We help you to develop and implement a clear plan of action from the outset of your project to ensure you remain on your optimal trajectory for the project duration. 

In the initial stages we refine your research questions, fine-tune your hypotheses for precise testing and enable a statistical analysis plan that optimizes your study results. Study power and the required sample size can vary wildly as a function of statistical test and data type. Determining a statistical analysis plan upfront is key as it enables us to calculate the most appropriate sample size and power for your study. 

 

We advise on data collection and inputting techniques that reduce the need for data cleaning and preparation at the analysis stage. This saves you time and money and allows you to more efficiently answer your research questions.

 

We use a rigorous approach to ensure the most appropriate statistical methodology has been implemented at the final analysis stage. We will fine-tune your statistical analysis plan as needed in line with any unexpected developments arising from the nuances of real-world data. We follow protocol to ensure your final analysis is as accurate and free from bias to every possible extent. We then write up the results of your study and guide your interpretation to fully ensure that any conclusions are correct and concise.

 

 

Biostatistics & Chemical Data Analysis

 

Our biostatistical consulting and reporting services are individually tailored to suit the project at hand and can cover a broad range of approaches. We can theoretically enter into collaboration at any stage of your research, whether it be the design stage, data collection, statistical analysis, reporting and preparing for presentations. Most commonly a statistical consulting project will cover one or more of the following areas:

 

Study Design and Planning 

 

Prior to the collection of data, a suitable study design should be determined, including calculation of the appropriate sample size for a desired power or vice versa. Having a clearly outlined plan for your research can make the later stages of analysis much easier. We can also prepare randomisation schedules in preparation for patient allocation to treatment or study groups. Common study designs include pilot studies, randomized controlled trials, epidemiological studies such as retrospective and prospective cohort, case-control and cross-sectional studies.

 

Data Collection and Cleaning

 

We can assist you to determine, in advance, the most beneficial format to collect your data for the purposes of addressing your research questions. Having research questions clearly set out in advance of data collection allows the data collection process to be that much more efficient, as data is in an optimal format from the outset. This reduces the amount of cleaning and transformation of the data file, reducing costs and making our job a little less tedious.

 

 

Statistical Reporting

 

Once all analysis has been completed, a summary interpreting the results in plain English will be provided as well as a complete statistical report, including results and conclusions, in line with conventional (APA) standards, or other preferred standards. Typically we will first send through to you a standard statistical report with results and any visuals but without the bells and whistles. Once you are happy with the contents it will then be written up to publication standards or even made into a PowerPoint presentation as per your requirements.

The finished product will typically include a statistical report outlining the results of the analyses and any tables, graphs and other visualisations. We are also happy to include any programming code used to perform the analyses and all output produced, as required. As often is the case many clients prefer to remain liberated of these finer details, therefore include these components upon your request. 

 

Subscale for Biostatisticians

 

Survival analysis

Analyze duration outcomes—outcomes measuring the time to an event such as failure or death—using Stata's specialized tools for survival analysis. Account for the complications inherent in survival data, such as sometimes not observing the event (censoring), individuals entering the study at differing times (delayed entry), and individuals who are not continuously observed throughout the study (gaps). You can estimate and plot the probability of survival over time. Or model survival as a function of covariates using Cox, Weibull, lognormal, and other regression models. Predict hazard ratios, mean survival time, and survival probabilities. Do you have groups of individuals in your study? Adjust for within-group correlation with a random-effects or shared frailty model.

 

Multilevel mixed-effects models

Whether the groupings in your data arise in a nested fashion (patients nested in clinics and clinics nested in regions) or in a nonnested fashion (regions crossed with occupations), you can fit a multilevel model to account for the lack of independence within these groups. Fit models for continuous, binary, count, ordinal, and survival outcomes. Estimate variances of random intercepts and random coefficients. Compute intraclass correlations. Predict random effects. Estimate relationships that are population averaged over the random effects.

 

Bayesian analysis

Fit Bayesian regression models using one of the Markov chain Monte Carlo (MCMC) methods. You can choose from a variety of supported models or even program your own. Extensive tools are available to check convergence, including multiple chains. Compute posterior mean estimates and credible intervals for model parameters and functions of model parameters. You can perform both interval- and model-based hypothesis testing. Compare models using Bayes factors. Compute model fit using posterior predictive values. Generate predictions.

 

Power, precision, and sample size

Before you conduct your experiment, determine the sample size needed to detect meaningful effects without wasting resources. Do you intend to compute CIs for means or variances, or perform tests for proportions or correlations? Do you plan to fit a Cox proportional-hazards model or compare survivor functions using a log-rank test? Do you want to use a Cochran--Mantel--Haenszel test of association or a Cochran--Armitage trend test? Use Stata's power command to compute power and sample size, create customized tables, and automatically graph the relationships between power, sample size, and effect size for your planned study. Or, use the ciwidth command to do the same but for CIs instead of hypothesis tests by computing the required sample size for the desired CI precision. Instead of commands, use the interactive Control Panel to perform your analysis.

 

Linear, binary, and count regressions

Fit classical ANOVA and linear regression models of the relationship between a continuous outcome, such as weight, and the determinants of weight, such as height, diet, and level of exercise. If your response is binary, ordinal, categorical, or count, don't worry. Stata has estimators for these types of outcomes too. Use logistic regression to estimate odds ratios. Estimate incidence rates using a Poisson model. Analyze matched case–control data with conditional logistic regression. A vast array of tools is available after fitting such models. Predict outcomes and their confidence intervals. Test equality of parameters. Compute linear and nonlinear combinations of parameters.

 

Meta-analysis

Combine results of multiple studies to estimate an overall effect. Use forest plots to visualize results. Use subgroup analysis and meta-regression to explore study heterogeneity. Use funnel plots and formal tests to explore publication bias and small-study effects. Use trim-and-fill analysis to assess the impact of publication bias on results. Perform cumulative meta-analysis. Use the meta suite, or let the Control Panel interface guide you through your entire meta-analysis.

 

Multiple imputation

Account for missing data in your sample using multiple imputation. Choose from univariate and multivariate methods to impute missing values in continuous, censored, truncated, binary, ordinal, categorical, and count variables. Then, in a single step, estimate parameters using the imputed datasets, and combine results. Fit a linear model, logit model, Poisson model, hierarchical model, survival model, or one of the many other supported models. Use the mi command, or let the Control Panel interface guide you through your entire MI analysis.

 

Marginal means, contrasts, and interactions

Marginal means and contrasts let you analyze the relationships between your outcome variable and your covariates, even when that outcome is binary, count, ordinal, categorical, or survival. Compute adjusted predictions with covariates set to interesting or representative values. Or compute marginal means for each level of a categorical covariate. Make comparisons of the adjusted predictions or marginal means using contrasts. If you have multilevel data and random effects, these effects are automatically integrated out to provide marginal (that is, population-averaged) estimates. After fitting almost any model in Stata, analyze the effect of covariate interactions, and easily create plots to visualize those interactions.

 

Causal inference

Estimate experimental-style causal effects from observational data. With Stata's treatment-effect estimators, we can use a potential-outcomes (counterfactuals) framework to estimate, for instance, the effect of the mother smoking on the baby's birthweight or the effect of a drug on survival time after a heart attack. Fit models for continuous, binary, count, fractional, and survival outcomes with binary or multivalued treatments using inverse-probability weighting (IPW), propensity-score matching, nearest-neighbor matching, regression adjustment, or doubly robust estimators. If the assignment to a treatment is not independent of the outcome, you can use an endogenous treatment-effects estimator.

 

Epidemiological tables

Want to analyze data from a prospective (incidence) study, cohort study, case–control study, or matched case–control study? Stata's tables for epidemiologists make it easy to summarize your data and compute statistics such as incidence-rate ratios, incidence-rate differences, risk ratios, risk differences, odds ratios, and attributable fractions. You can analyze stratified data too—compute Mantel–Haenszel combined estimates, perform tests of homogeneity, and standardize estimates. If you have an ordinal rather than binary exposure, you can perform a test for a trend.

 

Programming

Want to program your own commands to perform estimation, perform data management, or implement other new features? Stata is programmable, and thousands of Stata users have implemented and published thousands of community-contributed commands. These commands look and act just like official Stata commands and are easily installed for free over the Internet from within Stata. A unique feature of Stata's programming environment is Mata, a fast and compiled language with support for matrix types. Of course, it has all the advanced matrix operations you need. It also has access to the power of LAPACK. What's more, it has built-in solvers and optimizers to make implementing your own maximum likelihood, GMM, or other estimators easier. And you can leverage all of Stata's estimation and other features from within Mata. Many of Stata's official commands are themselves implemented in Mata.

 

Python integration

Interact Stata code with Python code. You can interchange data between Stata and Python and pass results from Python back to Stata. You can call Python libraries such as NumPy, matplotlib, Scrapy, scikit-learn, and more from Stata.

 

Survey methods

Whether your data require a simple weighted adjustment because of differential sampling rates or you have data from a complex multistage survey, Stata's survey features can provide you with correct standard errors and confidence intervals for your inferences. Simply specify the relevant characteristics of your sampling design, such as sampling weights (including weights at multiple stages), clustering (at one, two, or more stages), stratification, and poststratification. After that, most of Stata's estimation commands can adjust their estimates to correct for your sampling design.

 

Automated reporting and dynamic document generation

Stata is designed for reproducible research, including the ability to create dynamic documents incorporating your analysis results. Create Word or PDF files, populate Excel worksheets with results and format them to your liking, and mix Markdown, HTML, Stata results, and Stata graphs, all from within Stata.