and to the need for full and open participation in the management of dualities of interest.

Clinical Trials Data Monitoring Committees↗
PubMed2004

The role of measurement reliability in clinical trials.

One of the principal characteristics of an outcome measure in a clinical trial, and any measurement in general, is its reliability. Reliability refers to the reproducibility of the measurement when repeated at random in the same subject or specimen. Reliability is often confused with validity, which refers to the extent to which the variable properly measures the underlying trait of interest. The coefficient of reliability is an estimate of the proportion of all variation that is not due to measurement error and is readily estimated from replicate measurements. The reliability of a measurement determines its maximal correlation or R2 and slope (or effect size) in regression models, its sensitivity and specificity when used for classifications or predictions, and the power of a statistical test employing the measurement. All decline as the reliability of the measure declines. The reliability of a measurement is an important consideration in the choice of the primary outcome measure for a clinical trial and in the choice of measures used for assessment of eligibility and exclusion. Reliability of measures should be assessed and assured by a quality control program based on randomly selected duplicate assessments. Just as the power of a study is reported in a final publication, so also should the reliability of the outcome and eligibility measurements so as to allow the authors to better describe, and readers to better understand, the sources of imprecision in study results, and those who follow to improve the design of future trials.

Clinical Trials as Topic↗
PubMed2004

C-peptide is the appropriate outcome measure for type 1 diabetes clinical trials to preserve beta-cell function: report of an ADA workshop, 21-22 October 2001.

The underlying cause of type 1 diabetes, loss of beta-cell function, has become the therapeutic target for a number of interventions in patients with type 1 diabetes. Even though insulin therapies continue to improve, it remains difficult to achieve normal glycemic control in type 1 diabetes, especially long term. The associated risks of hypoglycemia and end-organ diabetic complications remain. Retention of beta-cell function in patients with type 1 diabetes is known to result in improved glycemic control and reduced hypoglycemia, retinopathy, and nephropathy. To facilitate the development of therapies aimed at altering the type 1 diabetes disease process, an American Diabetes Association workshop was convened to identify appropriate efficacy outcome measures in type 1 diabetes clinical trials. The following consensus emerged: While measurements of immune responses to islet cells are important in elucidating pathogenesis, none of these measures have directly correlated with the decline in endogenous insulin secretion. HbA(1c) is a highly valuable clinical measure of glycemic control, but it is an insensitive measure of beta-cell function, particularly with the currently accepted standard of near-normal glycemic control. Rates of severe hypoglycemia and diabetic complications ultimately will be improved by therapies that are effective at preserving beta-cell function but as primary outcomes require inordinately large and protracted trials. Endogenous insulin secretion is assessed best by measurement of C-peptide, which is cosecreted with insulin in a one-to-one molar ratio but unlike insulin experiences little first pass clearance by the liver. Measurement of C-peptide under standardized conditions provides a sensitive, well accepted, and clinically validated assessment of beta-cell function. C-peptide measurement is the most suitable primary outcome for clinical trials of therapies aimed at preserving or improving endogenous insulin secretion in type 1 diabetes patients. Available data demonstrate that even relatively modest treatment effects on C-peptide will result in clinically meaningful benefits. The development of therapies for addressing this important unmet clinical need will be facilitated by trials that are carefully designed with beta-cell function as determined by C-peptide measurement as the primary efficacy outcome.

C-Peptide↗
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

Group sequential large sample T2-like chi2 tests for multivariate observations.

In many studies, a K degree of freedom large sample chi2 test is used to assess the effect of treatment on a multivariate response, such as an omnibus T2-like test of a difference between two treatment groups in any of K repeated measures. Alternately, a K df chi2 test may be used to test the equality of K+1 groups in a single outcome measure. Jennison and Turnbull (Biometrika 1991; 78: 133-141) describe group sequential chi2 and F-tests for normal errors linear models, and Proschan, Follmann and Geller (Statist. Med. 1994; 13: 1441-1452) describe group sequential tests for K+1 group comparisons. These methods apply to sequences of statistics that can be characterized as having an independent increments variance-covariance structure, thus simplifying the computation of the sequential variance-covariance matrix and the resulting sequential test boundaries. However, many commonly used statistics do not share this structure, including a Liang-Zeger (Biometrika 1986; 73: 13-22) GEE longitudinal analysis with an independence working correlation structure and a Wei-Lachin (J. Amer. Statist. Assoc. 1984; 79: 653-661) multivariate Wilcoxon rank test, among others. For such analyses, this paper describes the computation of group sequential boundaries for the interim analysis of emerging results using K df tests that are expressed as quadratic forms in a statistics vector that is distributed as multivariate normal, at least asymptotically. We derive the elements of the covariance matrix of multiple successive K df chi2 statistics based on established theorems on the distribution of quadratic forms. This covariance matrix is estimated by augmenting the data from the successive interim analyses into a single analysis from which the component sequential tests and their variance-covariance matrix can then be extracted. Boundary values for the sequential statistics can then be computed using the method of Slud and Wei (J. Amer. Statist. Assoc. 1982; 77: 862-868) or using the alpha-spending function of Lan and DeMets (Biometrika 1983; 70: 659-663) with a surrogate measure of information. An example is presented using the analysis of repeated cholesterol measurements in a clinical trial.

Biometry↗
PubMed2003

Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program.

OBJECTIVE: To describe the costs of the Diabetes Prevention Program (DPP) interventions to prevent or delay type 2 diabetes. RESEARCH DESIGN AND METHODS: We describe the direct medical costs, direct nonmedical costs, and indirect costs of the placebo, metformin, and intensive lifestyle interventions over the 3-year study period of the DPP. Resource use and cost are summarized from the perspective of a large health system and society. Research costs are excluded. RESULTS: The direct medical cost of laboratory tests to identify one subject with impaired glucose tolerance (IGT) was $139. Over 3 years, the direct medical costs of the interventions were $79 per participant in the placebo group, $2,542 in the metformin group, and $2,780 in the lifestyle group. The direct medical costs of care outside the DPP were $272 less per participant in the metformin group and $432 less in the lifestyle group compared with the placebo group. Direct nonmedical costs were $9 less per participant in the metformin group and $1,445 greater in the lifestyle group compared with the placebo group. Indirect costs were $230 greater per participant in the metformin group and $174 less in the lifestyle group compared with the placebo group. From the perspective of a health system, the cost of the metformin intervention relative to the placebo intervention was $2,191 per participant and the cost of the lifestyle intervention was $2,269 per participant over 3 years. From the perspective of society, the cost of the metformin intervention relative to the placebo intervention was $2,412 per participant and the cost of the lifestyle intervention was $3,540 per participant over 3 years. CONCLUSIONS: The metformin and lifestyle interventions are associated with modest incremental costs compared with the placebo intervention. The evaluation of costs relative to health benefits will determine the value of these interventions to health systems and society.

Diabetes Mellitus, Type 2↗
PubMed2002

Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

BACKGROUND: Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors--elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle--are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes. METHODS: We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups. RESULTS: The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin. CONCLUSIONS: Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.

Adult↗