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Data extraction is an important process whereby data identified by a systematic review are extracted and prepared for meta-analysis. This is often not straightforward. This resource is designed to help you make sense of it all and avoid some common pitfalls.

Introduction

Do you find data extraction in meta-analysis frustrating: A new blog series offering tips for data extraction

Extracting diagnostic accuracy outcomes

What can I do if the 2×2 table in a diagnostic accuracy study is not reported?

What if the prevalence is also not reported?

What if the sensitivity or specificity is also not reported?

What if neither sensitivity nor specificity are reported?

Extracting dichotomous outcomes

What can you do when prognostic studies report measures of risk on different scales?

What can you do when a study reports a beta coefficient instead of a hazard ratio?

How do you calculate a standard error of a beta coefficient?

How can categorical risk data be pooled?

A worked example using a trend estimation method to summarise categorical risk data

Wanting a particular reference category in categorical risk data

What if something is missing from categorical risk data?

Estimating a hazard ratio from time-to-event data

Estimating a hazard ratio from a Kaplan curve and information about follow-up

Estimating a hazard ratio from a Kaplan curve and numbers at risk

Extracting continuous outcomes

What should I do if I have a missing mean, standard deviation or sample size?

What if the data I want are reported for the wrong time point?

What if the summary statistic I want is given for the wrong group?

Obtaining summary statistics by using complementary equations

What if you’re missing a mean and only a similar statistical statistic is given?

What if you’re missing a standard deviation and only a similar summary statistic is given?

What if neither the summary statistic I want nor a similar statistic are reported?

What if the study only reports an effect estimate?

Other approaches to dealing with diverse continuous outcome data

General issues

Logarithms and log-transformations

How can you make data extraction more efficient?

How can you reduce the risk of errors and bias when extracting data?

What if there’s only a graph?

Extracting graphical data in fewer words

pico, PICO (No, I’m not shouting)

What if there’s no intervention?

Sensitivity analysis